The intraoperative activity of a surgeon has substantial impact on postoperative outcomes. However, for most surgical procedures, the details of intraoperative surgical actions, which can vary widely, are not well understood. Here we report a machine learning system leveraging a vision transformer and supervised contrastive learning for the decoding of elements of intraoperative surgical activity from videos commonly collected during robotic surgeries. The system accurately identified surgical steps, actions performed by the surgeon, the quality of these actions and the relative contribution of individual video frames to the decoding of the actions. Through extensive testing on data from three different hospitals located in two different continents, we show that the system generalizes across videos, surgeons, hospitals and surgical procedures, and that it can provide information on surgical gestures and skills from unannotated videos. Decoding intraoperative activity via accurate machine learning systems could be used to provide surgeons with feedback on their operating skills, and may allow for the identification of optimal surgical behaviour and for the study of relationships between intraoperative factors and postoperative outcomes. A machine learning system leveraging a vision transformer and supervised contrastive learning accurately decodes elements of intraoperative surgical activity from videos commonly collected during robotic surgeries. It was recently demonstrated that such outcomes are strongly influenced by intraoperative surgical activity3, that is, what actions are performed by a surgeon during a surgical procedure and how well those actions are executed. For the vast majority of surgical procedures, however, a detailed understanding of intraoperative surgical activity remains elusive. This scenario is all too common in other domains of medicine, where the drivers of certain patient outcomes either have yet to be discovered or manifest differently. The status quo within surgery is that intraoperative surgical activity is simply not measured. Such lack of measurement makes it challenging to capture the variability in the way surgical procedures are performed across time, surgeons and hospitals, to test hypotheses associating intraoperative activity with patient outcomes, and to provide surgeons with feedback on their operating technique.The overarching goal of surgery is to improve postoperative patient outcomesIntraoperative surgical activity can be decoded from videos commonly collected during robot-assisted surgical procedures. Such decoding provides insight into what procedural steps (such as tissue dissection and suturing) are performed over time, how those steps are executed  by the operating surgeon, and the quality with which they are executed , limiting their utility. These technologies are also seldom rigorously evaluated, where it remains an open question whether they generalize to, or perform well in, new settings, such as with unseen videos from different surgeons, surgical procedures and hospitals. Such a rigorous evaluation is critical to ensuring the development of safe and trustworthy AI systems.Given these limitations, emerging technologies such as artificial intelligence (AI) have been used to identify surgical activity6), which have been developed to decode only a single element (such as surgeon skill). We also show that SAIS, through deployment on surgical videos without any human-driven annotations, provides information about intraoperative surgical activity, such as its quality over time, that otherwise would not have been available to a surgeon. Through a qualitative assessment, we demonstrate that SAIS provides accurate reasoning behind its decoding of intraoperative activity. With these capabilities, we illustrate how SAIS can be used to provide surgeons with actionable feedback on how to modulate their intraoperative surgical behaviour.In this study, we propose a unified surgical AI system (SAIS) that decodes multiple elements of intraoperative surgical activity from videos collected during surgery. Through rigorous evaluation on data from three hospitals, we show that SAIS reliably decodes multiple elements of intraoperative activity, from the surgical steps performed to the gestures that are executed and the quality with which they are executed by a surgeon. This reliable decoding holds irrespective of whether videos are of different surgical procedures and from different surgeons across hospitals. We also show that SAIS decodes such elements more reliably than state-of-the-art AI systems, such as Inception3D  curves stratified according to the three subphases Fig. . We obseTo determine whether SAIS can generalize to unseen surgeons at distinct hospitals, we deployed it on video samples from St. Antonius Hospital (SAH) Fig.  and Hous10 score  suturing step (20 min long) without any human supervision  mechanism was the largest contributor to the performance of SAIS, where its absence resulted in ∆PPV of approximately −20. This finding implies that capturing the relationship between, and temporal ordering of, frames is critical for the decoding of intraoperative surgical activity. We also observed that the dual-modality input  has a greater contribution to performance than using either modality of data alone. By removing RGB frames (‘without RGB’) or optical flow (‘without flow’), the model exhibited an average ∆PPV of approximately −3 relative to the baseline implementation. Such a finding suggests that these two modalities are complementary to one another. We therefore used the baseline model (SAIS) for all subsequent experiments.In the previous section, we showed the ability of SAIS to decode surgical subphases (the ‘what’ of surgery) and to generalize to video samples from unseen surgeons at distinct hospitals, and also quantified the marginal benefit of its components via an ablation study. In this section, we examine the ability of SAIS to decode surgical gestures (the ‘how’ of surgery) performed during both tissue suturing and dissection activities  and in the surgical field of view . Furthermore, our hypothesis for why this degradation affects certain gestures (such as cold cuts) more than others (such as clips) is that the latter exhibits less variability than the former, and is thus easier to classify by the model.To measure the degree to which SAIS can generalize to unseen surgeons at a distinct hospital, we deployed it on video samples from SAH Fig. . We founWhile videos of different surgical procedures (such as nephrectomy versus prostatectomy) may exhibit variability in, for example, anatomical landmarks (such as kidney versus prostate), they are still likely to reflect the same tissue dissection gestures. We explored the degree to which such variability affects the ability of SAIS to decode dissection gestures. Specifically, we deployed SAIS on video samples of a different surgical step: renal hilar dissection (HD), from a different surgical procedure: robot-assisted partial nephrectomy (RAPN) 12 C UCL)12 . While tC UCL)12  and DVC C UCL)12 .12  to which it has never been exposed .n = 800 gesture predictions in total). This ensured we retrieved predictions from a more representative and diverse set of videos, thus improving the generalizability of our findings. We report the precision of these predictions after manually confirming whether or not the corresponding video samples reflected the correct gesture  gesture predictions exhibited precision of ~0.75 in both anatomical locations. We also observed that SAIS was able to identify an additional gesture category beyond those it was originally trained on. Manually inspecting the video samples in the cold cut (c) gesture category with a seemingly low precision, we found that SAIS was identifying a distinct cutting gesture, also known as a hot cut, which, in contrast to a cold cut, involves applying heat/energy to cut tissue.To qualitatively evaluate the performance of SAIS, we present its gesture predictions for a single 30-min NS video Fig. . Each roWe also found that SAIS can discover outlier behaviour, despite not being explicitly trained to do so. Specifically, SAIS identified a contiguous 60-s interval during which a camera move (m) was performed, and which is 60× longer than the average duration (1 s) of a camera move. Suspecting outlier behaviour, we inspected this interval and discovered that it coincided with the removal of the camera from the patient’s body, its inspection by the operating surgeon, and its re-insertion into the patient’s body.At this point, we have demonstrated that SAIS, as a unified AI system, can independently achieve surgical subphase recognition (the what of surgery) and gesture classification (the how of surgery), and generalize to samples from unseen videos in the process. In this section, we examine the ability of SAIS to decode skill assessments from surgical videos. In doing so, we also address the how of surgery, however through the lens of surgeon skill. We evaluated the quality with which two suturing subphases were executed by surgeons: needle handling and needle driving  have achieved state-of-the-art results in decoding surgical skills on the basis of videos of either a laboratory trialodel see  to decododel see . We foun14. We therefore wanted to explore whether or not SAIS was identifying relevant visual cues while decoding the skill level of surgeons. This would instill machine learning practitioners with confidence that SAIS is indeed latching onto appropriate features, and can thus be trusted in the event of future deployment within a clinical setting. We first retrieved a video sample depicting a low-skill activity (needle handling or needle driving) that was correctly classified by SAIS. By inspecting the attention placed on such frames by the attention mechanism  placed on frames of a video sample of needle handling Fig.  and needWe wanted to demonstrate that SAIS can also provide surgeons with information about surgical skills that otherwise would not have been available to them. To that end, we tasked SAIS with assessing the skill of all needle handling and needle driving video samples collected from SAH.With needle handling (and needle driving) viewed as a subphase of a single stitch and knowing that a sequence of stitches over time makes up a suturing activity (such as VUA) in a surgical case, SAIS can generate a surgical skills profile for a single case Fig.  for needSAIS can also provide actionable information beyond the individual surgical case level. To illustrate this, we present the proportion of needle handling Fig.  and needWe initially claimed that the decoding of intraoperative surgical activity can pave the way for multiple downstream applications, one of which is the provision of postoperative feedback to surgeons on their operating technique. Here we provide a template of how SAIS, based on the findings we have presented thus far, can deliver on this goal. In reliably decoding surgical subphases and surgical skills while simultaneously providing its reasoning for doing so, SAIS can provide feedback of the following form: ‘when completing stitch number three of the suturing step, your needle handling (what—subphase) was executed poorly (how—skill). This is probably due to your activity in the first and final quarters of the needle handling subphase (why—attention)’. Such