Arabidopsis seedlings vs. cm-scale sunflowers and beans), and (iv) movement types .In recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic  and tropic  movements. Nastic movements include circumnutations, a circular movement of plant organs commonly associated with search and exploration, while tropisms refer to the directed growth of plant organs toward or away from environmental stimuli, such as light and gravity. Tracking these movements is therefore fundamental for the study of plant behaviour. Convolutional neural networks, as used for human and animal pose estimation, offer an interesting avenue for plant tracking. Here we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking. We evaluated it on time-lapse videos of cases spanning a variety of parameters, such as: (i) organ types and imaging angles , (ii) lighting conditions (full spectrum vs. IR), (iii) plant morphologies and scales  provided better results than a simpler skeleton with more individuals .In all, these results suggest SLEAP is a robust and versatile tool for high-throughput automated tracking of plants, presenting a new avenue for research focusing on plant dynamics.The online version contains supplementary material available at 10.1186/s13007-022-00851-9. Interest in the study of plant behaviour, first described by Charles Darwin himself , has bee4D [Previous studies have relied on classical image processing techniques to automate this process, such as dense optical flow  based on4D , 14 provIn recent years, the development and use of machine learning algorithms, such as convolutional neural networks (CNNs) have greatly advanced the state-of-the-art in image-based data processing tasks across the fields of behaviour and neuroscience –19. For www.sleap.ai; [Arabidopsis, bean, and sunflower) differing in imaging angles (side-view and top-view), lighting conditions , scales , movement types  and organ types being tracked . Unlike animals, plant morphology changes over the course of a time-lapse video, introducing a problem since SLEAP requires the “skeleton”  to be pre-defined during labelling. Hence, we leverage SLEAP’s high model training efficiency to explore different model configurations and approaches optimized for each dataset, to deal with this challenge and to achieve reliable tracking with few labels.Here we evaluated the suitability of an animal pose tracking framework, Social LEAP Estimates Animal Poses , to theArabidopsis gravitropism (AG), Arabidopsis phototropism (AP), bean twining (BT), sunflower phototropism (SP). In the case of the sunflower shade avoidance experiments, which involve the top view of a plant crown, we adopted two different labelling definitions, where individuals are defined as either single leaves (STL) or as whole plants (STP). Table For this study we analysed the movement of different plants from time-lapse videos which were generated for different experimental protocols Table . Each exArabidopsis thaliana was grown in 12/12 h day-night cycle, on half-strength MS phytagel medium, in the growth chamber at 24 °C for 72–96 h. After germination, seedlings were transferred to another Petri dish under a biological hood. Seedlings were arranged by groups of five and the roots were placed between the lid of the Petri dish and a block of fresh phytagel (same recipe as for cultivation). The Petri dish was then mounted on a rotating plate, the inclination of which is set by an Arduino microcontroller. During the experimental period, the device keeps the Petri dish vertical (seeds growing upward) for 10 h, then tilts it 90 degrees for 48 min, and then tilts it back to its initial orientation for the rest of the experiment for 8 h. White light was provided from the top only, except when taking pictures (light from side). Pictures were taken every 10 min using a Nikon D750. For this study we only used the images taken after the tilting of the plants.Arabidopsis seedlings were germinated as in (a). After germination, seedlings were transferred into small 3D printed cases which left only the hypocotyl exposed to light. As for gravitropism protocol, roots were placed between a wall of the box and a fresh phytagel block. During the experiment, seedlings were left in complete darkness during the experiments and pictures were taken every 10 min using to near infra-red (peak wavelength: 940 nm) flashes. A single transient blue-light stimulation (10 s) was provided using a set of LEDs from the left side.Phaseolus vulgaris) were germinated in soil at 24 °C and a 16/8 h day/night cycle. After germination they were transferred to individual (9 × 9 cm) pots with 20–20-20 N-P-K fertilizer until they reached about 30 cm in height. The plants were then transferred into the experimental setup and were each offered a hanging straw as support for twining. Pictures were taken every 2 min from the side with a Nikon D7500.Beans (Helianthus annus) seeds were kept in a fridge (5 °C) for 12 h before peeling them and soaking them for 24 h in water. They were than germinated in vermiculite within 50 ml tubes and a 12 h day and night cycle at 24 °C. After germination small orange dots (~ 1 mm) were painted with acrylic paint along the stem of the seedlings. The seedlings were then placed in groups of 5 within black-painted tube-racks into the experimental setup, where they were subjected to unilateral blue light  for ~ 22 h. Pictures were taken every 4 min throughout the experiment using a D3400 Nikon camera.Sunflower (After germination of sunflower seeds as in (d), seedlings were transferred into soil within 9 × 9 cm plastic pots. The plants were then grown with a day-night cycle of 16/8 h at ~ 22 °C. We grew three plants in a row, with pots being right next to each other. Pictures were taken every 5 min from ~ 40 cm above the plants, using a webcam (Logitech c270 webcam) connected to a Raspberry Pi .Each video was labelled within the SLEAP v1.1) GUI, see Fig. .1 GUI, swww.sleap.ai), whereby we recorded the time needed to train the networks. Because the bean twining video was large and resulted in out-of-memory errors, we resized it by setting the ‘input scaling’ to 0.5 in the training configurations, before training on this video.SLEAP offers two approaches to train models for identifying the posture of multiple individuals: “top-down” and bottom-up”. In the top-down mode, a network first finds the different “instances”  and then estimates the location of each part . In the bottom-up approach, first all landmarks are identified and only then are they grouped into instances. While an “instance” typically refers to a single organism, SLEAP is agnostic to the definition and can be used to track repeated morphological features, such as leaves. Following preliminary testing, we chose the bottom-up approach to train our models, though which approach makes more sense depends on the model system and biological question. We employ the UNet architecture in SLEAP, configured with an encoder with 5 downsampling blocks of 2 convolutions with ReLU followed by max pooling with a stride of 2; the decoder portion of the network uses a bilinear upsampling layer (stride of 2) with 2 convolutions with ReLU until reaching an output stride of 2; the filters scale by a factor of 1.5 at each block and skip connections connect the encoder and decoder, as previously described . To traihttps://sleap.ai/guides/proofreading.html#tracking-method-details for more details). For each video analysis we exported both the user labels and the predicted labels.For training, SLEAP trains models for a maximum of 200 epochs (which are rarely reached before converging), where an epoch is defined as the total number of batches that are required to perform one iteration over the training dataset, or 200, whichever is smaller, with a batch size of 4, initial learning rate of 1e-4 with the Adam optimizer as previously described [Here we detail three types of analyses we performed in order to assess the performance of SLEAP in the case of plant growth dynamics: training time, prediction accuracy, and comparison to other tracking methods. All statistical tests were calculated to a significance level of 0.05.To examine whether training time was influenced by the total number of labels per frame and the file size of each video, since different video analyses converged after a different number of epochs, we first calculated the mean training time SLEAP needed per epoch. We then calculated a linear mixed model (LMER)  with theTo determine the predictive power of the trained models we calculated the percentage of frames in which the labelled points and the number of individuals were correctly predicted. For this and the following analyses we categorized the labelled points for each video analysis into categories marked as B (representing base points), M (midpoints), T (tips) and TB (branching points). For more detailed definitions according to the observed organ, see Table For each plant part across each video analysis we used Kruskal–Wallis tests  to checkhttps://forgemia.inra.fr/hugo.chauvet-thiry/rootstemextractor) and kindly provided by M. Rivière. Angles in RootStem Extractor are averaged along the top part of the shoot, equal to two mean diameters of the shoot [SLEAP predicts the image coordinates of morphological landmarks but not derived features like shoot angles. To evaluate its performance for downstream analysis tasks, we next compared it to a specialized algorithm designed for plant phenotyping. To do this, we calculated and plotted the angles of the shoot tip for the AG, AP and SP analysis to demonstrate that the information provided by SLEAP can be used to investigate tropisms. Specifically, we calculated shoot angles, as the angle between the highest midpoint along the shoot and the tip branch . We then compared the tip angles of the AP video analysis calculated with SLEAP with those calculated using the RootStem Extractor . Additional tropic responses of plant shoots extracted from AG, AP and SP video analysis can be found in Additional file Figure Arabidopsis seedlings and cm-scale sunflowers and beans), and (iv) movement types . SLEAP successfully identified individuals and labelled points and tracked their movement across all types of the videos tested here. Training time depended both on the number of labelled points per frame as well as the size of the input video. We note that the exact training time will depend on the machine used and will be quicker using a computer with an integrated GPU.In this work we adopted Social LEAP Estimates Animal Poses (SLEAP)—a tool based on convolutional neural networks originally aimed at estimating animal poses—for plant tracking. We tested it on time-lapse videos of a variety of cases including: (i) organ types and imaging angles (e.g. top-view crown leaves and side-view shoots and roots), (ii) lighting conditions , (iii) plant morphologies and scales  which drastically change their shape during growth  and can furthermore occlude each other. We found that tracking the whole plant rather than single leaves proved more effective, by allowing tracking of all instances in all frames with consistently higher accuracy. Tracking single leaves has the benefit of a simple skeleton which remains fixed over time, in contrast to whole plants where new leaves can grow at any moment, requiring predefinition of more nodes within the skeleton than might be present for most of a video. However, though the skeleton was less complex (only 4 points rather than 19), there were many more instances to track. In many cases specific plant parts were not assigned to the correct instance, thus leading to fewer instances identified than were present. For instance, too few instances were found when two or more leaves were partly occluded, and the