Purpose: Data derived by cardiac magnetic resonance (CMR) feature tracking suggest that not only left ventricular but also left atrial function is impaired in patients with acute myocarditis. Therefore, we investigated the diagnostic value of speckle tracking echocardiography of the left ventricle and left atrium in patients with acute myocarditis and normal left ventricular ejection fraction (LVEF). Methods and results: 30 patients with acute myocarditis confirmed by CMR according to the Lake Louise criteria and 20 healthy controls were analyzed including global longitudinal strain (GLS) and left atrial (LA) strain parameters. Although preserved LVEF was present in both groups, GLS was significantly lower in patients with acute myocarditis . Further diastolic dysfunction measured by E/e’ mean was significantly deteriorated in the myocarditis group compared to the control group . LA reservoir function  and LA conduit function  were significantly reduced in patients with acute myocarditis compared to healthy controls. Also left atrial stiffness index  as well as left atrial filling index  were deteriorated in patients with myocarditis compared to the control group. Conclusion: In patients with acute myocarditis and preserved LVEF not only GLS but also LA reservoir function, LA conduit function and left atrial stiffness index as well as left atrial filling index were impaired compared to healthy controls indicating ventricular diastolic dysfunction and elevated LV filling pressures. The contribution of the left atrium (LA) to cardiac hemodynamics comprises the modulation of left ventricular filling by the interaction of atrial reservoir, conduit and booster contractile function. The function of LA has been characterized by pressure-volume curves via invasive examination , 2.It is known that the LA reacts in a very sensitive way to ongoing volume and pressure overload secondary to elevated left ventricular filling pressures . More reNochioka et al. showed that in patients with cardiac amyloidosis all LA function phases including reservoir, conduit and booster function were impaired compared to healthy controls. The results highly correlated with LV deformation .Furthermore, Matsubara et al. compared 28 children with “multisystem inflammatory syndrome in children (MIS-C)” due to Coronavirus disease 2019 (COVID-19) infection with 20 healthy controls and 20 consecutive patients with Kawasaki disease (KD) . In patiIn a multicenter study including 322 patients with different cardiovascular diseases, Inoue et al. investigated LA strain and other echocardiographic parameters and compared them with invasively measured LV filling pressures . GLS andOnly scarce data exist concerning LA strain parameters in adult patients with acute myocarditis. Dick et al. investigated 30 patients with feature tracking by cardiac magnetic resonance (CMR) imaging. In this study, a decreased LA reservoir and conduit function in patients with acute myocarditis compared to healthy controls was detected while LA booster pump function was preserved .Doerner et al. analyzed 86 patients with acute myocarditis and 30 healthy controls with feature tracking derived strain analysis by CMR. LA conduit function was reduced in patients with acute myocarditis compared to healthy controls. Furthermore, LA conduit function represented one of the best independent predictors of myocarditis in patients with preserved LV function .These data derived by CMR feature tracking suggest that not only left ventricular but also left atrial function is impaired in patients with acute myocarditis. However, data on echocardiographic left atrial strain analysis in patients with acute myocarditis are lacking. Therefore, we investigated the diagnostic value of speckle tracking echocardiography of the left ventricle and left atrium in patients with acute myocarditis and normal left ventricular ejection fraction (LVEF).As described in a previous study of our working group we retrospectively identified 31 patients with acute myocarditis and preserved LVEF admitted to the University Hospital Regensburg between November 2015 and November 2019 . The diaCMR and transthoracic echocardiography (TTE) were conducted in the acute phase of myocarditis. Follow up TTE were performed after a median of 14 weeks (10–24). Complete follow-ups were present in 20 cases. Patients with a history of severe symptoms e.g. sudden cardiac death, heart failure or reduced LVEF were excluded. In addition, patients aged < 18 years and patients with poor TTE image quality were not included. Clinical data and blood samples of all participants were analyzed. The study was approved by the local ethics committee.20 healthy age- and gender-matched subjects were recruited to serve as a control group.The control group did not have a history of myocarditis and other cardiac diseases. All volunteers gave written informed consent and were investigated by complete