granular and temporally localized feedback now allows a surgeon to better focus on the element of intraoperative surgical activity that requires improvement, a capability that was not previously available.P = 0.005) more likely when needle driving was assessed as high skill than as low skill by SAIS. When aggregating the skill assessments of video samples within a surgical case, that relationship is further strengthened . These preliminary findings are consistent with those based on manual skill assessments from recent studies16.While useful for mastering a surgical technical skill itself, surgeon feedback becomes more clinically meaningful when grounded in patient outcomes. For example, if low-skill assessments are associated with poor outcomes, then a surgeon can begin to modulate specific behaviour to improve such outcomes. To that end, we conducted a preliminary analysis regressing the surgeon skill assessments of SAIS at USC onto a patient’s binary recovery of urinary continence (ability to voluntarily control urination) 3 months after surgery . When coOnly in the past decade or so has it been empirically demonstrated that intraoperative surgical activity can have a direct influence on postoperative patient outcomes. However, discovering and acting upon this relationship to improve outcomes is challenging when the details of intraoperative surgical activity remain elusive. By combining emerging technologies such as AI with videos commonly collected during robotic surgeries, we can begin to decode multiple elements of intraoperative surgical activity.We have shown that SAIS can decode surgical subphases, gestures and skills, on the basis of surgical video samples, in a reliable, objective and scalable manner. Although we have presented SAIS as decoding these specific elements in robotic surgeries, it can conceivably be applied to decode any other element of intraoperative activity from different surgical procedures. Decoding additional elements of surgery will simply require curating a dataset annotated with the surgical element of interest. To facilitate this, we release our code such that others can extract insight from their own surgical videos with SAIS. In fact, SAIS and the methods that we have presented in this study apply to any field in which information can be decoded on the basis of visual and motion cues.Compared with previous studies, our study offers both translational and methodological contributions. From a translational standpoint, we demonstrated the ability of SAIS to generalize across videos, surgeons, surgical procedures and hospitals. Such a finding is likely to instil surgeons with greater confidence in the trustworthiness of SAIS, and therefore increases their likelihood of adopting it. This is in contrast to previous work that has evaluated AI systems on videos captured in either a controlled laboratory environment or a single hospital, thereby demonstrating limited generalization capabilities.17 is critical to gaining the trust of surgeons and ensuring the safe deployment of AI systems for high-stakes decision making such as skill-based surgeon credentialing. This is in contrast to previous AI systems such as MA-TCN12, which is only capable of highlighting the relative importance of data modalities , and therefore lacks the finer level of explainability of SAIS.From a methodological standpoint, SAIS has much to offer compared with AI systems previously developed for decoding surgical activity. First, SAIS is unified in that it is capable of decoding multiple elements of intraoperative surgical activity without any changes to its underlying architecture. By acting as a dependable core architecture around which future developments are made, SAIS is likely to reduce the amount of resources and cognitive burden associated with developing AI systems to decode additional elements of surgical activity. This is in contrast to the status quo in which the burdensome process of developing specialized AI systems must be undertaken to decode just a single element. Second, SAIS provides explainable findings in that it can highlight the relative importance of individual video frames in contributing to the decoding. Such explainability, which we systematically investigate in a concurrent study18, whereby video samples must contain either 16 frames or multiples thereof13. Abiding by this constraint can be suboptimal for achieving certain tasks, and departing from it implies the inability to leverage the pre-trained parameters that have proven critical to the success of previous methods. Furthermore, SAIS is architecturally different from previous models in that it learns prototypes via supervised contrastive learning to decode surgical activity, an approach that has yet to be explored with surgical videos. Such prototypes pave the way for multiple downstream applications from detecting out-of-distribution video samples, to identifying clusters of intraoperative activity, and retrieving samples from a large surgical database19.SAIS is also flexible in that it can accept video samples with an arbitrary number of video frames as input, primarily due to its transformer architecture. Such flexibility, which is absent from previous commonly used models such as 3D-CNNs, confers benefits to training, fine-tuning and performing inference. During training, SAIS can accept a mini-batch of videos each with a different number of frames. This can be achieved by padding videos in the mini-batch (with zeros) that have fewer frames, and appropriately masking the attention mechanism in the transformer encoder see . This isWe also showed that SAIS can provide information that otherwise would not have been readily available to surgeons. This includes surgical gesture and skill profiles, which reflect how surgical activity is executed by a surgeon over time for a single surgical case and across different cases. Such capabilities pave the way for multiple downstream applications that otherwise would have been difficult to achieve. For example, from a scientific perspective, we can now capture the variability of surgical activity across time, surgeons and hospitals. From a clinical perspective, we can now test hypotheses associating intraoperative surgical activity with long-term patient outcomes. This brings the medical community one step closer to identifying, and eventually modulating, causal factors responsible for poor outcomes. Finally, from an educational perspective, we can now monitor and provide surgeons with feedback on their operating technique. Such feedback can help surgeons master necessary skills and contribute to improved patient outcomes.10 are critical to adapting to an evolving taxonomy of surgical activity over time.There are important challenges our work does not yet address. First, our framework, akin to others in the field, is limited to only decoding the elements of surgical activity that have been previously outlined in some taxonomy (such as gestures). In other words, it cannot decode what it does not know. Although many of these taxonomies have been rigorously developed by teams of surgeons and through clinical experience, they may fail to shed light on other intricate aspects of surgical activity. This, in turn, limits the degree to which automated systems can discover novel activity that falls beyond the realm of existing protocol. Such discovery can lend insight into, for example, optimal but as-of-yet undiscovered surgical behaviour. In a similar vein, SAIS is currently incapable of decoding new elements of surgical activity beyond those initially presented to it. Such continual learning capabilitiesThe goal of surgery is to improve patient outcomes. However, it remains an open question whether or not the decoded elements of intraoperative surgical activity: subphases, gestures and skills, are the factors most predictive of postoperative patient outcomes. Although we have presented preliminary evidence in this direction for the case of surgical skills, large-scale studies are required to unearth these relationships. To further explore these relationships and more reliably inform future surgical practice, we encourage the public release of large-scale surgical video datasets from different hospitals and surgical specialties. Equipped with such videos and SAIS, researchers can begin to decode the various elements of surgery at scale.20, whereby an AI system is presented with increasingly difficult tasks during the learning process in order to improve its overall performance. In a concurrent study21, we also investigate whether SAIS exhibits algorithmic bias against various surgeon subcohorts22. Such a bias analysis is particularly critical if SAIS is to be used for the provision of feedback to surgeons. For example, it may disadvantage certain surgeon subcohorts  and thus affect their ability to develop professionally.Moving forward, we look to investigate whether SAIS has the intended effect on clinical stakeholders. For example, we aim to deploy SAIS in a controlled laboratory environment to assess the skill level of activity performed by medical students and provide them with feedback based on such assessments. This will lend practical insight into the utility of AI-based skill assessments and its perception by surgical trainees. We also intend to explore the interdependency of the elements of intraoperative surgical activity . This can be achieved, for example, by training a multi-task variant of SAIS in which all elements are simultaneously decoded from a video. In such a setting, positive interference between the tasks could result in an even more reliable decoding. Alternatively, SAIS can be trained to first perform subphase recognition (a relatively easy task) before transferring its parameters to perform skill assessment (a relatively harder task). This is akin to curriculum learningAll datasets  were collected under institutional review board approval in which informed consent was obtained (HS-17-00113). These datasets were de-identified before model development.24. One line of research has focused on exploiting robot-derived sensor data, such as the displacement and velocity of the robotic arms (kinematics), to predict clinical outcomes28. For example, researchers have used automated performance metrics to predict a patient’s postoperative length of stay within a hospital26. Another line of research has instead focused on exclusively exploiting live surgical videos from endoscopic cameras to classify surgical activity29, gestures33 and skills35, among other tasks37. For information on additional studies, we refer readers to a recent review9. Most recently, attention-based neural networks such as transformers38 have been used to distinguish between distinct surgical steps within a procedure42.Previous work has used computational methods, such as AI, to decode surgery11 is rigorous. Although it lends insight into the generalizability of a model to a video from an unseen participant, this setup involves reporting a cross-validation score, which is often directly optimized by previous methods , therefore producing an overly optimistic estimate of performance. As another example, consider the data split used for the CholecT50 dataset43. Here there is minimal information about whether videos in the training and test sets belong to the same surgeon. Lastly, the most recent DVC UCL dataset12 