remaining visible parts were assigned to a single instance. The STP analysis handled occlusions better, even when models were trained on only a few user-labelled frames.The correct prediction of specific plant parts did not depend on the number of labelled training frames for any video analyses, aside from the STP analysis, where there was a small trend that predictions of leaf tips increased with the number of training user-labelled frames. Overall, when plant parts were found, accuracy was high, though the distance between predicted and user labelled points depended on the specific plant part, and for some video analysis also on the number of user labelled frames. Accuracy in our analysis seemed to depend on how well features could be distinguished. For instance, in the AP analysis where images are rather noisy, prediction accuracy was lower for the points along the shoot, where there are no obvious distinguishing features, compared to the branching point, the leaf tips or the base of the plant. Similarly, the root tip in the AG video is very faint, and while the prediction was still very accurate, it was less accurate for this plant part compared to the other parts. This contrasts with the SP analysis where painted dots along otherwise featureless stems of sunflower seedlings served as tracking aid, while for STL and STP, the veins in the leaves offered good natural distinguishing features. For most of our analyses, accuracy increased with the number of labelled training frames, indicating that more is better. However, for some plant parts there was no difference in accuracy based on the number of user labelled frames, suggesting that there is an optimal number of frames after which any additional labelling will not lead to a further increase in accuracy.For optimal position tracking, a trained model would ideally be transferred from one video to another of the same experiment type, thus reducing labelling and training time. However, we found that our models tended to overfit, tracking the specific features of each individual/plant part and thus not allowing to transfer trained models from one video to another. We note that the short labelling and training times still make it feasible to track new sessions in a reasonable amount of time, but future work using more and more diverse labels may improve the ability of models to generalize to new sessions. Occlusions can provide another important challenge when trying to track the position and movement of plant parts. We found that connecting all nodes to the base (centre of the crown) helped deal with possible occlusions of the middle of a leaf. Similarly, such skeletons also help to track the twining tip of the bean plant in the BT video analysis when part of the stem was hidden behind the support , rather than connecting these points linearly along the stem ).Importantly, as the program was not built for plants whose structures change with growth, the skeleton needs to be carefully considered. High image quality with clearly separate individuals allows for high prediction accuracy with only 5 labelled frames per video. When image quality is low, additional markers on the plants can help to improve predictions. Top-view images are most challenging, since leaves change their morphology in 2D, and occlude each other often. Here different skeleton definitions help improve tracking.SLEAP offers a useful tool for fast and high throughput tracking of plant movement in time-lapse videos. We were able to use SLEAP to extract information on a variety of dynamical growth-driven plant processes, including plant tropisms, circumnutations movements and even interactions, such as dynamic leaf. We find that these results encourage the use of SLEAP, or of tools based on convolutional neural networks in general, on plants. Further development of such tools will optimize their success on such occluding structures of changing morphologies and may prove useful in other fields as well.Additional file 1: Video S1. Arabidopsis gravitropism (AG), corresponding to Fig. Additional file 2: Video S2. Arabidopsis phototropism (AP), corresponding to Fig. Additional file 3: Video S3. Bean twining (BT), corresponding to Fig. Additional file 4: Video S4. Sunflower phototropism (SP), corresponding to Fig. Additional file 5: Video S5. Sunflower shade avoidance, tracking individual leaves (STL), corresponding to Fig. Additional file 6: Video S6. Sunflower shade avoidance, tracking individual plants (STP), corresponding to Fig. Additional file 7. Additional figures S1–S5 and tables S1–S9.


Globally, understanding spatial analysis of malnutrition is increasingly recognized. However, our knowledge on spatial clustering of malnutrition after controlling for known risk factors of malnutrition such as wealth status, food insecurity, altitude and maternal characteristics is limited from Ethiopia. Previous studies from southern Ethiopia have shown seasonal patterns of malnutrition, yet they did not evaluate spatial clustering of malnutrition.The aim of this study was to assess whether child stunting and maternal malnutrition were spatially clustered in drought-prone areas after controlling for previously known risk factors of malnutrition.2). We controlled for previously known predictors of child stunting and maternal malnutrition to evaluate the presence of clustering. We did a logistic regression model with declaring data to be time-series using Stata version 15 for further evaluation of the predictors of spatial clustering.We used a community-based cohort study design for a one-year study period. We used SaTScan software to identify high rates of child stunting and maternal malnutrition clustering. The outcome based was the presence or absence of stunting and maternal malnutrition  with a higher risk of stunting and maternal malnutrition than in the underlying at risk populations. Stunted children within an identified spatial cluster were more likely to be from poor households, had younger and illiterate mothers, and often the mothers were farmers and housewives. Children identified within the most likely clusters were 1.6 times more at risk of stunting in the unadjusted analysis. Similarly, mothers within the clusters were 2.4 times more at risk of malnutrition in the unadjusted analysis. However, after adjusting for known risk factors such as wealth status, household food insecurity, altitude, maternal age, maternal education, and maternal occupation with SaTScan analysis, we show that child stunting and maternal malnutrition were not spatially clustered.The observed spatial clustering of child stunting and maternal malnutrition before controlling for known risk factors for child stunting and maternal malnutrition could be due to non-random distribution of risk factors such as poverty and maternal characteristics. Moreover, our results indicated the need for geographically targeted nutritional interventions in a drought-prone area. Child and maternal malnutrition remain a persistent problem, resulting in substantial increases in overall morbidity and mortality in developing countries . Globall5To reduce health disparities, an understanding of the geographical distribution of health problems is increasingly recognized by the global health community. Accordingly, researchers are more often using spatial analysis of malnutrition as it is helpful to understand the complex nature of the geographical distribution of malnutrition 8. KnowleEarlier studies on spatial analysis of malnutrition conducted in Ethiopia showed spatial clustering 10. Howev11The aim of this study was to assess whether child stunting and maternal malnutrition were spatially clustered in drought prone areas after controlling for known risk factors of malnutrition among children and their mothers.This study was conducted in Boricha, which is geographically located at 6º 46’N and 38º 04’E to 7º 01’N and 38º 24’E in southern Ethiopia. Boricha district is a drought prone area, approximately located 34 km south of Hawassa, the capital city of Southern Nations Nationalities of Peoples Region (SNNPR).Sidama is the largest ethnic group in the district and more than 90% of the population lives in rural areas. Protestant Christianity is the main religion in the district and most of people make their living directly from subsistence farming and livestock rearing. Based on the 2007 national census, approximately 315,000 people lived in the district in 2017. The district has 39 rural kebeles , each with an average population of 1,000 to 6,000 people. In 2017, there was one government hospital, five public health centres, 39 health posts in the district. Each kebele has at least one health post staffed by two health extension workers who report to the health centre.As Boricha is the drought prone area, malnutrition is the major health problem. Highest peak of acute malnutrition cases occurs between March and June during Belg rainy season, and the lowest occurrence of acute malnutrition occurs between September and December, following rains in March and June . MoreoveThis study is a part of a community-based cohort study conducted to evaluate child and maternal malnutrition at the community level. During 2017, all households were visited four times. The aim of our study was to measure the patterns and determinants of child stunting and child wasting, determine the risk factors of maternal malnutrition, and evaluate spatial clustering of child and maternal malnutrition.The main outcomes of the study were the spatial clustering of child stunting and maternal malnutrition. The main exposure variables were wealth, altitude, household food insecurity access, maternal education, maternal occupation, and maternal age. The aforementioned covariates were related to child and maternal malnutrition according to their significance level in the previous studies conducted in Boricha district and they are assumed to be non-randomly distributed geographically 12. Thus,We employed two-stage sampling technique to select mother-child pairs. First, nine rural kebeles  were randomly selected from 39 rural kebeles of Boricha district. Secondly, out of the nine rural kebeles, we recruited study subjects from gouts (villages) through cluster sampling technique.This cohort had included 935 children between 6 months to 47 months and 892 biological mothers between 15–49 years old from the total of 897 households at the beginning of the study. Children from 6 months to 47 months were recruited to accommodate age increment due to a one-year follow-up period of time. See earlier publication for detailed description of the study population .Sidamu Afoo). They were trained and conducted pre-test outside of the selected kebeles that had similar socio-economic characteristics. We collected on socio-demographic data such as maternal age, educational status of mothers and occupational status of mothers.We utilized eighteen data collectors and three supervisors for data collection, who were familiar with the local context and fluent speakers of the local language  device .Household food insecurity was assessed by using the Household Food Insecurity Access Scale (HFIAS) tool developed by the FANTA project and validated in different seasons of Ethiopia 1415. Foo14We constructed wealth index using principal component analysis . HousehoThe term malnutrition denotes both to undernutrition and over-nutrition. It is a condition that results from eating a diet in which various nutrients are either an insufficient or excessive. However, this paper focuses on the maternal and child undernutrition aspect such as child stunting and maternal undernutrition based on BMI measurements.Child anthropometric measurements were evaluated based on children’s height and recorded using Emergency