TTE.No external funding was obtained to support the study .As previously described TTE was Speckle tracking analysis was performed on a frame-by-frame basis by automatic tracking of acoustic markers (speckles) throughout the cardiac cycle. The duration of systole was defined in the apical three-chamber view by marking the aortic valve opening and closure. The myocardial borders were traced from the apical four-, two- and three-chamber views in the end-systolic frame of the 2D images in order to analyze global longitudinal strain (GLS).In accordance with the consensus document of the EACVI/ASE/Industry the region of interest (ROI) was defined by the endocardial border which symbolizes the inner contour of the myocardium . If necePeak systolic strain was determined as the maximum value of the peak negative strain during systole. By assessing the peak systolic longitudinal strain in all 17 longitudinal segments, global longitudinal strain was provided by the software as the average value of the different segments.LA volumes and areas were received by measurements in apical four- and two-chamber views. As commonly described the pulmonary veins, LA appendage and mitral leaflets were excluded for LA analysis . In ordeAs previously described CMR was p-value of < 0.05 was considered statistically significant.Statistical analysis was performed using SPSS Version 25 . Categorical data are presented as absolute and relative frequencies whereas continuous variables with normal distribution are presented as mean ± standard deviation, variables with skewed distribution as median . Differences between continuous variables in paired data were tested with a paired t-test, continuous variables in unpaired data were compared with an unpaired t-test. Differences in nominal variables and dichotomous variables in unpaired data were evaluated by Pearson’s chi-squared test and Fisher’s exact test, respectively. A 2 vs. 25.9 ± 3.6 kg/m2, p = 0.021). 83.3% of patients with acute myocarditis had preceding signs of infection and in 76.7% ECG abnormalities were present. Coronary angiography was performed in 50% of patients (n = 15) with myocarditis and obstructive coronary artery disease was excluded in all patients. In 10 cases LV filling pressures were measured invasively. The median LV filling pressure was 22 ± 6 mmHg, indicating elevated LV filling pressures.Baseline characteristics of the study population are depicted in Table Peak troponin and creatine kinase levels were selected to present the maximum extent of myocardial injury. Median peak troponin level was 8466 pg/ml  and median maximum C reactive protein (CRP) was 35.3 mg/l (10.0–86.0) in patients with acute myocarditis.Ejection fraction was preserved in the myocarditis group (LVEF 58 ± 4%) as well as in the control group  compared to the control group . No differences concerning indexed left atrial volumes (LAVI) or LA area were revealed. However, LA reservoir function was impaired in patients with myocarditis compared to healthy controls  associated with COVID-19 and compared the results to healthy children and children with Kawasaki disease . The besConsidering the results of our study, we speculate that the extent of myocardial injury may correlate with the persistence of diastolic dysfunction as a marker of structural changes. Further studies are needed to investigate this relationship.Concerning GLS, Caspar et al. detected myocardial dysfunction by 2D and 3D speckle tracking analysis in patients late after an acute episode of myocarditis and preserved LV function. Mean delay between the diagnosis of acute myocarditis and follow-up echocardiography was 21.7 ± 23.4 months . In the Stöbe et al. revealed myocardial injury in a relatively small cohort of patients with SARS-CoV-2 infection (severe acute respiratory syndrome-coronavirus-2) even in patients with mild symptoms . DespiteIn contrast to GLS limited but growing data exist concerning left atrial strain imaging. Therefore deformation analysis by speckle tracking echocardiography of the left ventricle as well as the left atrium should be added to routine diagnostics in patients with suspected acute myocarditis. These parameters should also be applied at the time of follow-up visits. The importance of speckle tracking echocardiography is even more pronounced in institutions with limited access to CMR.Some limitations of the present study warrant consideration:Firstly -as already mentioned in a previous study- our stuIn patients with acute myocarditis and preserved LVEF, not only GLS but also LA reservoir function, LA conduit function and left atrial stiffness index as well as LA filling index were impaired compared to healthy controls, indicating ventricular diastolic dysfunction and elevated LV filling pressures. Due to growing evidence left atrial and left ventricular strain imaging should be added to routine diagnostics in patients with suspected acute myocarditis.