consists of 36 publicly available videos for training and 9 private videos for testing. After manual inspection, we found that these nine videos come from six surgeons whose data are also in the training set. This is a concrete example of surgeon data leakage, and as such, we caution the use of such datasets for benchmarking purposes. It is therefore critical to more rigorously evaluate the performance of SAIS, and in accordance with how it is likely to be deployed in a clinical setting.Previous work often splits their data in a way that has the potential for information ‘leakage’ across training and test sets. For example, it is believed that the commonly adopted leave-one-user-out evaluation setup on the JIGSAWS datasetWe focused on surgical videos depicting two types of surgical activity commonly performed within almost any surgery: tissue dissection and suturing, which we next outline in detail.Tissue dissection is a fundamental activity in almost any surgical procedure and involves separating pieces of tissue from one another. For example, the RARP surgical procedure, where a cancerous prostate gland is removed from a patient’s body, entails several tissue dissection steps, one of which is referred to as nerve-sparing, or NS. NS involves preserving the neurovascular bundle, a mesh of vasculature and nerves to the left and right of the prostate, and is essential for a patient’s postoperative recovery of erectile function for sexual intercourse. Moreover, an RAPN surgical procedure, where a part of a cancerous kidney is removed from a patient’s body, entails a dissection step referred to as hilar dissection, or HD. HD involves removing the connective tissue around the renal artery and vein to control any potential bleeding from these blood vessels.44 enabling us to annotate any tissue dissection step with a sequence of discrete dissection gestures over time.These dissection steps (NS and HD), although procedure specific (RARP and RAPN), are performed by a surgeon through a common vocabulary of discrete dissection gestures. In our previous work, we developed a taxonomy45 and involves bringing tissue together. For example, the RARP procedure entails a suturing step referred to as vesico-urethral anastomosis, or VUA. VUA follows the removal of the cancerous prostate gland and involves connecting, via stitches, the bladder neck  to the urethra , and is essential for postoperative normal flow of urine. The VUA step typically consists of an average of 24 stitches where each stitch can be performed by a surgeon through a common vocabulary of suturing gestures. In our previous work, we developed a taxonomy5 enabling us to annotate any suturing activity with a sequence of discrete suturing gestures. We note that suturing gestures are different to, and more subtle than, dissection gestures.Suturing is also a fundamental component of surgery46 enabling us to annotate any suturing subphase with a binary skill level (low skill versus high skill).Each stitch can also be deconstructed into the three recurring subphases of (1) needle handling, where the needle is held in preparation for the stitch, (2) needle driving, where the needle is driven through tissue (such as the urethra), and (3) needle withdrawal, where the needle is withdrawn from tissue to complete a single stitch. The needle handling and needle driving subphases can also be evaluated on the basis of the skill level with which they are executed. In our previous work, we developed a taxonomyWe collected videos of entire robotic surgical procedures from three hospitals: USC, SAH and HMH. Each video of the RARP procedure, for example, was on the order of 2 h. A medical fellow (R.M.) manually identified the NS tissue dissection step and VUA tissue suturing step in each RARP video. We outline the total number of videos and video samples from each hospital in Table 47.It is important to note that human raters underwent a training phase whereby they were asked to annotate the same set of surgical videos, allowing for the calculation of the inter-rater reliability (between 0 and 1) of their annotations. Once this reliability exceeded 0.8, we deemed the training phase complete44. We focused on the six most commonly used dissection gestures: cold cut (c), hook (h), clip (k), camera move (m), peel (p) and retraction (r). Specifically, upon observing a gesture, a human rater recorded the start time and end time of its execution by the surgeon. Therefore, each NS step resulted in a sequence of n ≈ 400 video samples of gestures (from six distinct categories) with each video sample on the order of 0–10 s in duration. Moreover, each video sample mapped to one and only one gesture. The same strategy was followed for annotating the VUA suturing step with suturing gestures. This annotation followed the strict guidelines of our previously developed taxonomy of suturing gestures5. We focused on the four most commonly used suturing gestures: right forehand under (R1), right forehand over (R2), left forehand under (L1) and combined forehand over (C1).Each video of the NS dissection step (on the order of 20 min) was retrospectively annotated by a team of trained human raters  with tissue dissection gestures. This annotation followed the strict guidelines of our previously developed taxonomy of dissection gestures46. Since the VUA step is a reconstructive one in which the bladder and urethra are joined together, it often requires a series of stitches .Each video of the VUA suturing step (on the order of 20 min) was retrospectively annotated by a team of trained human raters  with surgical subphases and skills. This annotation followed the strict guidelines of our previously developed taxonomy referred to as the end-to-end assessment of suturing expertise or EASEn = 24 video samples of the needle handling, needle driving and needle withdrawal subphases with each video sample on the order of 10–30 s. The distribution of the duration of such video samples is provided in Supplementary Note With a single stitch consisting of the three subphases of needle handling, needle driving and needle withdrawal , a human rater would first identify the start time and end time of each of these subphases. Therefore, each VUA step may have Human raters were also asked to annotate the quality of the needle handling or needle driving activity (0 for low skill and 1 for high skill). For needle handling, a high-skill assessment is based on the number of times the surgeon must reposition their grip on the needle in preparation for driving it through the tissue (the fewer the better). For needle driving, a high-skill assessment is based on the smoothness and number of adjustments required to drive the needle through the tissue (the smoother and fewer number of adjustments the better). Since each video sample was assigned to multiple raters, it had multiple skill assessment labels. In the event of potential disagreements in annotations, we considered the lowest (worst) score. Our motivation for doing so was based on the assumption that if a human rater penalized the quality of the surgeon’s activity, then it must have been due to one of the objective criteria outlined in the scoring system, and is thus suboptimal. We, in turn, wanted to capture and encode this suboptimal behaviour.In all experiments, we trained SAIS on a training set of video samples and evaluated it using ten-fold Monte Carlo cross- validation where each fold’s test set consisted of subphases from videos unseen during training. Such an approach contributes to our goal of rigorous evaluation by allowing us to evaluate the ability of SAIS to generalize to unseen videos (hereon referred to as across videos). This setup is also more challenging and representative of real-world deployment than one in which an AI system generalizes to unseen samples within the same video. As such, we adopted this evaluation setup for all experiments outlined in this study, unless otherwise noted. A detailed breakdown of the number of video samples used for training, validation and testing can be found in Supplementary Note For all the experiments conducted, unless otherwise noted, we split the data at the case video level into a training (90%) and test set (10%). We used 10% of the videos in the training set to form a validation set with which we performed hyperparameter tuning. By splitting at the video level, whereby data from the same video do not appear across the sets, we are rigorously evaluating whether the model generalizes across unseen videos. Note that, while it is possible for data from the same surgeon to appear in both the training and test sets, we also experiment with even more rigorous setups: across hospitals—where videos are from entirely different hospitals and surgeons—and across surgical procedures—where videos are from entirely different surgical procedures (such as nephrectomy versus prostatectomy). While there are various ways to rigorously evaluate SAIS, we do believe that demonstrating its generalizability across surgeons, hospitals and surgical procedures, as we have done, is a step in the right direction. We report the performance of models as an average, with a standard deviation, across the folds.To capture both visual and motion cues in surgical videos, SAIS operated on two distinct modalities: live surgical videos in the form of RGB frames and the corresponding optical flow of such frames. Surgical videos can be recorded at various sampling rates, which have the units of frames per second (fps).48, which we found to provide reasonable maps.Knowledge of the sampling rate alongside the natural rate with which activity occurs in a surgical setting is essential to multiple decisions. These can range from the number of frames to present to a deep learning network, and the appropriate rate with which to downsample videos, to the temporal step size used to derive optical flow maps, as outlined next. Including too many frames where there is very little change in the visual scene leads to a computational burden and may result in over-fitting due to the inclusion of highly similar frames . On the other hand, including too few frames might result in missing visual information pertinent to the task at hand. Similarly, deriving reasonable optical flow maps, which is a function of a pair of images which are temporally spaced, is contingent upon the time that has lapsed between such images. Too short of a timespan could result in minimal motion in the visual scene, thus resulting in uninformative optical flow maps. Analogously, too long of a timespan could mean missing out on informative intermediate motion in the visual scene. We refer to these decisions as hyperparameters see . ThroughOur AI system—SAIS—is vision based and unified Fig. . It is vD-dimensional representations, T temporally ordered frames via a (frozen) vision transformer (ViT) pre-trained on the ImageNet dataset in a self-supervised manner49. In short, this pre-training setup, entitled DINO, involved optimizing a contrastive objective function whereby representations of the same image, augmented in different ways (such as random cropping), are encouraged to be similar to one another. For more details, please refer to the original paper50.We extract a sequence of H × H and introduce a self-attention mechanism that attempts to capture the relationship between image patches . We found that this spatial