Nutrition Assessment for Standardized Monitoring and Assessment of Relief and Transitions software . Children up to 24 months of age were measured in a recumbent position using a length board to the nearest 0.1cm. Children who are able to stand unassisted measured in the standing position to the nearest 0.1cm. We used mother’s recall and memorable events to assess child age. Child stunting was defined as height-for-age Z-score of less than two standard deviation of the World Health Organization Child Growth Standards Median .2 [The presence of maternal malnutrition was defined as a body mass index [BMI] <18.5 kg/m2 . The weiData were double-entered and checked using EpiData v. 3.1 , and transferred to STATA 15  for further cleaning and analysis. Descriptive statistics such as frequency counts, percentages, means and 95% CI were used to summarize the data. For the anthropometric measurements we deleted extreme values of child stunting (height-for-age Z-scores greater than six or less than minus six) which represented 4.9% of the measurements. Similarly, we deleted cases with improbable heights (about 1%).2) rates using a discrete scan statistics of Poisson probability model  <18.5 kg/mdistrict 12.Although identifying the presence of clustering after controlling for known risk factors is the primary objective, we also identified the effects of the risk factors for the observed clustering . Hence, We had secured ethical approval from Institutional Review Board (IRB) at Hawassa University  and the Regional Committee for Medical and Health Research Ethics, Western Norway (ref: 2016/1631/REK Vest) before we had started data collection. All study participants were informed about the benefit and harm of the study, and then informed written consent was obtained from all study participants. Those who could not sign on used their thumb print.The authors thank the study subjects for their willingness to participate, and the data collectors, supervisors, and Boricha district authorities for their positive input.Table 1 describes the characteristics of the study population by the nutritional status.A total of 935 children residing in 897 households were visited for the four-consecutive time for the study. About 505 (54%) were boys and 427 (46%) were females. Most of our respondents  were biological mothers of children. The mean age of respondents was 28 years. About 50% (445 of 887) of mothers were illiterate. Occupationally, 770 of 889 (87%) of mothers were housewives and farmers. The average family size of the study households was five persons. See Table 1 describes the characteristics of the study population by the nutritional status.We found that the prevalence of stunting and maternal malnutrition varied among the nine kebeles. The highest prevalence of stunting (62%) and was recorded in Fulasa-Aldaada kebele. The highest prevalence of maternal malnutrition (52%) recorded in Sadamo-Dikicha kebele. The prevalence of stunting increased as the age of the child increased. See The crude analysis of SaTScan showed that, there were areas with higher risk of stunting than in the underlying at-risk populations. The most likely and secondary significant spatial clusters for stunting were identified in the north and north-eastern part of the study area.Table 2 for more information.Children identified within the most likely clusters were 1.6 times more at risk of stunting than in the underlying at-risk populations in the unadjusted analysis. However, covariate adjusted SaTScan analysis showed that clustering disappeared. See Table 3 for more information.The crude analysis of SaTScan showed that, there were areas with higher risk of maternal malnutrition than in the underlying at-risk populations. Mothers within the most likely clusters were 2.4 times more at risk of malnutrition in the crude analysis. However, covariate adjusted SaTScan analysis did not show spatial variation of maternal malnutrition. See Table 4).In this analysis we aimed to provide further insights into risk factors for the clustering of stunting and to evaluate the effects of risk factors for the observed crude spatial clustering. Accordingly, we found significant differences with regard to factors such as poverty, maternal age, maternal education, and maternal occupation between stunting cases found within a crude spatial cluster and cases outside the cluster  which could increase the spatial resolution of cluster detection . MoreoveWe had less than 10% of missed measurements (height-for-age Z-score and body mass index) due to loss to follow-up and outliers, which indicated that the impact of loss to follow and outliers was low. Moreover, our study population is a representative sample of the population, and the non-response bias was low. We performed a cohort study design, and thus our study can establish temporal associations. Moreover, our study had consistency with the previous studies regarding to local clustering of child stunting [The non-random distribution of risk factors not evaluated in our study could influence the spatial clustering of child and maternal malnutrition. However, we included the most common risk factors of child and maternal malnutrition. We opted to use a circular window in the spatial scan statistics rather than other windows to identify the significant clusters; however, the true clusters may be elliptic or rectangular. Scan statistics using circular windows cannot detect these shapes, unless all possible angles are considered, which is difficult to compute .Nutritional epidemiology has been criticized for its inaccurate survey measurements of food consumption and reliance on observational associations 4445. Thu44In this study, measurement errors could be originated from the anthropometric recordings, recall bias, and social desirability bias. However, to minimize such biases, we trained data collectors and supervisors, conducted pre-tests, evaluated technical error measurements, and calibrated instruments. Moreover, we used memorable events to aid the mother’s recall of their children’s age and we also used short recall periods for variables related to household food consumption. Missed information regarding government support such as the safety-net programme could influence the findings of our study.In conclusion, child stunting and maternal malnutrition were not spatially clustered after we controlled for known risk factors for child stunting and maternal malnutrition, and these risk factors are probably not randomly distributed. The results of this study would be helpful for geographically targeting nutritional interventions and to consider type of nutrition related interventions such as educational, agricultural or health care services in a drought prone area.


Postoperative digital subtraction angiography (DSA) is the gold standard for establishing a cure of an arteriovenous malformation (AVM) after treatment. The incidence of residual AVM identified on postoperative DSA ranges from 1.8 to 11%. Although this is important for finalizing the treatment of AVMs, postoperative DSA rarely shows new findings that were not previously identified on preoperative imaging. We present a unique case where we identified residual AVM nidus on immediate postoperative DSA that drained into two deep veins that were not evident on preoperative DSA and increased the AVM grade from Spetzler-Martin grade II to III. To our knowledge, this finding has not been previously reported in the literature. We resected the residual AVM nidus identified on postoperative DSA, leading to an angiographic cure. The patient demonstrated a postoperative right-sided supplementary motor area syndrome that resolved over time with therapy. She made a complete functional recovery by her one-month follow-up appointment. Postoperative digital subtraction angiography (DSA) is considered the gold standard for establishing a cure of a cerebral arteriovenous malformation (AVM) ,2. PostoWe present a unique case where we identified residual AVM on DSA after resection. The residual nidus drained into two deep veins that were not evident on preoperative DSA, increasing the AVM grade from S-M grade II to III. To our knowledge, this finding has not been previously reported in the literature.We illustrate a case of a 21-year-old, previously healthy, right-handed female who presented after a sudden onset of headache and vomiting. Her Glasgow Coma Score was 15, and there were no focal deficits on neurological examination. Computed tomography of the head (CTH) demonstrated intraventricular hemorrhage that appeared to arise from the anterior horn of the right lateral ventricle Figure . Her intFurther imaging with DSA demonstrated that the right frontal AVM nidus measured 36.8 × 25.4 mm and was supplied predominantly by the right pericallosal and callosomarginal arteries Figures , 2B. TheThe patient remained stable preoperatively, and after discussing multiple therapeutic approaches, she opted for surgical resection. She underwent a right frontal craniotomy with an interhemispheric approach for surgical resection. After resection, which was thought to be a surgical cure, she remained under general anesthesia and underwent an immediate cerebral DSA. The DSA revealed residual nidus with two sources of deep venous drainage: one short draining vein entered the inferior sagittal sinus and another entered the right basal vein of Rosenthal Figures , 3B. ThuPostoperative CTH demonstrated expected postoperative changes that were stable on repeat imaging Figure . On postCerebrovascular AVMs have an incidence rate of 1.42 cases per 100,000 person-years . The preSurgical treatment of intracranial AVMs remains challenging, and judicious selection of microsurgical candidates remains a central challenge in predicting outcomes. The S-M grading scale considers the size, venous drainage, and eloquence of the involved brain region to give a numerical value between 1 and 5; it is the most commonly used grading system to assess surgical risk ,11. ThesFurther studies have shown that outcomes and suggested treatment for grades I-II are similar, with resection recommended because of hemorrhage risk. For grade III AVMs, although there is a lack of randomized controlled trial data to guide management , there hIn this case, we presented a patient who underwent microsurgical resection for what was initially believed to be a grade II AVM. This treatment modality was appropriate based on prior literature. This unique case demonstrates that the AVM was more accurately graded as an S-M grade III, although this information did not come to light until her first intraoperative DSA. We postulate that the deep draining veins were not seen on preoperative DSA because the superficial draining vein was large and drained most of the flow from the nidus, leaving the deeper veins with insufficient flow to be seen on DSA until after the resection with the subsequent sacrifice of the superficial draining vein and redirection of draining blood flow. Despite the presence of the deeper draining veins that required additional resection, this patient experienced an excellent outcome and complete treatment of her AVM.Postresection DSA is the gold standard for demonstrating a surgical cure for AVM. This case demonstrates the importance of considering immediate postoperative DSA under the same general anesthesia session and demonstrates deep venous drainage first seen on the immediate postoperative DSA. To our knowledge, this is the first documented case of AVM residual with new deep draining veins not visualized on preoperative DSA. Most notably, this upgraded the AVM from S-M grade II to III. Ultimately, the patient underwent surgical resection without long-standing neurological deficits and has experienced excellent recovery.