The electrocardiogram (ECG) signal is prone to distortions from gradient and radiofrequency interference and the magnetohydrodynamic effect during cardiovascular magnetic resonance imaging (CMR). Although Pilot Tone Cardiac (PTC) triggering has the potential to overcome these limitations, effectiveness across various CMR techniques has yet to be established.To evaluate the performance of PTC triggering in a comprehensive CMR exam.Fifteen volunteers and twenty patients were recruited at two centers. ECG triggered images were collected for comparison in a subset of sequences. The PTC trigger accuracy was evaluated against ECG in cine acquisitions. Two experienced readers scored image quality in PTC-triggered cine, late gadolinium enhancement (LGE), and T1- and T2-weighted dark-blood turbo spin echo (DB-TSE) images. Quantitative cardiac function, flow, and parametric mapping values obtained using PTC and ECG triggered sequences were compared.Breath-held segmented cine used for trigger timing analysis was collected in 15 volunteers and 14 patients. PTC calibration failed in three volunteers and one patient; ECG trigger recording failed in one patient. Out of 1987 total heartbeats, three mismatched trigger PTC-ECG pairs were found. Image quality scores showed no significant difference between PTC and ECG triggering. There was no significant difference found in quantitative measurements in volunteers. In patients, the only significant difference was found in post-contrast T1 (p = 0.04). ICC showed moderate to excellent agreement in all measurements.PTC performance was equivalent to ECG in terms of triggering consistency, image quality, and quantitative image measurements across multiple CMR applications. Synchronization of cardiovascular magnetic resonance (CMR) data acquisition to cardiac motion is typically required to obtain artifact-free images of the heart and flowing blood. Conventionally, the R-wave of the electrocardiogram (ECG) is used as a trigger signal to synchronize data acquisition with the cardiac cycle. However, ECG setup can be time consuming; skin preparation is required and electrodes may need to be repositioned several times to obtain an adequate signal, thereby adding to the overall exam time . The ECGThe Pilot Tone (PT) technique was proposed as a novel method to synchronize CMR acquisition with cardiac and respiratory motion . The PT Fifteen healthy volunteers  and 20 cardiac patients  were recruited from two CMR centers . The study was approved by the respective local institutional review boards and informed written consent was obtained from all subjects. The exclusion criteria for volunteers included: 1) under 18 years of age, 2) pregnant, 3) claustrophobic, and 4) standard contraindications to CMR. All cardiac patients were referred for CMR by local physicians. Demographic information for volunteers and patients is included in Supplementary Material 1.Raw PT signals were acquired using the imaging receiver coils, simultaneous with image acquisition, and sampled at a rate of 2000 Hz. Detuning of the receiver coils during RF transmission, as is normally performed to avoid damage to the receiver system, induced a spike-like artifact in the PT signal. To eliminate this interference and to extract cardiac motion, a previously described PTC signal-processing algorithm  was emplThe polarity of the PT cardiac surrogate was determined during calibration based on skewness. Under resting conditions, diastole is longer than systole, thus the skewness indicates the direction of diastole and can be used to define the polarity of the PTC. The maximum of the PTC signal acquired during the calibration scan was also stored, and the threshold for triggering set at 0.4 times this maximum. This threshold was set empirically as a compromise between reliably avoiding double triggers caused by the mid-diastolic “shoulder” of the signal, while simultaneously avoiding missing triggers due to low signal maxima. A diagram of the PTC calibration process is summarized in The delay between the PTC trigger and ECG R-wave can be estimated during calibration by assuming that the PT cardiac surrogate reaches its maximum simultaneous with the R-wave. The delay was calculated as the average of the times between the PT trigger points in the training data and their preceding PT cardiac surrogate local maxima, plus the processing latency induced by Kalman filter. This delay estimation was later used to automatically set the acquisition window for single-shot scans, which are typically timed to diastole.Data were collected on two clinical 3T systems  at two different centers. A 20-second PT calibration scan was performed on each subject before acquiring PTC triggered images. All breath-held scans were acquired at end expiration.At Site A, 15 healthy volunteers underwent a