attention picks up on instrument tips, needles, and anatomical edges , to obtain an E-dimensional video representation, The two modality-specific video representations, hVideo, undergoes a series of attractions and repulsions with learnable embeddings, which we refer to as prototypes. Each prototype, p, reflects a single category of interest and is of the same dimensionality as hVideo. The representation, c, is attracted to the single prototype, C is the total number of categories. We achieve this by leveraging contrastive learning and minimizing the InfoNCE loss, To achieve the task of interest, the video-specific representation, During training, we share the parameters of the Transformer encoder across modalities to avoid over-fitting. As such, we learn, in an end-to-end manner, the parameters of the Transformer encoder, the classification token embedding, the temporal positional embeddings, the parameters of the projection head and the category-specific prototypes.hVideo, and each of the prototypes, To classify a video sample into one of the categories, we calculate the similarity  between the video representation, hVideo, can be dependent on the choice of frames  which are initially input into the model. Therefore, to account for this dependence and avoid missing potentially informative frames during inference, we deploy what is known as test-time augmentation (TTA). This involves augmenting the same input multiple times during inference, which, in turn, outputs multiple probability mass functions. We can then average these probability mass functions, analogous to an ensemble model, to make a single classification. In our context, we used three test-time inputs; the original set of frames at a fixed sampling rate, and those perturbed by offsetting the start frame by K frames at the same sampling rate. Doing so ensures that there is minimal frame overlap across the augmented inputs, thus capturing different information, while continuing to span the most relevant aspects of the video.The video representation, K = 3 and K = 6 frames.During training and inference, we use the start time and end time of each video sample to guide the selection of video frames from that sample. For gesture classification, we select ten equally spaced frames from the video sample. For example, for a video sample with a frame rate of 30 Hz and that is 3 s long, then from the original 30 × 3 = 90 frames, we would only retrieve frames ∈ . In contrast, for subphase recognition and skill assessment, we select every other tenth frame. For example, for the same video sample above, we would only retrieve frames ∈ . We found that these strategies resulted in a good trade-off between computational complexity and capturing sufficiently informative signals in the video to complete the task. Similarly, optical flow maps were based on pairs of images that were 0.5 s apart. Shorter timespans resulted in frames that exhibited minimal motion and thus uninformative flow maps. During training, to ensure that the RGB and optical flow maps were associated with the same timespan, we retrieved maps that overlapped in time with the RGB frames. During inference, and for TTA, we offset both RGB and optical flow frames by 51 using a V100 GPU on a DGX machine. Each RGB frame and optical flow map was resized to 224 × 224  before being input into the ViT feature extractor. The ViT feature extractor pre-processed each frame into a set of square patches of dimension H = 16 and generated a frame representation of dimension D = 384. All video representations and prototypes are of dimension E = 256. In practice, we froze the parameters of the ViT, extracted all such representations offline , and stored them as h5py files. We followed the same strategy for extracting representations of optical flow maps. This substantially reduced the typical bottleneck associated with loading videos and streamlined our training and inference process. This also facilitates inference performed on future videos. Once a new video is recorded, its features can immediately be extracted in an offline manner, and stored for future use.We conduct our experiments in PyTorche−1, and optimize its parameters via stochastic gradient descent. Mini-batch samples are often required to have the same dimensionality (B × T × D) where B is the batch size, T is the number of frames and D is the dimension of the stored representation. Therefore, when we encountered video samples in the same mini-batch with a different number of temporal frames (such as T = 10 versus T = 11), we first appended placeholder representations (tensors filled with zeros) to the end of the shorter video samples. This ensured all video samples in the mini-batch had the same dimension. To avoid incorporating these padded representations into downstream processing, we used a masking matrix (matrix with binary entries) indicating which representations the attention mechanism should attend to. Importantly, padded representations are not attended to during a forward pass through SAIS.Unless otherwise stated, we trained SAIS using a mini-batch size of eight video samples and a learning rate of 1We trained several variants of SAIS to pinpoint the contribution of each its components on overall performance. Specifically, the model variants are trained using SAIS (baseline), evaluated without test-time augmentation (‘without TTA’), and exposed to only optical flow (‘without RGB’) or RGB frames (‘without flow’) as inputs. We also removed the self-attention mechanism which captured the relationship between, and temporal ordering of, frames (‘without SA’). In this setting, we simply averaged the frame features. Although we present the PPV in After we trained and evaluated a model on video samples (on the order of 10–30 s), we deployed it on entire videos (on the order of 10–30 min) to decode an element of surgical activity without human supervision. We refer to this process as inference. As we outline next, a suitable implementation of inference is often dependent on the element of surgical activity being decoded.Video samples used for training and evaluating SAIS to decode the three suturing subphases of needle handling, needle driving and needle withdrawal spanned, on average, 10–30 s , needle driving (ND) and needle withdrawal (NW). Approach 2: we presented SAIS with 5-s non-overlapping video samples from the same video. The motivation for choosing a shorter video sample is to capture a brief subphase that otherwise would have bled into another subphase when using a longer video sample. As such, each 5-s video sample was associated with a single probabilistic output. Note that we followed the same approach for selecting frames from each video sample as we did during the original training and evaluation setup .P) × 10 folds (F) × 3 TTAs = 90 probabilistic outputs in total. As is done with ensemble models, we then averaged these probabilistic outputs (a.k.a. bagging) to obtain a single probabilistic output,jth probability value for j ∈  (C categories) is obtained as follows:Recall that we trained SAIS using ten-fold Monte Carlo cross-validation, resulting in ten unique models. To increase our confidence in the inference process, we performed inference following the two aforementioned approaches with each of the ten models. As such, each 10-s timespan was associated with 3 probabilistic outputs , we were left with individual predictions for each subphase spanning at most 10 s (because of how we earlier identified video samples). However, we know from observation that certain subphases can be longer than 10 s . Specifically, we presented SAIS with 1-s non-overlapping video samples of an entire NS video. As such, each 1-s video sample was associated with a single probabilistic output, As with inference for suturing subphase recognition, we deployed the ten SAIS models (from the ten Monte Carlo folds) and three TTAs on the same video samples. As such, each 1-s video sample was associated with 10 × 3 = 30 probabilistic outputs. These are then averaged to obtain a single probabilistic output, Sthresh = 1.74 led to reasonable results.We also leveraged the entropy of gesture classifications as a way to quantify uncertainty and thus abstain from making highly uncertain gesture classifications. We found that To account for the observation that gestures can span multiple seconds, we aggregated individual 1-s predictions that were close to one another over time. Specifically, we aggregated multiple predictions of the same gesture into a single prediction if they were less than 2 s apart. For example, if a retraction gesture (r) is predicted at intervals 10–11 s, 11–12 s and 15–16 s, we treated this as two distinct retraction gestures. The first one spans 2 s (10–12 s) while the second one spans 1 s (15–16 s). This avoids us tagging spurious and incomplete gestures  as an entirely distinct gesture over time. Our 2-s interval introduced some tolerance for a potential misclassification between gestures of the same type and allowed for the temporal continuity of the gestures.11 and DVC UCL12. In short, these datasets contain video samples of individuals performing suturing gestures either in a controlled laboratory setting or during the dorsal vascular complex step of the RARP surgical procedure. For further details on these datasets, we refer readers to the original respective publications.We trained SAIS on two publicly available datasets: JIGSAWS11. This involves training on video samples from all but one user and evaluating on those from the remaining user. These details can be found in a recent review9.We followed the commonly adopted leave-one-user-out cross-validation setup12. The publicly available dataset, at the time of writing, is composed of 36 such videos .We trained the I3D model to decode the binary skill level of needle handling and needle driving on the basis of video samples of the VUA step. For a fair comparison, we presented the I3D model with the same exact data otherwise presented to SAIS (our model). In training the I3D model, we followed the core strategy proposed in ref. e−3.However, having observed that the I3D model was quite sensitive to the choice of hyperparameters, we found it necessary to conduct an extensive number of experiments to identify the optimal setup and hyperparameters for decoding surgical skill, the details of which are outlined next. First, we kept the logits layer as is, resulting in a 400-dimensional representation, and followed it with a non-linear classification head to output the probability of, for example, a high-skill activity. We also leveraged both data modalities (RGB and flow) which we found to improve upon the original implementation that had used only a single modality. Specifically, we added the two 400-dimensional representations  to one another and passed the resultant representation through the aforementioned classification head. With the pre-trained I3D expecting an input with 16 frames or multiples thereof, we provided it with a video sample composed of 16 equally spaced frames between the start time and end time of that sample. While we also experimented with a different number of frames, we found that to yield suboptimal results. To train I3D, we used a batch-size of 16 video samples and a learning rate of 1Z1 ∈ , for each video sample reflecting the probability of a high-skill assessment. In the first variant of this experiment, we assigned each video sample, linked to a surgical case, a urinary continence recovery (3 months after surgery) outcome, Y. To account for the fact that a single outcome, Y, is linked to an entire surgical case, in the second variant of this experiment, we averaged the outputs, Z, for all video samples within the same surgical case. This, naturally, reduced the total number of samples available.To determine whether the skill assessments of SAIS are associated with patient outcomes, we conducted an experiment with two variants. We first deployed SAIS on the test set of video samples in each fold of the Monte Carlo cross-validation setup. This resulted in an output, Z2) and the age of the patient being operated on (Z3), and regressed the probabilistic outputs of SAIS to the urinary continence recovery outcome using a logistic regression model (SPSS), as shown below (σ is the sigmoid function). After training this model, we extracted the coefficient, b1, and report the odds ratio (OR) and the 95% confidence interval (CI).In both experiments, we controlled for the total number of robotic surgeries performed by the surgeon (caseload, Further information on research design is available in the Supplementary InformationSupplementary notes, figures and tables.Reporting SummaryPeer Review File