This constitutes the first readily accessible and searchable resource for researchers from all disciplines with an interest in documenting and investigating the musical dynamics underlying the pandemic.When a sweeping COVID-19 pandemic forced cultural venues, schools, and social hangouts into hibernation in early 2020, music life relocated to the digital world. On social media platforms like YouTube, Facebook, Twitter, and TikTok, sofas and balconies took center stage for musical performances presented as live-streamed concerts and recorded videos. Amateurs and professional musicians alike embraced digital formats and innovated novel genres of corona-themed music. Adapting the well-known “musicking” term from cultural musicology Small, , we willBecause establishing a typology of coronamusic lies beyond the scope of a data report, we will restrict ourselves to mentioning examples here. Across the globe, topical tunes were composed, and humorous COVID-19 lyrics were invented for well-known songs, resulting in thought-provoking contrafacta some of which were designed to process an unfamiliar everyday life in safe sonic environments. Reminiscent of previous outbreaks such as “The Plague of Saint Charles” hitting Milan in 1576 Chiu, , daily rAn important question concerns when and how much people engaged with music during lockdown. Music listening through audio-based streaming platforms  decreased considerably, most likely due to changes in music-accompanied behaviors like public commuting and gym exercise  lockdown were negatively associated with anxiety and depression, and this effect was mediated by individual reward sensitivity. Other reports indicate that nostalgic music listening—which is known to induce positive emotions toward one's own autobiographical memories in general . The video-based subset was subsequently extended with retrospectively sampled YouTube videos to ensure a substantial daily representation for the first two lockdown months. While audio-only content  was permitted, we focused on videos because music consumption partly migrated from audio- to video-based platforms during COVID-19 lockdown  to facilitate content collection.To estimate the reach of recruitment efforts, statistics were sourced from Twitter. Specifically, tweets from NH personal account included an initial announcement of the Google Sheet with a clarifying follow-up tweet on 26 March, an announcement of the SurveyXact questionnaire with two follow-up tweets on 3 April, a follow-up announcement in Danish on 20 April, and a closing notice on 23 July. A total of 30,242 impressions , 1,033 engagements , 118 retweets, and 113 likes were obtained. Further unquantified activity is expected from retweets and shares on other platforms . Additionally, all contributors were encouraged to share survey links within their own network, and the database was advertised to research colleagues at virtual launching events for the MUSICOVID network with >250 global attendance on 19 May. Finally, the overall research project was promoted with survey links on the researchers' university webpages and by national news media in Denmark, Norway, Finland, Sweden, and The Netherlands.Crowdsourced videos and media reports had the highest representation during March-April 2020 . This cohttps://osf.io/y7z28/). We decided to reproduce the cultural biases of the original dataset by focusing on countries represented with ≥15 crowdsourced videos ,Retrospective sampling employed the “googlesearch” library in Python (script available on OSF: site:youtube.com before:2020-02-28 after:2020-02-27 (music OR musica OR musik OR musique OR muziek OR song OR tune OR lied OR liedje OR chanson OR canzone OR sang) AND (covid OR covid19 OR “covid-19” OR coronavirus OR corona OR “sars-cov-2” OR #quarantunes OR #covidance OR #coronasongs OR #coronamusic OR #TogetherAtHome OR #StayHome OR #Pandemix OR #COVered19 OR #gratitunes)Thus, a music-related term was required in combination with a coronavirus-themed term or one of the nine most common crowdsourced hashtags. In addition to the target-country languages, Dutch, German, and French terms were included as these languages were represented with ≥10 crowdsourced videos from Netherlands (14), Germany (15), France (13), and Canada (11) . The PytTo produce a single prioritized list of candidate URLs for each date, rows were selected from the top of the generated output using a randomly drawn vector of country labels weighted according to the crowdsourced country distribution while avoiding duplicate URLs.ad-hoc categories from Google Sheet/SurveyXact were subsequently compared to check for inconsistencies, add and merge categories, and refine definitions when necessary.For characterizing and classifying videos, a coding scheme was developed. Specifically, 15 videos were first randomly selected for exploratory content analysis by all authors independently. Codes based on The final coding scheme agreed upon by all team members comprised:ID: Arbitrarily assigned unique indices.Category: Sub-corpus .Source: Sampling origin URLs: URL(s) locating the item.Title: Title sourced online or submitted by contributor.PublicationDate: Date stamp for publication.SoMePlatform: Social media platform .JointMusicking*: Whether >1 person is visibly performing together.OrigCovidSong*: Whether the performer(s) have composed the music themselves during the coronavirus crisis (regardless whether the lyrics were unambiguously COVID-related).OrigCovidLyrics*: Whether lyrics make reference to the coronavirus situation.Movement*: Whether movement is featured and/or encouraged in the video, including dancing, yoga, and prominent moving around while performing, to an extent beyond what is minimally required for successful musical performance/attendance.HealthInfo*: Whether instructions like “stay home,” “wash hands,” “wear a mask” occur.Conflict*: Whether content  refers to conflict–e.g., between nations, fellow citizens, or cohabitants.Country: Performers' nationality and/or residence country.LyricsLanguage: Language(s) of lyrics.Setting**: Setting(s) of music making/visual content .Genre**: Musical genre(s) represented, using at least one of the 23 categories from Revised Short Test of Music Preferences . Consensus was reached during coding scheme development and was informed by the authors' continual watching of coronamusic videos during the pandemic.Features**: All-inclusive category for any other characteristic features .factual variables (no asterisk), (b) binary variables (single asterisk), and (c) open-coded variables (double asterisks). Whereas binary variables were dichotomous, open-coded variables used inclusive coding allowing for multiple values and NAs without enforcing mutual exclusivity . Two further variables indicated date (RetroSampleDate) and geolocation (RetroSampleCountry) for retrospective sampling. “Features” labels were developed and refined during group meetings throughout the coding process as more videos were viewed (and re-coded when necessary).There were three variable types: (a) in plenum. This systematic procedure ensured that the vast majority of factual and binary codings achieved 100% inter-rater agreement between two coders whereas the remaining more ambiguous codings always had support from at least three authors.All video URLs were randomly assigned a first and second coder from amongst the authors (see FirstCoder and SecondCoder columns). After all coders had independently coded their assigned videos, conflicts for binary variables were resolved during bilateral virtual meetings whereas open-coded variables were combined across coders. A small subset of unresolved conflicts and principled issues were determined Final editorial proofreading and streamlining was performed to optimize discoverability; typos were amended, spellings were unified, text was converted to lowercase, nouns were singularized, non-essential URL parameters were removed, and similar categories were merged . Genre designations constituting parts of STOMP-R categories were re-assigned to these . The project group was consulted when doubt arose.During coding, duplicates, dysfunctional links, and ineligible submissions were removed, and some videos were re-classified as media or other . Video compilations curated by news media sources  were only re-classified if a reporter was featured visually or via voice-over. Coronamusic-themed links not including videos or news reports were designated as other. Because codings could differ between items with identical audio but distinct video, those were kept.A coding scheme was developed for news media content using a procedure similar to that described in Section Videos. SoMePlatform, JointMusicking, OrigCovidSong, OrigCovidLyrics, LyricsLanguage, and Genre were excluded due to limited relevance. Country was redefined as “countries of media platform and journalist(s) reporting.” All other video variables were included. Five media-specific variables were added:MediaPlatform: Name of news media source.MediaFormat: Format(s) used .MediaLanguage: Language of writing or narrator speech.EmbeddedExternalVideo*: Whether external video content  is embedded.MusicMakerProfessionalism**: Professional status of music makers described/depicted (amateur and/or prof).*), and open-ended (**) variables and were subject to dual coding with conflict resolution and editorial proofreading as described above. No retrospective sampling was performed.Media items comprised factual, binary  , 254 media items , and 62 other items , published between 8 February and 27 July 2020. All sub-corpora are accessible as separate Excel/CSV files at tabase/) . Figure Sharing music enables people to connect with others : https://github.com/dana-and-monsters/corona-music-database; ShinyApp: https://dana-and-monsters.shinyapps.io/corona-music-database/.The datasets presented in this study can be found in online repositories. The names of the repositories and accession number(s) can be found below: OSF (version number 20210519): NH conceived of the project, launched crowdsourcing, obtained permissions for data handling, produced the figures, and wrote the initial draft of the manuscript. JT performed a very large amount of the database coding and resolution of coding conflicts. DS created the ShinyApp and contributed to figures. JB created the Python script for retrospective sampling. All authors contributed substantially to research design, coding scheme, content coding, data curation, revision of the initial draft, and approval of the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Stereo video has been widely applied in various video systems in recent years. Therefore, objective stereo video quality metric (SVQM) is highly necessary for improving the watching experience. However, due to the high dimensional data in stereo video, existing metrics have some defects in accuracy and robustness. Based on the characteristics of stereo video, this paper considers the coexistence and interaction of multi-dimensional information in stereo video and proposes an SVQM based on multi-dimensional analysis (MDA-SVQM). Specifically, a temporal-view joint decomposition (TVJD) model is established by analyzing and comparing correlation in different dimensions and adaptively decomposes stereo group of frames (sGoF) into different subbands. Then, according to the generation mechanism and physical meaning of each subband, histogram-based and LOID-based features are extracted for high and low frequency subband, respectively, and sGoF quality is obtained by regression. Finally, the weight of each sGoF is calculated by spatial-temporal energy weighting (STEW) model, and final stereo video quality is obtained by weighted summation of all sGoF qualities. Experiments on two stereo video databases demonstrate that TVJD and STEW adopted in MDA-SVQM are convincible, and the overall performance of MDA-SVQM is better than several existing SVQMs. Stereo video can provide a more immersive watching experience for viewers, so stereo video systems have been widely used in various fields ,2. The pConsidering the dependence of stereo video quality metric (SVQM) on reference video, objective SVQM can be divided into three categories, that is full-reference (FR), reduced-reference (RR) and no-reference (NR) . As the Benefiting from the rapid development of traditional image and video quality metric, objective quality metrics for stereo video are gradually proposed. In the early stage of the research, the design of SVQM is mainly realized by referring to image quality metric (IQM) and 2D video quality metric (VQM). For example, the well-performed IQM and VQM ,8,9, sucIn stereo video, the information of each dimension interweaves. Separately processing information in different dimensions cannot reflect the characteristics of stereo video precisely. In this work, to deeply explore the characteristics of stereo video, we attempt to implement multi-dimensional joint analysis, and propose an SVQM based on multi-dimensional analysis (MDA-SVQM). Specifically, firstly, by comparing the correlation in each dimension, the optimal decomposition direction is selected to construct the temporal-view joint decomposition (TVJD) model for stereo group of frames (sGoF). Then, based on decomposition, the feature extraction of TVJD subband information is completed and the quality of sGoF is obtained by regression. Finally, the quality weight of each sGoF is obtained by calculating the spatial-temporal joint energy using spatial temporal energy weighting (STEW) function, and the final stereo video quality can be acquired by weighted summation of sGoF qualities. Experiments on two stereo video databases demonstrated that the MDA-SVQM proposed in this work can accurately evaluate the quality of stereo video.(1)To solve the problem that stereo video contains both inter-view and temporal information, which leads to the difficulty in quality evaluation, this work designs a TVJD model based on correlation analysis, which can describe the characteristics of stereo video more accurately;(2)To solve the problem that TVJD subbands are substantial and complex, this work classifies the subbands by their own characteristics and generation mechanism. On this basis, the features of different subbands are extracted separately, which makes the features more sensitive to distortions;(3)To solve the problem that it is difficult to model the influence of temporal fluctuation on stereo video quality, in this work, STEW function is designed by simulating the stimulation of spatial-temporal alteration on the visual system.To sum up, the innovation of this work mainly lies in:The rest of this paper is organized as follows. In this section, we will make a brief review of SVQM and related fields.As the terminal receiver of visual information, viewer’s subjective rating is obviously of better accuracy. Because of this, subjective quality evaluation plays an important role in exploring the characteristics of stereo video. In order to ensure the standardization and reasonability of subjective evaluation, video quality expert group (VQEG) in International Telecommunication Union (ITU) has released a series of standards for subjective evaluation implementation, such as ITU-R BT.500, ITU-R P.910 and ITU-R BT.1788. In these standards, subjective evaluation methods such as ACR-HR, DSCQS and SAMVIQ and watching environment requirements are recommended in detail. In the early stage of research, researchers mainly analyzed the impact of different acquisition and display devices on stereo visual quality. In these researches, Saad et al.  studied Based on the databases established by subjective evaluation, the objective metrics for stereo video haves been proposed gradually. As mentioned in Introduction, VQM is the foundation of designing SVQM. Starting from simply combining the spatial quality of each frame in a video, state-of-the-art VQMs employ deep learning and other signal processing methods , which sTo improve the readability, the summary of some important notations and abbreviations in this paper are given in Essentially, stereo video can be characterized as high-dimensional . To be specific, one dimension along view direction, one dimension along temporal direction, and two dimensions in spatial domain. Obviously, different dimensional information will cause different visual perception in HVS. Meanwhile, different frequency components will also percept differently. As a result, each frequency component decomposed from different dimensions should be processed separately to complete the precise description of stereo video. Based on above analysis, considering the computational complexity, in this subsection, temporal and inter-view lifting-based wavelet transform has been jointly implemented as sGoF decomposition.In standard lifting-based wavelet transform, the pixels in the same location of two adjacent frames are decomposed into high and low frequency components. However, when viewing a video, the viewer will track the moving objects. Hence, to make the decomposition more consistent with visual perception, compensation is integrated, so as to realize the object-based frequency decomposition.lI1 and lI2 denote two adjacent frames in a video, and lM1→l2 be the mapping for two frames. Then, the object-based decomposition can be implemented to decompose lI1 and lI2 into high frequency subband lHl21, and low frequency subband lLl21,, which can be expressed asx, y) represents the pixel location, and mH and mL are the high and low frequency component decomposed from lI1 and lI2.Let From the perspective of construction, stereo video can be regarded as the ordered arrangement of frames in both temporal and inter-view directions. Resultingly, the frequency decomposition of stereo video should also be implemented in two directions, which are defined as temporal filter (TF) and view filter (VF). Meanwhile, as Equation (1) illustrated, the key problem in decomposition is how to establish the mapping relationship between adjacent frames. Considering the characteristics of stereo video, motion vector can be used as the mapping in temporal direction, and disparity vector can be adopted for inter-view direction. Hence, the compensation can be optimized by motion compensation (MC) and disparity compensation (DC). To sum up, MCTF and DCVF can be used to decompose stereo video in temporal and inter-view directions.V represent inter-view decomposition and T represent temporal decomposition. For an sGoF with 16 frames, the traditional structure can be expressed as 4T + V. However, due to the variety of scene depth and motion intensity, the conclusion that inter-view correlation is stronger is not always true. Meanwhile, in multi-level decomposition, the correlation changes after each decomposition. That is, the decomposition in one direction can reduce the correlation in that direction to 1/2, without affecting the correlation of the other directions. Therefore, to decompose sGoF more efficiently, it is highly necessary to design an adaptive interleaving decomposition structure for temporal and inter-view decomposition. Aiming at this, a correlation analysis-based TVJD model is designed in this work.Obviously, a stereo video contains two views, so only one inter-view decomposition is needed. As for temporal decomposition, the number is determined by the temporal length of the sGoF . Hence, for efficiency and accuracy, it is necessary to determine the decomposition order between two directions reasonably. Generally speaking, in multi-view video, temporal correlation is usually stronger than inter-view correlation. Therefore, a conventional decomposition structure is to implement DCVF after multi-level MCTF. Specifically, let mI and mI+1, the amplitudes of high frequency subband coefficients are close to 0. That is to say, the smaller the amplitude of high frequency subband coefficients, the stronger the correlation between two frames to be decomposed. Furthermore, because the decomposition scheme adopted in this work integrated motion and disparity compensation, the amplitude of motion vector and disparity vector can also be utilized to reflect the correlation in temporal and inter-view directions. According to the above two conclusions, a decomposition cost function can be constructed from two aspects to determine the correlation of each direction, that is the amplitude of high frequency subband and motion or disparity vectors, which are denoted by H and Vd, respectively. Let costD represent the decomposition cost, the calculation can be expressed asd denotes the decomposition direction, that is T or V.  denotes the pixel location and m and n is the spatial height and width of the stereo video.In detail implementation, the key problem is how to determine the optimal direction in each time of decomposition. According to the calculation of object-based decomposition given in Equation (1), it can be deduced that, for ideal mapping of two adjacent frames optimalS denote the optimal direction, the selection can be expressed asThen, the decomposition costs of both directions can be calculated before each level of decomposition, respectively, to adaptively select the optimal direction. Let It should be pointed out that only two views are included in stereo video. Consequently, inter-view decomposition only needs to be implemented once. Therefore, for low-computational complexity in TVJD, once the inter-view decomposition is selected as the optimal, the decomposition cost will not be calculated in the subsequent decomposition, and the temporal decomposition will be selected directly.To conclude, by the calculation and comparison of decomposition costs in different directions, TVJD constructs the adaptive interleaving decomposition structure for sGoF, thus improving the decomposition efficiency and describing the multi-dimensional characteristics of stereo video more accurately.Referring to scalable image processing, in TVJD, it is only necessary to further decompose the low frequency subband. Accordingly, in MDA-SVQM, a 16-frame sGoF can be decomposed into six subbands by TVJD. After that, further exploration for subband characteristics is needed, so that feature extraction can be more consistent with visual perception.As a basis, several stereo videos with different scenes are selected for analyzing. Because TVJD adopts joint decomposition in temporal and inter-view directions, we employ temporal index (TI) and disparity index (DI) to represent the characteristics of the test video. To be specific, the larger the TI is, the greater the temporal difference is, that is, the motion in the scene is more intensity. Meanwhile, the larger the DI is, the greater the difference in the inter-view direction is, that is, the stereo of the scene is more.In In sGoF, the high frequency subbands generated by TVJD, that is MFJI and MRJI, usually represent the detail information of moving objects. Meanwhile, for natural scene image, the distribution of high frequency components usually follows a pattern of high peak and heavy tail. Based on the above analysis, by analyzing the relationship between distribution and distortion, features of the MFJI and MRJI can be extracted.MFK (m) and MRK (m) denote the kurtosis feature of MFJI and MRJI in m-th sGoF. Detail calculation can be expressed asMFφ(m) and MFφ(m) are coefficients of MFJI and MRJI, respectively, MFE and MRE denote the information entropy of subbands MFJI and MRJI. Detail calculation can be expressed asp[MFφ(i)] is the probability of coefficients MFφ(i).On the other hand, the compression can also reduce the richness of texture information in video, which leads to the amplitude diversity decrease in motion information coefficients. In histogram, it can be reflected by the reduction in the tail. Meanwhile, the greater the amplitude diversity of the subband coefficients, the greater the overall information entropy. Therefore, in MDA-SVQM, information entropy is applied to represent the richness of motion information subbands. Let VM denotes the distortion feature vector of motion information subband, the construction can be expressed asTo sum up, by exploring the relationship between coefficient distribution and distortion degree, the kurtosis and entropy of each motion information subband are extracted as the distortion features. Then, the distortion feature vectors of motion information subband of are constructed. Let Different from high frequency subband, low frequency subband mainly represents structure information in stereo video scene. To further prove the high correlation between low frequency subband and video scene, In order to more accurately measure the distortion, feature extraction should be consistent with the visual perception for low frequency information. As the key of visual understanding, LOID features can joint-capture the position and direction information of the object in the scene, and have the advantages of rotation invariance. Therefore, the low dimensional representation of low frequency subband based on LOID features is obviously a method that conforms to HVS perception for low frequency information.steermax operations to obtain the LOID feature. Let if denote the i-th frame in low frequency subband, the feature map of if can be acquired through SWM layers, which is denoted as it. For detail implementation, if is first convolved with CHWs, ϕn) representation, and processed by the first level of non-linear steermax operation, which can be expressed ass denotes the wavelet scale, and F is defined as local geometry.In this work, a steerable wavelet machine (SWM)  is adoptψc) is still a low-dimensional representation of low frequency subband. To precisely reflect the influence of distortion on stereo video, it is necessary to further explore the characteristics of LOID. From the above implementation of LOID feature extraction, it can be found that MF-based LOID performs a lot of gradient-based calculation. Therefore, low frequency subband representation can thus be considered as gradient-based. Since the gradient contrast plays an important role for image recognition in HVS, when the distortion occurred, it will inevitably affect the gradient contrast. Meanwhile, the image gradient histogram follows the Weibull distribution, which also has a high correlation with the visual perception. To sum up, in this part, Weibull distribution-based statistical feature is extracted for low frequency subband.