The debriefing process after health care simulations should provide a psychologically safe learning environment for nursing students. Case video-based debriefing on a simulation of high-risk neonatal care can help nursing students feel psychologically safe and make learning more effective. In this study, we developed case video-based debriefing materials for a simulation of high-risk neonatal care for nursing students in South Korea and evaluated their effects.This mixed-methods study, consisting of a survey and an in-depth interview, was conducted between August and December 2022. The participants were 27 nursing students for the development of the case video-based debriefing and 51 nursing students for the evaluation of its effects  at a university in South Korea. A case video-based debriefing on a simulation of high-risk neonatal care was developed, and the experimental group took part in case video-based debriefing. The participants’ self-efficacy, critical thinking, state anxiety, and satisfaction with practice were examined. The experimental group’s learning experiences were explored. Quantitative data were analyzed using the chi-square test, the unpaired t-test, and repeated-measures analysis of variance. Qualitative content analysis was conducted.In the experimental group, critical thinking and satisfaction with practice increased to a greater extent than in the control group. However, the changes in self-efficacy and state anxiety were not significantly different between the experimental and control groups. Four categories were extracted from nursing students who participated in the case video-based debriefing: “learning facilitated by the simulation,” “expanded learning,” “safe learning environment,” and “efficient utilization of case videos.”Case video-based debriefing on a simulation of high-risk neonatal care effectively enhanced nursing students’ critical thinking and satisfaction with practice, and it will be utilized to improve nursing students’ competency in high-risk neonatal care. For nursing students to become competent in high-risk neonatal care in the clinical field after graduation, they must gain experience discovering and solving neonatal care problems by applying theoretical nursing knowledge to an authentic clinical context . Due to Simulations are an effective educational method that helps nursing students deal with realistic clinical cases and perform nursing activities in a safe environment . SimulatHealth care simulations consist of three stages: pre-briefing, which is a preparatory process; simulation running, which proceeds according to the learning goals; and debriefing, in which learners reflect on their performance and receive feedback . DebriefIn previous research, video debriefing seemed to induce learners to act more appropriately based on direct observations of their actions , but leaTherefore, the first aim of this study was to identify the mistakes that learners often make in a simulation of high-risk neonatal care and to utilize that information to produce video clips for debriefing. The case video-based debriefing on a simulation of high-risk neonatal care was intended to help nursing students feel psychologically safe and make their learning more effective. Next, this study examined the effects of case video-based debriefing on self-efficacy, critical thinking, state anxiety, and satisfaction with practice. In addition, this study explored students’ learning experiences during case video-based debriefing.The research hypotheses were as follows: first, self-efficacy would increase to a greater extent in the experimental group than in the control group; second, critical thinking would increase to a greater extent in the experimental group than in the control group; third, state anxiety would show a greater decrease in the experimental group than in the control group; and fourth, satisfaction with practice would increase to a greater extent in the experimental group than in the control group.This was a mixed-methods study consisting of a survey for quantitative data and an in-depth interview for qualitative data. To evaluate the effects of case video-based debriefing, a quasi-experimental study was conducted with a nonequivalent control group pretest–posttest design at one university in South Korea Fig. . This stStudents at a nursing college in South Korea were recruited through convenience sampling. Nursing students who voluntarily consented to take part in the study in writing were included from August to December 2022. To develop case video-based debriefing, the inclusion criterion was having experienced a simulation of high-risk neonatal care, and the exclusion criteria were refusing to participate in the study or not having experienced a simulation of high-risk neonatal care. To evaluate the case video-based debriefing, the inclusion criterion was participating in a 45-h, one-credit neonatal intensive care unit (NICU) practicum, and the exclusion criteria were refusing to participate in the study or not completing a NICU practicum.The G*Power 3.1.9.7 program with repeated-measures analysis of variance (ANOVA) and within-between interactions was used to calculate the number of participants. An effect size of 0.25, which was based on previous research . Taking The nursing students were divided into eight teams, consisting of 6–7 members, and one team per week participated in the NICU practicum. To prevent the experimental effect from spreading, students who practiced in the NICU during the first half of the practicum were put into the control group, while students who practiced in the NICU during the second half were allocated to the experimental group.Case video-based debriefing on a simulation of high-risk neonatal care was developed using the analysis, design, development, implementation, and evaluation (ADDIE) model .A questionnaire survey and focus group interviews were conducted among 27 nursing students who had previously participated in simulations of high-risk neonatal care. In the open-ended questionnaire survey, the participants were asked to describe what they did wrong (situations where they made mistakes), difficult situations, most memorable situations, most important points, and most memorable debriefing experiences in simulations of high-risk neonatal care. Five focus groups consisting of 5–6 participants each were formed, and one interview was conducted per group. The focus group interviews were conducted in a small conference room at convenient times for the participants. They were asked about the same topics covered in the questionnaire. With prior consent, the interviews were recorded and transcribed verbatim immediately after interview completion.Based on the results of the open-ended questionnaire survey and the contents of the focus group interviews, a case video scenario for debriefing was created. The scenario was developed based on cases in which nursing students made many mistakes, cases that they found difficult, and cases that they considered important. The scenario was completed through a validity test conducted by one nursing professor with experience in operating simulations of high-risk neonatal care, one nurse with more than 5 years of NICU work experience, and two nursing students with experience in simulations of high-risk neonatal care Table .Table 1Based on the completed scenario, a video of about 5 min for each topic was produced. The produced case video was completed after validity testing conducted by one nursing professor with experience in operating simulations of high-risk neonatal care, one nurse with more than 5 years of NICU work experience, and two nursing students with experience in participating in simulations of high-risk neonatal care, who also verified the validity of the scenario.From September to October 2022, verbal debriefing was performed for the control group, in which the instructor provided verbal feedback in the debriefing stage of a simulation of high-risk neonatal care, and from November to December 2022, debriefing was performed using the developed case videos for the experimental group. Debriefing was conducted by showing a case video where the experimental group made mistakes or incorrectly implemented the simulation, and an average of four case videos were used for each practice group. According to the clinical practice operation regulations, the same practice location, practice period, and practice guidance were applied to both the control group and the experimental group. The participants were not told which group they were assigned to.In both the control and experimental groups, general characteristics, self-efficacy, critical thinking, state anxiety, and satisfaction with practice were investigated 1–2 days before the start of NICU practice and on the last day of NICU practice after the practice was completed. The time required for the survey was approximately 20 min. The researchers did not directly conduct the pre- and post-surveys and were blinded, since questionnaires were distributed by the head of the practice group and participants were requested to provide self-reported answers. Each questionnaire had a unique number that the study participants were asked to remember when filling out the pre-survey questionnaire, and they were asked to fill out the post-survey questionnaire with the same unique number. If there were missing answers in the questionnaire, the head of the practice group asked study participants who remembered their unique numbers to answer the corresponding questions again; thus, there were no questionnaires with missing answers.The experimental group completed the post-survey questionnaire on the last day of NICU practice and participated in focus group interviews. One researcher who did not participate in conducting the simulation formed focus groups, each of which consisted of 6–7 members of the practice team, and conducted interviews in a small conference room. The participants were asked to discuss the positive and negative points of their debriefing experience using case videos for the