η and β denote the scale and shape parameters respectively, and LOIDGM is the gradient amplitude of the LOID feature map. Let xp and yp denote the Prewitt filter operators in the directions of horizon and vertical, the calculation of LOIDGM can be expressed asPrevious studies on visual physiology have pointed out that the shape and scale parameters in Weibull distribution’s probability density function (PDF) can accurately describe the spatial consistency and complexity of images . Hence, η represents the width of the distribution and can reflect the local contrast. The change of local contrast will affect the perception of human eyes on quality. At the same time, the shape parameter β refers to the kurtosis of the distribution, which is sensitive to the local edge. In order to further explain the relationship between the parameters and distortion, we encoded the same image with JPEG and JP2K in different compression degrees, and extracted the corresponding parameters of each compressed image. Specifically, the scale parameter Vs represent the distortion feature vector, the construction of the vector can be expressed asηSF, SRη, SFβ and βSR are the scale and shape parameters of each low frequency subband, respectively.Based on the above conclusion, for the low frequency subband, firstly, the LOID feature map reflecting its essential information is extracted by SWM. Then, considering the perceptual characteristics of Weibull distribution, the PDF parameters of the LOID feature map are taken as distortion features. Let sGoFP . In SVR training, a radial basis function is adopted. Specifically, we used a publicly available LIBSVM package for SVR implementation. Then, sGoFP  can be used to predict the quality of sGoF sGq. The calculation can be expressed asBy obtaining the distortion features of each TVJD subband, the final feature vectors can be constructed, which is denoted by It should be noted that a traditional subjective quality database only provides the overall subjective quality of each distorted stereo video, but not the subjective quality of each sGoF. Therefore, to obtain the sGoF subjective quality, we build a new stereo video subjective quality database, and consider the overall quality of each stereo video as the sGoF’s subjective quality. Meanwhile, we make two assumptions to guarantee the rationality of the above scheme. Firstly, because of its short temporal duration, the sGoF with small length can be considered as stable in temporal quality. Secondly, because the quality of each sGoF in our database changes periodically, and the period is set as the same length of sGoF in this work. To conclude, it can be considered that the overall subjective quality can be used to represent the sGoF’s subjective quality.In Early studies on binocular visual psychology pointed out that for simple ideal stimuli, with the increase in contrast on a certain view, the dominance of the view will gradually increase . For wati, j) is calculated by Gaussian weight function, which can be expressed asd represents the direction, and it can be expressed as the temporal energy and spatial energy, which are denoted by Te and Se, respectively. μ is local average value, μ = ij x∑ ωi, j) of frame n can be calculated asBy obtaining the temporal and spatial local energy of each pixel, the temporal and spatial weight of a pixel in the location of  Core (TM) i7-3770 CPU @3.40 GHz, 8G RAM, Windows 7 64-bit is used for the verification of metrics in two databases.In this section, we will first introduce the stereo video database and performance indicators used in this work for performance evaluation. Then, the performance of each module in MDA-SVQM is verified to prove the effectiveness of the proposed framework. Finally, the overall performance of MDA-SVQM will be further illustrated by the performance comparison with other existing SVQMs. It should be noted that the quality metrics are realized in MATLABIn our work, a stereo video database is set up by subjective experiment, which is named as NBU-3DV database. Here, the NBU-3DV database is utilized to train for sGoF quality prediction model. Then, the validation experiment and performance comparison are carried out in the internationally acknowledged NAMA3D-COSPAD1 database (abbreviated as NAMA3D database) . At the The NAMA3D database contains 10 undistorted stereo videos with a resolution of 1080 × 1920 at 25 fps as reference. Meanwhile, the database also contains 100 symmetric distorted stereo videos generated from reference videos. Specifically, five types of distortion are considered in the NAMA3D database, including H.264 compression, JP2K compression, reduction in resolution, image sharpening and down-sampling with sharpening. The mean opinion score (MOS) with a range of 1 to 5 is used to represent the subjective quality of distorted video. The higher the MOS value is, the better the quality of distorted video is.As previously mentioned, in this work, an sGoF quality prediction model should be constructed by SVR. Generally speaking, it is not reliable to share the same dataset for training and prediction in regression. Based on the above considerations, we establish an NBU-3DV database by subjective experiment. In this section, we will briefly introduce the subjective experiment and NBU-3DV database construction.In construction of the database, firstly, we select six pairs of undistorted stereo video as reference videos, which are all publicly available standard stereo video for encoding test. The reference videos have HD or full HD resolution with a YUV 420 format in .avi containers of a 10-s time duration. In As for distorted video generation, we use HEVC ,29 to enThree commonly used measures are applied to quantitatively evaluate the performance of the quality metric, that is Pearson linear correlation coefficient (PLCC), Spearman rank correlation coefficient (SROCC) and Root mean squared error (RMSE). The illustration and detail calculation of the performance indicators are as follows.(a)PLCCn represents the total number of distorted stereo video. ioq and isq denote the objective and subjective quality of i-th distorted video, and PLCC reflects the linearity between subjective quality and quality evaluated by an objective quality metric. The value range of PLCC is −1 to 1. The closer the absolute value of PLCC is to 1, the better the evaluation performance of the objective metric. Otherwise, the worse. The calculation of PLCC can be expressed as(b)SROCCiRoq and iSoq denote the rank of i-th objective and subjective quality in all objective and subjective qualities, respectively.SROCC is employed to measure the monocity between subjective quality and objective quality. The value range of SROCC is . When subjective and objective qualities are strictly monotonic, the absolute value of SROCC is 1. The SROCC can be calculated as(c)RMSERMSE quantitatively reflect the numerical distance between subjective and objective quality. RMSE values from 0 to infinity. Obviously, the smaller the RMSE, the better the performance of objective metric. Detail calculation can be expressed asIn summary, for a perfect objective quality metric, PLCC and SROCC should be close to 1, and RMSE should approximate to 0.MDA-SVQM mainly consists of four modules, including stereo video decomposition, feature extraction, GoF quality prediction and stereo video quality pooling. Among them, the innovation of this work mainly lies in TVJD, as well as the new STEW weighting function. Therefore, the effectiveness of TVJD and STEW is verified in this subsection.In MDA-SVQM, TVJD is used for sGoF decomposition. To achieve the purpose of efficient decomposition, TVJD adaptively selects the optimal decomposition direction according to the scene characteristics. To prove the scene adaptability of TVJD, we choose stereo video Boxer from NAMA3D database for testing in this part. T + V is selected by TVJD, that is, the inter-view decomposition is implemented after all temporal decomposition. However, from sGoF 14, a second person appears, and the moving intensity has been greatly increased compared with that before. Therefore, from this sGoF, the temporal correlation gradually decreases, and the intra-view correlation gradually exceeds the temporal correlation. As for the optimal decomposition structure, the order of inter-view decomposition gradually advanced, and the structure of 2T + V + 2T or 3T + V + T are chosen. Through the above experimental results and analysis, it can be concluded that TVJD can adaptively construct the optimal decomposition structure according to correlation in two directions and lay a good foundation for feature extraction and quality evaluation.As previous mentioned, temporal correlation is stronger than intra-view correlation for most stereo video. Hence, it can be seen from In MDA-SVQM, STEW is employed to weight the sGoF quality and yield the final stereo video quality. Obviously, since the inter-view-temporal information in stereo video has been processed in TVJD, STEW only considers the spatial-temporal information in sGoF weighting. Based on the above premise, in this part, we adopt LIVE video quality database for validation of STEW. To reflect the performance of STEW in temporal weighting more accurately, we design several different evaluation schemes for comparison. In detail implementation, the SSIM is applied to calculate the image quality of each frame in a video. Then, different weighting strategies are adopted for temporal quality pooling. To be specific, temporal average pooling, temporal asymmetric pooling  and temp(1)Randomly select the distorted stereo videos in NAMA3D database to construct the test dataset, that is, 80% of the distorted video is used as the training data, and the remaining 20% is the training dataset, and no overlap between two sets;(2)Adopt NBU-3DV database as the training dataset and test on NAMA3D database.In this subsection, we evaluate the performance of the proposed MDA-SVQM in NAMA3D database and NBU-3DV databases, and performance comparison with other existing well-performed SVQM is also conducted. In MDA-SVQM, the sGoF quality prediction model is established by SVR. In order to ensure the reliability of the performance evaluation, we repeat the train-test procedure for 100 times and select the median value of performance indicators as the final performance indicators. At the same time, two different methods are used in training datasets selection:By the above implementation, it can be ensured that the indicators obtained in the performance verification experiment are accurate and reliable to fully reflect the performance of the quality metric. In comparative methods selection, we first select the traditional IQM (PSNR and SSIM), and then a series of existing well-performed SVQM is also contained. With regards to 2D metrics, we apply them on each view and frame, then take the average value as the approximate quality of stereo video.Firstly, On the other hand, Meanwhile, in order to further prove the extensibility of MDA-SVQM, the performance indicators for different types of distorted video in NAMA3D database are given in In the above experiments and analyses, we verify the accuracy and validity of the proposed MDA-SVQM. To prove the positive role of each module in MDA-SVQM, we designed different experimental schemes. First, by analyzing the decomposition direction of TVJD for a specific test video, we prove that TVJD can adaptively select the decomposition direction according to the scene characteristics, to achieve the purpose of efficient decomposition. On the other hand, for sGoF quality weighting, we prove the validity of STEW by comparing with the traditional temporal pooling strategies on video quality. Finally, by comparing the performance with the existing well-performed SVQM on NAMA3D and NBU-3DV databases, it is shown that the MDA-SVQM has excellent stereo video quality evaluation performance.Based on this work, there is still plenty room for further analysis and research. Firstly, in decomposition for sGoF, although the purposely designed TVJD decomposes the corresponding pixels in inter-view or temporal direction by matching and compensation, which improves the decomposition efficiency. However, the correlation between pixels and pixels in the spatial domain is ignored. That is, decomposition is not implemented for objects in the scene. Hence, in subsequent research, we can attempt to integrate semantically related decomposition strategies, which can achieve object-based decomposition, thereby further enhancing the performance of decomposition. Meanwhile, in MDA-SVQM, for the subbands generated by TVJD, the corresponding feature extraction strategies are selected by analyzing the composition and generation mechanism of the subbands. Although the proposed method considers the validity of subband information and feature extraction to some extent, there is room for further improvement. On the one hand, it is not comprehensive to analyze the nature of subband coefficients only from the perspective of generation mechanism. Obviously, even in the subbands of the same frequency component, there will be differences in the properties because of the different content they contained. Therefore, further studies will attempt to do a more detailed and comprehensive subband analysis from the perspective of the scene objects and the motion in the video. On the other hand, MDA-SVQM extracts features from the perspective of statistical characteristics. Although SWT features can be used to reflect the intrinsic characteristics of coefficients, they are essentially a gradient-based feature extraction strategy. Therefore, in the future research, we can attempt to analyze the high-dimensional information in the TVJD subband from the perspective of deep learning and data mining, so that feature extraction can more accurately reflect the distortion of stereo video.Stereo video contains both stereo information generated by left and right views and spatial-temporal information generated by video, which makes it difficult to describe in the design of quality metric. Aiming at this problem, in this work, we propose a new quality metric based on multi-dimensional analysis (MDA-SVQM). Specifically, by analyzing temporal and inter-view correlation, the optimal decomposition structure is adaptively constructed to complete the decomposition of sGoF by TVJD. Then, according to the characteristics of different subbands, the subbands are classified and the corresponding quality features are extracted. At the same time, sGoF quality is obtained by regression. Finally, an STEW function is designed to weight the sGoF quality, so as to obtain the stereo video quality. Experimental results on NAMA3D database and NBU-3DV databases show that MDA-SVQM is reasonable and effective in structure, better in performance than the existing SVQM, and can accurately measure the distortion of stereo video. In the future work, TVJD can be further analyzed and improved. Firstly, an object-based decomposition can be attempt to design for better consistency with visual perception. Secondly, the characteristics of TVJD subbands should also be further explored, so as to extract the corresponding subband feature more accurately. Another, future study will focus on integrating MDA-SVQM in stereo video coding, especially for coding parameter optimization. Thus, the proposed metric can eventually contribute to the watching experience improvement.


Imaging optimisation can benefit from combining structured data with qualitative data in the form of audio and video recordings. Since video is complex to work with, there is a need to find a workable solution that minimises the additional time investment. The purpose of the paper is to outline a general workflow that can begin to address this issue. What is described is a data management process comprising the three steps of collection, mining and contextualisation. This process offers a way to work systematically and at a large scale without succumbing to the context loss of statistical methods. The proposed workflow effectively combines the video and structured data to enable a new level of insights in the optimisation process. One challenge is that when the observed interventions are different from one another, variations in the structured data reports may be hard to interpret. Whereas one can discern the general trends, outliers and individual data points become impossible to fully understand because the unique conditions of these events are lost in the collection process. There is thus a need to develop a generally applicable strategy to overcome this issue.Angiographic equipment for interventional procedures is used for a range of different treatments. However, the utilisation of these technologies is complex, and methods for optimisation must be considered for each procedure separately. Along with the emitted radiation, modern imaging equipment will also generate massive data streams about their settings and functioning. The collection, monitoring and assessment of this information are central to the optimisation process.The purpose of the paper is to outline a general workflow that can begin to address the difficulties with imaging optimisation by augmenting the stream of structured data with an additional source, that is, with qualitative data in the form of audio and video recordings. If these alternative sources can be combined intelligently and efficiently, it might offer a new prospect for the large-scale optimisation process of image-guided treatments. The separate steps of a workflow are described and exemplified with data from a research project. The focus for these materials will be on data processing matters, where the subsequent optimisation decisions and implementations are reported elsewhere. In this regard, video can nowadays displace the observer of a surgical procedure both in time and in space. By recording surgeries, detailed analyses of the use of medical technologies, 9 and specific work practices are enabled, 11.Many modern operating rooms are now equipped with recording instruments and ceiling-mounted cameras. The reasons for these installations may vary, but the sharing of knowledge is a recurrent argument. The very notions of the operating theatre, or the surgical amphitheatre, speak of a history where surgical suites were built with a dual purpose: the surgery itself and the teaching or performance in front of peers and students. With a developing understanding of asepsis, large live audiences not wearing scrubs were no longer invited to observe the events. However, in practice, any systematic analysis of video-based materials is severely challenging and a very time-consuming process.If such recordings are indeed collected, they may themselves serve multiple purposes. Technically, video uptakes should allow for the evaluation of imaging information in image-guided treatments to be also based on the physical and communicative work conducted. If analysed appropriately, these records may offer invaluable insights for imaging optimisationThe following section outlines a possible workflow for allowing the incorporation of video materials in the optimisation process. The proposed procedure follows the three steps of collection, mining and contextualisation.The first step leading up to the analysis is the collection and preparation of two separate datasets: structured data reports and video recordings, respectively. What is critical at this stage is to establish matching timelines for the separate datasets. In the simplest case, this only means that the video timestamps should be correctly set to enable synchronisation later. However, if multiple video-feeds are being collected, it might be necessary to carry out some more elaborate video-editing work to harmonise the separate streams.pandas.pydata.org), or something similar. Such a move enables subsequent manipulation and analysis.Structured data reports  standard) are standardised but may contain different parameters. Typically, they hold detailed information about the technique and dose parameters along with the precise time for each event. The suggestion is to convert this information into Time Series Data Frames in pandas, the Python Data Analysis Library  were carried out and documented. Out of these, 12 procedures were randomly selected to be additionally recorded on video. In total, 58 h were recorded with the aid of a ceiling-mounted camera in the operating room, a microphone near the operator and by capturing the operators’ screen. These separate video streams were combined into a single timeline using Final Cut Pro X (Apple Incorporated) software.An Artis Zeego angiographic system  provided the structured data, such as, for instance, dose rates, time and the current settings.The analysis started with a summary overview where it became evident that there were substantial differences in the levels of radiation used. The medical procedures varied in complexity, and the procedure time ranged between 1 and 12 h see .However, these variations could not fully account for the differences in radiation levels between the procedures see .The treatments that had also been recorded were plotted as a function of the timewise accumulation of radiation. For each treatment, this would yield a unique pattern or roadmap see .The stepwise increases evidenced most clearly in the procedures displaying the highest values were the results of digital subtraction angiographies (DSAs) and cone beam computed tomographies. While it is already known that DSAs generally come with a higher dose than fluoroscopy, the former were examined further.The entire material contained 667 DSA acquisitions sequences that relied on one specific protocol in 47% of the cases. This half, however, accounted for 79% of the total Incident Air Kerma (in the reference point) produced during all these acquisitions. It thus became relevant to identify where and when this protocol could be replaced by one with lower settings.Thus far, the examination had mainly worked from the structured data, but at this point, the analysis would need additional information. If different protocols and sensor settings should be recommended, those recommendations would also need to consider the differences in image quality that would be the result. In addition, the assessment of whether a single dose should be deemed too high or appropriate must build on the understanding of what problem the operator was trying to solve with that specific image acquisition. Hypothetically, for each case, conceivably, a higher dose was indeed needed to ground a diagnostic decision there and then. Alternatively, perchance the higher dose was avoidable.These are precisely the kinds of questions that we can now begin to address. To advance to this next level of analysis, it becomes necessary to situate specific image acquisition sequences in the unique circumstances of their use. What were the problems encountered there, and what did it take to solve those? Based on this understanding, it then becomes possible to make recommendations for optimised use.A related example of how to work with contextualisation can be given by studying the roadmaps of individual treatments. The features of the plots can help guide the search for critical moments in the data.As with the example of Guided by this search, points of interest can be identified—for instance, moments where there are significant changes in the profile of the graph. Having first identified these points, it becomes possible to interrogate the video materials and analyse the exact circumstances in which individual decisions were made.. Concerning the example, the prevalence or absence of accounts motivating the change in dose would be relevant study objects. Also, it becomes possible to identify instances where some change in image acquisition settings was made but where the increase in image quality could not solve the actual problem encountered in that situation. By enabling the identification of such occurrences, the proposed workflow opens for new areas of improvements. In this way, the outlined approach can be considered as a form of situational optimisation.With this method, it is suddenly possible to examine singular events that significantly affect the outcome in terms of radiation. These short episodes can be examined with video-based methods focussing on task-specific communicationThe proposed workflow comes with its prerequisites and limitations. First, the imaging equipment’s structured data reports should be systematically collected, which entails that relevant information is stored and made retrievable. Second, additional obstacles are found with the production and management of video recordings. At hospitals where cameras are present, it can be difficult to save those recordings to disc. Furthermore, even with such systems in place, there may be a lack of infrastructural support for large video materials’ long-term storage.The described situational optimisation can draw on the rich and contextual information that video records afford while simultaneously avoiding some of the considerable drawbacks with this method. Video materials are complex, and any work on them is time-consuming. The outlined approach, however, can significantly reduce the time spent on convoluted materials. It offers a way to work systematically and at a large scale without succumbing to the context loss of statistical methods. The proposed workflow could thereby enable a new level of insights to inform and guide the optimisation process.