simulation of high-risk neonatal care and differences from previous experiences of verbal debriefing. With prior consent, the interviews were recorded and transcribed verbatim immediately after interview completion.All instruments were designed as self-reported surveys and were utilized only after the original authors’ agreement.Self-efficacy was measured using the general self-efficacy scale for adults, which was developed by . On a 6-Critical thinking was measured using the critical thinking disposition scale, which was developed by Yoon . On a 5-State anxiety was measured using the state anxiety scale of the state-trait anxiety inventory, which was developed by Spielberger  and tranSatisfaction with practice was measured using the scale developed by Yoo , which wThe quantitative data were analyzed using SPSS version 25.0 . The chi-square test and the unpaired t-test were used to assess baseline homogeneity between the experimental and control groups. Repeated-measures ANOVA was used to examine changes in outcome variables based on the intervention.The qualitative data were analyzed using the inductive approach of content analysis suggested by Elo and Kyngas . During There were no significant differences in general characteristics between the experimental and control groups, including age, gender, academic performance, health status, and satisfaction with university life. There were also no significant differences between the experimental and control groups in self-efficacy, critical thinking, state anxiety, or satisfaction with practice Table .Table 2p = 0.044). However, there was no significant difference between the groups according to time , nor was there a difference according to time alone   Table . In othep = 0.447). However, significant differences were found according to time  and between the groups according to time  , but there was no significant difference between the groups according to time , nor was there a difference by group alone   Table , In othep = 0.033), as well as by time  and according to both group and time  “Participants said that after finishing the simulation in a tense state, they did not remember exactly what they and the members of the group did during the simulation. They said that by watching the case video in the debriefing stage, they clearly remembered what they did in the simulation. They were able to have detailed discussions and receive feedback based on accurate memories of the simulations they experienced and they could remember the contents clearly.I was so nervous during the simulation that I couldn’t remember a thing after it was over, but after watching the case video, I remembered it again, so it was nice. Also, while watching the case video, the professor pointed out my mistake, and I was able to talk with the professor and team members about how to act in the future, so it was more memorable.” (Participant 11)“Participants were more immersed in learning because the environment and situation in the case video matched the environment and situation when they performed the simulation.The environment in the video and the environment in the simulation were the same, and the mistakes shown in the video were the same as the mistakes we made. That’s why I was able to empathize tremendously and was able to immerse myself during the debriefing.” (Participant 19)“Participants were able to identify various mistakes they could have made through case videos. They had an opportunity to learn to prevent future mistakes.I could make a mistake like that at any time. While watching the video, I thought, ‘I can make mistakes like this’ and ‘I can make mistakes like that.’ In the simulation, I could see what I should be careful about in the future.” (Participant 25)“Participants confirmed in detail where they made a mistake through case videos produced for various mistakes that could occur in the simulation. They also realized from the case videos that there was a solution other than how they solved the problem.I learned that doing that is a mistake, and I also learned that problems can be solved differently.” (Participant 2)“Participants were nervous and embarrassed when they were evaluated by instructors and team members on what they did well or wrong while watching the video of their simulation during debriefing in other subjects. However, in this simulation, they were able to immerse themselves in learning comfortably as debriefing proceeded while watching a case video of others who took their roles.I was so embarrassed when I watched the video with other people, but I was relieved because I wasn’t appearing in this video.” (Participant 3)“During the debriefing, participants said that if they pointed out another peer’s mistakes, they did not express their opinions actively, fearing that the pointed-out peer would be upset. However, in this debriefing using case videos in which they and their peers did not appear, they were able to express their opinions freely without the need to worry about their peers’ feelings.It was hard to point out my friend’s mistake while watching their video. If I point out my friend’s mistake, they might feel bad, so I didn’t say anything. But this time, I was able to talk comfortably about their mistakes with them.“ (Participant 1)“Participants were able to focus on the mistake itself, not the person who made the mistake, in the debriefing through case videos featuring other people replacing their roles. As a result, they were able to accept feedback comfortably without emotional exhaustion.The focus was on what went wrong, not who among us was at fault, so we were comfortable taking feedback.” (Participant 21)“Participants said that it was difficult to concentrate on case videos that contained situations that did not exactly match the situations in which they made mistakes, so they did not clearly remember them. In addition, they were disappointed that the debriefing included discussion and feedback about their mistakes that were not included in the case videos.It wasn’t a video that could point out exactly what we did wrong, so it wasn’t memorable.” (Participant 25)“It was a pity that we didn’t have video materials for all the actions we did wrong.” (Participant 20)“Participants were able to concentrate more on the debriefing through the case videos, and hoped that more diverse case videos would be developed.I lost my immersion when there were discussions or feedback without videos. It would be nice if there were more examples of videos.” (Participant 23)“In this study, we created case videos for debriefing based on the results of an open-ended questionnaire survey and focus group interviews among nursing students. The topics of the case videos were newborn assessment methods, interpretation of results, and basic assessment data, including aseptic techniques. Practical educational materials were created after analyzing the needs of nursing students, who were the intended audience of the materials. A study on educational needs for practicing neonatal intensive care among nursing students  also demThe experimental group that participated in the case video-based debriefing developed in this study showed a greater improvement in critical thinking than was observed in the control group. In the focus group interviews with the experimental group, participants reported that they were able to understand nursing activities more easily and accurately by watching case videos and that they were able to engage in detailed discussions and feedback because they remembered their own behavior. A previous study  also repSatisfaction with practice also showed a greater increase in the experimental group than in the control group. Based on the focus group interviews with the experimental group, case video-based debriefing created a safe learning atmosphere. Conducting the debriefing while watching one’s own and colleagues’ simulation made students nervous about being evaluated by their instructors and peers and put them in the stressful social situation of having to evaluate their peers. This is consistent with a previous report  where thHowever, self-efficacy and state anxiety were not found to differ over time between the experimental group and the control group. The self-efficacy of the control group did not change in the pre- and post-survey, and the self-efficacy of the experimental group increased in the post-survey compared to the pre-survey, but not to a significant extent. Self-efficacy refers to the belief that one can solve a problem by taking appropriate action in a specific situation . ConsideIn addition, there was no significant difference in state anxiety over time between the two groups, since state anxiety decreased from the pre-survey to the post-survey in both the experimental group and the control group. Nursing students who are about to take a practicum and simulation often experience anxiety, worrying about whether they will be able to do their job well without making mistakes. As state anxiety measures the present anxiety felt in a specific situation , it tendIn summary, this study developed case video-based debriefing materials for a simulation of high-risk neonatal care, and the experimental group took part in case video-based debriefing. Case video-based debriefing on a high-risk neonatal care simulation effectively improved nursing students’ critical thinking and satisfaction with practice. The use of case video-based debriefing will help nursing students become more proficient at providing high-risk neonatal care.Nonetheless, there are several limitations to this study. First, nursing students from a single nursing college were recruited through convenience sampling, so its findings should be interpreted with caution. Second, in order to prevent the spread of the experimental effect, the experimental group and the control group were assigned according to the practice period; thus, random assignment was not performed. There were no significant differences in homogeneity testing of the general characteristics and dependent variables of the experimental group and the control group before the intervention, but a randomized controlled study is needed in the future. Third, despite our efforts to produce videos that matched the simulation situation, the video content had certain limitations. In particular, participants reported that it was difficult to concentrate on videos that did not exactly match their behavior. In the future, it will be necessary to develop videos of a broader range of cases and improve the video data. Fourth, only one posttest in this study was administered following the intervention, and no follow-up tests were administered to evaluate the long-term effect of case video-based debriefing. In the future, follow-up studies should be carried out to confirm the intervention’s long-term effects.This study developed case video-based debriefing materials for a simulation of high-risk neonatal care for nursing students and assessed its effects. The results revealed that case video-based debriefing effectively improved students’ critical thinking and satisfaction with practice. The application of case video-based debriefing to a simulation improved the learning effect for nursing students by providing a psychologically safe learning environment. Case video-based debriefing will be utilized to improve nursing students’ competency in high-risk neonatal care.


Background: The thymus gland is a lymphoid organ normally located in the anterior mediastinum. Location abnormalities of the thymus, such as ectopic thymus or the superior herniation of a mediastinal thymus, could be responsible for the occurrence of cervical masses in pediatric patients, raising concerns among clinicians. The knowledge of these conditions is essential for a thorough differential diagnosis and for preventing unnecessary invasive procedures. Methods: Descriptive retrospective series of three patients with a cervical mass, that was later diagnosed as ectopic thymic tissue. Results: The thymus has a unique and distinctive ultrasound appearance that is the keystone to detecting thymic parenchyma in locations other than anterior mediastinum. In selected patients, an accurate ultrasound could be conclusive for the diagnosis, with no need for further and potentially risky procedures. Conclusions: This case series supports the use of ultrasound in both diagnosis and follow-up of thymus location abnormalities, advocating a minimal invasive and conservative approach. The thymus gland is a lymphoid organ usually located in the anterior mediastinum. It is a paired organ that is recognizable early in the 6th week of fetal life. The thymus originates embryologically from the ventral wings of the third pharyngeal pouch on each side. During the 8th week, each thymic primordium elongates caudally and medially and forms the thymopharyngeal duct. As the primordial thymus descends into its final position within the anterior superior mediastinum, the upper extremity of the thymus is drawn out and eventually disappears . The thyAmong thymus congenital anomalies, abnormal locations, such as ectopic cervical thymus (ECT) or the superior herniation of a normal mediastinal thymus, could be responsible for the occurrence of cervical masses in pediatric patients.Neck masses are common in pediatric population. Although in most cases they are benign conditions, physicians should be aware that 12% of neoplastic diseases in pediatric age are located in the head and neck .The knowledge of abnormal locations of thymic tissue is essential for a thorough differential diagnosis and for preventing unnecessary invasive procedures.Ultrasound and magnetic resonance imaging (MRI) are both useful for the diagnosis of abnormal locations of thymic tissue. While ultrasound exams are often rapid, well-tolerated and easy to perform in the majority of children, MRI examinations usually require deep sedation or general anesthesia at this age.We report three pediatric clinical cases of congenital thymus location abnormalities along with their clinical and radiologic features, showing that an accurate ultrasound could have been conclusive for the diagnosis, with no need for further investigations. This case series supports a conservative and minimal invasive approach in both diagnosis, management and follow-up of thymus location abnormalities. The study was performed in accordance with the World Medical Association Declaration of Helsinki, and patient confidentiality was protected. The specific approval of an Ethics Committee was not required for this noninterventional case series based on retrospective, anonymized data; nevertheless, informed consent was obtained from parents for publication.A 5-year-old previously healthy girl was admitted to our Pediatric Unit for a recent history of an anterior neck swelling appearing during coughing, that brought family and physician’s concerns. She had no history of fever, dysphagia, wheeze, stridor or other signs of respiratory distress. Her past clinical history was unremarkable. A painless, non-pulsatile, 4 cm × 4 cm swelling, protruding above the suprasternal notch during coughing, was found on clinical examination. The skin above the mass was preserved. No localized or generalized lymphadenopathy was found on physical examination. Blood tests were within normal limits, including complete blood cell count, liver and kidney function, markers of inflammation and the immunological panel. A static ultrasound of the neck was performed, without abnormal findings.An MRI of the neck, carried out under general anesthesia, showed an orthotopic thymus, located into the anterior mediastinum, without any mass effect. After the MRI, a dynamic ultrasonography was carried out during the Valsalva maneuver, showing the superior herniation of a mass with the sonographic appearance of the normal thymus. The mass consisted of a roughly oval structure, with multiple linear hyperechoic septa, and scattered hyperechoic foci, giving a characteristic “speckled” or “starry sky” appearance. The ultrasound examination showed the intermittent superior movement of the thymus into the cervical region, reaching the cervical trachea, without any buckling or displacement of adjacent structures .At clinical and sonographic 3-year follow-up the size of the swelling was decreased, due to the physiological involution of the thymus.A previously healthy 1-year-old child presented with a 10-day history of a palpable mass on his left side of the neck, with no other signs or symptoms. On clinical examination, the cervical mass appeared painless, tender to the touch, movable in the surrounding tissue. The remainder of the child’s general physical examination was unremarkable. Blood tests, including screening tests for immune function deficiency, were normal.The ultrasound showed a 2 × 3 cm hypoechoic mass with consensual vascular signal, located to the left side of the cervical trachea. It had a relatively homogeneous background echogenicity, with multiple linear hyperechoic septa .The child was admitted to our Pediatric Unit for further investigation. On clinical examination, the cervical mass appeared painless, tender to the touch, movable in the surrounding tissue. The remainder of the child’s general physical examination was unremarkable. Blood tests were within normal limits in relation to age. An MRI under anesthesia was carried out, showing an oval mass in the left side of the neck, just beneath the submandibular gland, without significant mass effect. The lesion appeared isointense to the thymus gland on all sequences and showed a slight enhancement in the central area, being compatible with ectopic thymus gland. T2-weighted scans showed a well-demarcated quadrilateral-shaped lesion with homogeneous intermediate signal intensity, consistent with ECT in the left latero-cervical region. On Short TI Inversion Recovery (STIR) MRI sequences, the mass appeared isointense to the mediastinal thymus gland .Given the benignity of this condition and the physiologic involution of the thymus, no further investigation or therapy were needed. An ultrasound and clinical follow-up was started 6 months and 1 year later, respectively, documenting a progressive shrinkage of the lesion.A 11-month-old toddler was referred to our Pediatric Emergency Unit for a one-week history of rhinitis and two episodes of vomiting. His prior clinical history was unremarkable. On clinical examination an acute upper respiratory infection was diagnosed, with palpable reactive bilateral cervical lymph nodes. During clinical examination a swelling located on the left submandibular area was found and an enlargement of the left submandibular gland was suspected. The swelling was painless, soft, non-pulsatile and non-fluctuating without any signs of inflammation. Blood tests were normal, showing no increase of inflammatory markers nor immunological abnormalities. A soft-tissue ultrasound of the mass was performed, showing an enlargement of the left submandibular gland, reactive lymph nodes in the left cervical area and into the parotid parenchyma, and a 3.5 × 1.7 cm hypoechoic irregular lesion, with intralesional vascular signs and punctate and linear echogenic foci resulting in a “speckled” or “salt and pepper” pattern a.The echo pattern of this mass was identical to that of the normal thymus located in the superior mediastinum, thus the diagnosis of ECT was suggested. Additionally, the ultrasound showed a portion of thymic tissue extending into deeper layers, a probable remnant of its migration during embryogenesis, aiding in its identification b.The ultrasound examination was effective in making a prompt and confident diagnosis of an abnormal thymus location, without the need of further and potentially risky investigations. A radiological and clinical follow-up was started. At the 11-month follow-up, far from the acute upper respiratory infection, a slight reduction in size was observed, without any symptoms .This paper describes three pediatric patients with cervical mass due to abnormal thymus locations, highlighting the benignity of these congenital anomalies, particularly given the physiologic involution of the thymus that makes them transient.The thymus is physiologically located at the level of the anterior mediastinum, behind the sternum, anterior to the aorta and jugular artery and inferior to the sternocleidomastoid muscle. Anomalies during embryogenesis or migration could be responsible for the occurrence of location thymus abnormalities, such as ECT, intrathyroidal ectopic thymus or the superior herniation of a normal mediastinal thymus.The ECT is usually located along a line extending from the angle of the mandible to the manubrium sterni , althougMost cases of ECT are diagnosed between 2 and 13 years of age, because the thymic parenchyma exhibits hyperplasia during the first decade of life or following infection or vaccination, making ECT more evident during early childhood, followed by a gradual regression due to physiologic involution of the thymus . AccordiAnother rare and peculiar manifestation of thymus location abnormalities is its herniation into the neck from the anterior mediastinum. A transient swelling above the suprasternal notch appears when an increase in intrathoracic pressure occurs, such as during straining, crying or, as in our Case 1, coughing . The dynIn most of cases, thymus location abnormalities are asymptomatic. When the child presents with symptoms suggesting a mass effect, such as stridor, dyspnea and dysphagia, clinicians should suspect a more threatening condition, needing further investigations.The ultrasound has a pivotal role in the assessment of thymus location anomalies. In fact, the peculiarity of its echotexture is essential to detect thymic parenchyma in locations other than anterior mediastinum . On ultrWhile normal mediastinal thymus could be susceptible to shape modification due to respiratory or cardiac contractions, ECT tends to maintain its own shape. Moreover, in ECT or intrathyroid thymus ultrasound can occasionally show a line of thymic tissue connecting it to the orthotopic thymus or simply a probable remnant of its migration during embryogenesis, aiding its identification.Other imaging studies including computed tomography (CT) or MRI are often performed in pediatric patients with cervical masses, even though in selected cases an accurate ultrasound can be sufficient for the diagnosis. When the typical thymic “starry sky” appearance is detected on ultrasound, no signs or symptoms consistent with mass effect are reported and when the mass does not show a rapid dimensional growth, additional, and potentially risky procedures  or surgical interventions, such as the mass excision to perform a histological examination, may be unnecessary. A strict clinical and radiological follow-up is recommended, to document the mass reduction, due to the physiologic involution of the thymus.In the past, a fine needle aspiration biopsy (FNAB) or surgical excision were commonly performed to confirm the diagnosis ,17. In mMoreover, indications for excisional surgery may be the evidence or the clinical suspicion of a mass effect (suggested by imaging or symptoms such as dyspnea or dysphonia) and the rapid dimensional growth of the lesion. Since thymus is a soft, pliable organ, that changes its shape in response to respiratory and cardiac motion, the evidence of compression of adjacent structures should raise the suspect of hyperplasia or malignancy, requiring a surgical approach .However, thymectomy may be responsible for immunodeficiencies because of a dysregulation in the T-cell lines. For this reason, a preoperative MRI is recommended in order to confirm the presence of at least minimal remnants of thymic tissue in the mediastinum. In addition, the possible severe complications of the surgical approach, such as the rupture of the innominate artery, recurrent laryngeal nerve injuries or damage of adjacent structures in the upper mediastinum, should be considered before surgery .In conclusion, this case series focuses on abnormal locations of thymic tissue that can be an uncommon cause of neck masses in pediatric patients. Hence, these conditions, including ECT, intrathyroidal ectopic thymus and the superior herniation of a normal mediastinal thymus, should be considered in the differential diagnoses in children evaluated for a cervical mass. Given their benignity and the peculiar ultrasonographic appearance of thymic tissue, in selected cases our paper supports a conservative and minimal invasive approach in both diagnosis and management of these conditions. When the typical thymic “starry sky” appearance is detected on ultrasonography, no signs or symptoms consistent with mass effect are reported and when the mass does not show a rapid dimensional growth, ultrasound should be preferred for the diagnosis and follow-up of these patients over other imaging studies, which may require general anesthesia or cause radiation exposure.We highlighted these benign uncommon conditions in Pediatrics to be promptly recognized by health care providers to avoid additional investigations, family concern and unnecessary treatments.