With the aging society, musculoskeletal degenerative diseases are becoming a burden on society, and rotator cuff disease is one of these degenerative diseases. The purpose of this study was to examine the incidence of shoulder osteoarthritis and the etiologic factors of rotator cuff disease in the Korean elderly population. A total of 102 patients performing ultrasonography were recruited, and their demographic factors were analyzed. As functional factors, visual analog scale and the peak torque of external and internal rotators of the shoulder using an isokinetic dynamometer were measured. As an anatomical factor, the acromiohumeral distance in the plain radiograph of the glenohumeral anterior-posterior view was used. There were more female patients (65.7%) than male patients (34.3%). The age range with the highest number of respondents was 50–59 years old. The mean visual analogue score was 4.09 (Min 1 to Max 9). Age and dominant hand side factors appear to be the crucial etiologic factors of the presence and severity of rotator cuff disease. The lower net value of the external rotator strength is weakly related to the presence of rotator cuff disease after adjusting for age, and this is the only modifiable factor in the study. With the aging society, degenerative diseases of the musculoskeletal system are becoming more prevalent and a burden on society . OsteoarUnlike knee, hip, and spine joints, the shoulder joint is not influenced directly by the bodyweight. Rather, it is influenced by multifactorial reasons, including the functional use of the upper extremity in daily life . DegenerThe incidence of rotator cuff disease (RCD) is a strong etiologic factor causing shoulder OA . RCD is In addition to degenerative changes, muscle imbalance surrounding the rotator cuff has been suggested as a crucial cause of RCD ,26,27. SThus, the aim of this study was to investigate whether there are other modifiable etiologic factors of RCD in addition to currently known demographic factors.One hundred and two unilateral RCD patients performing ultrasonography (US) using a 7.5 MHz linear transducer  in the authors’ clinic from October 2016 to May 2021 were recruited. The inclusion criteria were as follows: patients who have visited to our clinic from October 2016 to May 2021, adults above 18 years old, patients who was diagnosed with RCD by ultrasonography, and patientswho providedinformed written consent. The patients who complained of shoulder pain and weakness were examined by ultrasonography. After ultrasonography was taken, the presence of rotator cuff injury was included for the study.An experienced physician, who is the specialist for physical medicine and has experienced 12,000 cases of shoulder ultrasonography per year for 21 years, measured the supraspinatus long- and short-axis using ultrasonography. The patient was in the modified Crass position, where the patient’s ipsilateral hand is placed on the closest hip or buttock region, and long-axis view of the supraspinatus by the transducer was obtained . US imagThe ultrasonic findings were as follows: RCD severity , presence of SASD bursitis, and presence of frozen shoulder. The RCD severity was defined as the supraspinatus tear severity in this study. Our ultrasonographic finding related to severity of the supraspinatus tendon tear was according to Snyder classification for RCD . SupraspThe presence of bursitis was diagnosed when the thickness of subacromial/subdeltoid bursa more than 2 mm in supraspinatus long-axis view . As therTM; Biodex Medical Systems, Inc., New York, NY, USA) was used to measure the peak torque of both the ER and IRs of the shoulder, respectively. When measuring isokinetic strengths of both ER and IRs of the shoulder, patients areon the firm table in the supine posture by fixation of the scapula and trunk. The tested arm wasabducted 90 degrees and elbow wasflexed 90 degrees. The lesion side ER and IR data were calculated as the ratio (ER/IR ratio).As functional factors, pain in visual analog scale (VAS) and the peak torque of both ER and IRs of the shoulder were measured. Pain was divided into resting and movement pain using a VAS (range 0–10). The subjects were divided into three groups according to score: mild (VAS < 4), moderate (VAS 4–6), and severe (VAS > 6). An isokinetic dynamometer  view. One radiologic specialist reviewed all subjects’ plain radiograph of glenohumeral AP view and measured the AHD. We used the same distance measurement tool in picture archiving and communication system (PACS) by a radiologic specialist. In addition, to minimize the measurement bias, we set the same distance and angle during radiographs.The exclusion criteria were as follows: patients who had previous shoulder surgery history, disease of shoulder instability, patients diagnosed with C5 or C6 radiculopathy or both, and lack of written informed consent of patient. This study was approved by the Pusan National University Yangsan Hospital Department of Health Institutional Review Board (IRB No. 05-2021-243). All the data were acquired from the patients by questionnaires and medical records after given informed consent.Statistical analysis was performed using SPSS 18.0 . The purpose of the study was to examine the factors related to the RCD. In order to achieve this purpose, statistical analysis was conducted according to the following procedure. First, frequency analysis and descriptive analysis were conducted to examine the demographic characteristics of the subjects in this study. Second, multiple correlation analysis was used to measure the strength of the linear relationship between each factor and tried to explained the relationship between RCD and demographic factors. Third, a t-test was used for the comparison between two groups related to the RCD . Finally, hierarchical regression analysis was conducted to determine the relationship between the RCD and several independent variables. In this hierarchical multiple regression analysis, the independent variable ‘age’ was entered first in the analysis of the data to investigate the relationship with the RCD. The second independent variable entered was dominant hand side and finally, SASD bursitis was put as the third independent variable to determine if this variable adds to the explanation of the RCD in addition to what is already explained by the prior independent variables.According to an analysis of the demographic characteristics, there were more female patients (65.7%) than male patients (34.3%) . The agep < 0.001), and other variables  showed no statistically significant correlations with each other.As shown in This study conducted the hierarchical regression analysis following the classification of the etiological factors in three dimensions to analyze the determinants of the RCD severity . The detp < 0.001), Model 2 , and Model 3  all showed that the relationship between each variable and the RCD severity was significant. The level of explanatory power for each model also increased to 10.8% (R2 = 0.108) in Model 1, 19.3% (R2 = 0.193) in Model 2, and 21.2% (R2 = 0.212) in Model 3. In addition, an analysis of the tolerance (TOL) and variance inflation factor (VIF) to confirm the multicollinearity showed that the TOL was higher than 0.1. The variance inflation factors were below the threshold of 10, demonstrating no collinearity issues between the independent variables. Accordingly, in the case of Model 1, age  had a positive (+) influence on the RCD severity, meaning that there was a significant correlation between age and the RCD severity. In Model 2, age  had a positive relationship with the RCD severity and a negative relationship with the presence of the dominant hand . These results were significant. Finally, in the results of Model 3, in which the presence of SASD bursitis variable was added, the severity of RCD increased significantly with age and the lesion on the dominant-hand side. On the other hand, there was no significant relationship between the presence of SASD bursitis and RCD severity.Hierarchical regression analysis was performed with three steps to investigate the effect on the RCD severity . In the p = 0.028). Regression analysis was performed between the ER strength and the presence of RCD after adjusting the age factor. With adjusting for age, the lesion-side ER strength in the RCD group decreased significantly   .From previous reports, musculoskeletal OA also increases with age ,36,37,38A previous study reported the RCD-related demographic factors, such as age, sex, and dominant arm ,30,31. IAccording to the original assumption that functional impairment could also be an important factor causing RCD in addition to age, this study investigated the muscle imbalance of the rotator cuff muscles. In previous reports, the related functional factors, such as muscle imbalance of the rotator cuff muscles, mainly in the horizontal plane, were reported to cause RCD ,43. The Sometimes, the muscles of the shoulder may become imbalanced by injury or atrophy, which can cause the shoulder to move forward with certain activities and again cause impingement . With agAlthough RCD can decrease the AHD, the AHD of the symptomatic shoulder in recreational athletes is not significantly decreased . A decreThe clinical importance of this study is that the multifactorial causes of RCD were examined in a Korean population. In addition to the aging factor as a cause of RCD, demographic, functional, and radiological factor analyses were used to reveal the precise relationship of RCD development. In particular, these factors need to be considered in an aging society. These findings also showed that age and dominant hand factors were the only statistically significant factors of RCD development, thus eventually correlated with OA. Although there was a weak correlation, the net strength of ER should also be considered in preventing the development of RCD in the elderly.This study had several limitations. First, only the supraspinatus tendon pathology was regarded as the RCD severity. Severe RCD pathologies, including more than two muscle diseases, were excluded to allow for a clear investigation. In addition, previous surgical operation or unstable patients were also excluded. Thus, the actual prevalence of the rotator cuff pathology from various etiologies would be greater than assumed. Second, in the radiology findings, the AHD and supraspinatus tendon tear thickness were measured using X-ray and US, respectively. The morphological size of the distance and tendon tear may not be precisely related to the functional muscle weakness to cause RCD. Third, other systemic factors, including hypertension, diabetes, and thyroid function, were not investigated. Although these systemic factors are not crucial factors to cause RCD, further study will be needed to determine the relationship between all the demographic factors and RCD development.Age and dominant hand side factors appear to be the crucial etiologic factors of the presence and severity of RCD. The lower net value of the ER strength is weakly related to the presence of RCD after adjusting for age. Despite the degenerating process with aging, consideration of various factors should be needed to retard the RCD development in an aging society.