NF1 and a variable number of flanking genes in the 17q11.2 region. This syndrome is characterized by more severe symptoms than those shown by patients with intragenic NF1 mutation and by variable expressivity, which is not fully explained by the haploinsufficiency of the genes included in the deletions. We here reevaluate an 8-year-old NF1 patient, who carries an atypical deletion generating the RNF135-SUZ12 chimeric gene, previously described when he was 3 years old. As the patient has developed multiple cutaneous/subcutaneous neurofibromas over the past 5 years, we hypothesized a role of RNF135-SUZ12 chimeric gene in the onset of the patient’s tumor phenotype. Interestingly, SUZ12 is generally lost or disrupted in NF1 microdeletion syndrome and frequently associated to cancer as RNF135. Expression analysis confirmed the presence of the chimeric gene transcript and revealed hypo-expression of five out of the seven analyzed target genes of the polycomb repressive complex 2 (PRC2), to which SUZ12 belongs, in the patient’s peripheral blood, indicating a higher transcriptional repression activity mediated by PRC2. Furthermore, decreased expression of tumor suppressor gene TP53, which is targeted by RNF135, was detected. These results suggest that RNF135-SUZ12 chimera may acquire a gain of function, compared with SUZ12 wild type in the PRC2 complex, and a loss of function relative to RNF135 wild type. Both events may have a role in the early onset of the patient’s neurofibromas.Neurofibromatosis type I (NF1) microdeletion syndrome, accounting for 5–11% of NF1 patients, is caused by the heterozygous deletion of The online version contains supplementary material available at 10.1007/s10048-023-00718-8. NF1 gene and frequently associated with a severe manifestation of neurofibromatosis type 1 [RNF135-SUZ12 gene maintaining an open reading frame in the transcript, that is abundantly expressed in blood. Interestingly, RNF135 is a regulator of several tumor suppressor genes [SUZ12 is a component of the polycomb repressive complex 2 (PRC2) that has an important role during the organism development and, at cellular level, contributes to maintain the cell identity [NF1 microdeletion syndrome MIM#613675) is a clinical condition accounting for 5–11% of NF1 patients [s type 1 . On the 675 is a s type 1 , 5, occuor genes . SUZ12 iidentity . FurtherAll clinical data reported were collected during the periodical follow-up of patient 171 [EZH2 and EED), two RNF135 target genes (TP53 and PTEN), and seven PRC2 target genes , in addition to the RNF135-SUZ12 and wild-type transcripts, were selected for the expression analysis, where the 2−ΔCt method was applied, using the TBP gene as housekeeping control for normalization. The specific oligonucleotides for the qPCR assays are shown in Supplementary Table Total RNA (500 ng), extracted from patient’s and controls’ peripheral blood according to standard procedures, was reverse-transcribed by the Maxima™ H Minus cDNA synthesis master mix with dsDNase . Two PRC2 components  method and the results were considered statistically significant when BH-adjusted p < 0.05.For each gene analyzed, the mean and the standard deviation were calculated in the patient’s biological triplicate and in the group of healthy controls, which included ten wild-type subjects. The independent samples Student’s Patient 171 is male and 8 years 4 months old, previously reported when he was 3 years old –12 but hRNF135-SUZ12 transcript, already identified in patient 171 when he was 3 years old, and to evaluate its current expression levels, in addition to those of the wild-type RNF135 and SUZ12 transcripts, we carried out quantitative RT-PCR assays on the RNA extracted from the patient’s peripheral blood. The qPCR assays revealed, in patient 171, the specific expression of the chimeric RNF135-SUZ12 transcript, which was expressed four times more than the wild-type RNF135 transcript and 80% less than the wild-type SUZ12 transcript , which derives from the RNF135 portion retained by the chimera that probably acquire an increased stabilizing function, thereby contributing to the expression deregulation of PRC2 target genes


Malnutrition is both a significant cause and a result of poverty and deprivation. In developing nations, child malnutrition is still the main public health issue. Severe malnutrition affects every system of the body and leads to medical instability. The assessment of the burden of severe malnutrition is important for ready-to-use therapeutic foods and preparing therapy for these conditions. Therefore, this study aimed to assess the prevalence and spatial distribution of severe malnutrition and the factors associated with it.Data from the 2019 Mini-EDHS  with stratified sampling techniques were used. The data were weighted using sample weight to restore the data's representativeness and provide accurate statistical estimations. A total of 5,006 weighted samples of children under the age of five were used to analyze the study. A multilevel binary logistic regression model was built, and a cutoff P-value of 0.05 was used. The wag staff normalized concentration index and curve as well as spatial analysis were used.P-value < 0.01).The prevalence of severe malnutrition practice among under five years children in Ethiopia was 14.89% , and ranges from 4.58% in Addis Ababa to 25.81% in the Afar region. Women with secondary and above education status as compared to uneducated , high community women's education as compared to low , women from richest household as compared to poorest  and living in Oromia region as compared to Tigray  were preventive factors. Whereas children 24–59 months of age as compared to under six months , and being multiple births as compared to single  have significant risk factors for severe malnutrition. There was a pro-poor distribution of severe malnutrition among under-five children in Ethiopia with a concentration index of -0.23 . Severe malnutrition has significant spatial variation over regions in the country where the entire Afar, Eastern Amhara, Southern, and eastern Tigray regions were severely affected .The spatial distribution of childhood severe malnutrition was not random. Regions like Tigray, Afar, Eastern parts of Amhara, and Somalia regions should be considered priority areas for nutritional interventions for reducing severe malnutrition. Equity-focused nutritional interventions could be needed to curb the wealth-related inequalities of childhood severe malnutrition. Malnutrition is a consequence of the consumption of dietary nutrients either insufficiently or exclusively by especially children . It causWorldwide, the prevalence of different forms of malnutrition such as stunting (height-for-age), wasting (weight-for-height), and underweight (weight-for-age) in under-five children are still high , 7. In 2Child malnutrition continues to be the leading public health problem in developing countries , 10. AfrEthiopia is one of the largest populated nations in sub-Saharan Africa and the country with the fifth-highest rate of under-five mortality worldwide in 2018 . In EthiSevere malnutrition is, defined as where the respective anthropometric measurements z-scores are below -3 SD . Childre0 -140 N and 330 – 480E with 1.1 million Sq. km coverage. It is the second most populous country in Africa and is federally decentralized into nine regions and two city administrations   of mothers of children were in the age group of 25–34 years with a median age of 28 (IQR: 8) years. More than half of women (53.72%) had no formal education. Moreover, half (52.06%) of children were delivered at home.Nearly three-fifths (59.83%) of the children were found in the age group from 24–59 months with a median age of 28 (IQR: 30) months. Almost all (97.82%) of the child are single birth. Three-fourths (74.96%) of the children lived in rural and 40% of the children are from the Oromia region , and ranges from 4.58% in Addis Ababa to 25.81% in Afar. Of which, most of them were chronic severe malnutrition i.e. severe stunting (12.62%), others were severely underweight (7.19%) and severe wasting (1.16%) .Secondary and above-educated women were 83% less likely to have children with severe malnutrition than women with no formal education . Moreover, children who were from high-community education clusters were 46% less likely to have severe malnutrition than those from low-community women's education .The odds of having severe malnutrition among children from not currently married mothers were 2.69 times higher as compared to a child from a married one .Children who come from the richest wealth family were 37% less likely to have severe malnutrition than the poorest family .Being multiple births and having two under-five children in the family were 5 and 2 times more likely to have severe malnutrition than those who were singleton birth and single child  and  respectively.The odds of severe malnutrition among children aged 6–23 and 24–59 months were 1.93 and 1.62 times higher than children who were found 0–5 months age  and  respectively. Children who live in the Oromia region were 66% less likely to have severe malnutrition than those who live in Tigray  (Table p < 0.01). Severe malnutrition had more prevalent in Afar, Amhara and Tigray regions and ranges from 36.37% to 57.89%  for severe malnutrition were most pronounced using 10 distance bands. Severe malnutrition is more common and hot spotted in Tigray, Afar, Eastern parts of Amhara, and Somalia regions, and ranges from 5.26% to 57.89% Fig. .Fig. 4HoP-value < 0.01) .The spatial distribution of childhood severe malnutrition was not random. Regions like Tigray, Afar, Eastern parts of Amhara, and Somalia regions should be considered priority areas for nutritional interventions for reducing severe malnutrition. Equity-focused nutritional interventions could be needed to curb the wealth-related inequalities of childhood severe malnutrition.Some of the strengths of this study include the use of recent nationally representative large sample data, the utilization of wealth-related inequalities, and the spatial distribution analysis. Due to the cross-sectional nature of the data, recall and social desirability bias may be present.