comprehensive, PTC triggered, non-contrast cardiac exam that included acquisition of cardiac localizer, dark-blood turbo spin echo (DB-TSE), cine covering the left and right ventricles, flow of the ascending aorta and the main pulmonary artery (MPA), and quantitative mapping images. Corresponding ECG triggered images for each application were also collected for comparison in these volunteers. Additionally, in six patients clinically referred for CMR at Site A, PTC triggered pre- and post-contrast T1 maps and late gadolinium enhancement (LGE) images were acquired along with corresponding ECG triggered scans. Scan parameters and slice positions were matched between PTC and ECG triggered sequences, although the repetition times (TR) of the PTC triggered scans were rounded up to the nearest 0.5 ms to match the PT sampling rate. Inversion time in PTC triggered LGE scans was set slightly longer relative to the ECG triggered scans to account for the longer time delay between contrast injection and image acquisition. A summary of sequences and scan parameters used at Site A are shown in Supplementary Material 2.At Site B, 14 cardiac patients clinically referred for CMR were scanned. All CMR sequences were PTC triggered, except for an additional set of ECG-triggered cine slices collected for comparison. The specific CMR sequences used on each patient varied depending on the clinical indication; these included sequences for localization, morphology, cine, flow, parametric mapping, early gadolinium enhancement (EGE), and LGE. A summary of sequences and scan parameters used at Site B are shown in Supplementary Material 3.ECG).PTC and ECG triggered, breath-hold segmented cine series were acquired in the majority of subjects, including 15 of the volunteers at Site A, and 14 of the patients at Site B. These data were used to evaluate PTC trigger accuracy. During each PTC triggered image acquisition, the ECG signal and triggers were recorded simultaneously by the scanner. PTC and ECG trigger timing were evaluated using MATLAB . The first heartbeat was not used to acquire image data; therefore, the first trigger was excluded from the analysis. PTC and its corresponding ECG triggers were identified. The R-wave based ECG trigger was assumed to always occur prior to the motion-based PTC trigger, as electrical activity of the heart precedes mechanical motion. Therefore, trigger delay was defined as the time elapsed from the ECG trigger to the PTC trigger within each heartbeat. Trigger jitter was defined as the standard deviation of the trigger delays across heartbeats. Both mean trigger delay and trigger jitter were calculated in milliseconds and as a fraction of average cardiac cycle measured by ECG ; these images came from subsets of volunteers and patients at each center, depending on the availability of PTC and ECG comparative data. Images from all cardiac views from a single subject, single application, and single triggering method were combined into a single display and presented in random order to the readers who were blinded to the triggering method. The scoring criteria ranging from 5: excellent, no apparent artifacts and/or blurring to 1: very poor, image totally obstructed by artifacts and/or blurring. In 6 patients from Site A with both ECG and PTC triggered LGE, the images were randomized and the global presence/absence of LGE was evaluated by a physician.Cardiac function and flow measurements were quantitatively evaluated using suiteHEART . Biventricular cardiac output (CO), stroke volume (SV), ejection fraction (EF), and end systolic and end diastolic volumes  were measured from cine images acquired in both volunteers (Site A) and patients (Site B). Aortic and MPA CO, SV, and peak velocities were measured from phase contrast images. Myocardial T1, T2, and T2* relaxation times were measured in the interventricular septum from parametric maps in volunteers, per SCMR guidelines . In patiThe Wilcoxon signed rank test with an alpha level of 0.05 was performed on the image quality scores to test for differences between the two triggering methods. Pairwise Student’s t-test with an alpha level of 0.05 was performed on the quantitative measurements to test for significant differences. The p-values of biventricular cardiac function measurements, aortic flow, MPA flow, and parametric mappings were adjusted separately using the Benjamini and Hochberg correction method  with a fAn example of PTC vs. ECG signal and triggers are shown in A summary of the image quality analysis is shown in In volunteers, quantitative global cardiac function results for PTC and ECG triggered cine, flow from 2D phase contrast, and myocardial T1, T2, and T2* values from parametric maps are shown in Biventricular cardiac function values from cine, and native and post contrast T1 and ECV values in patients are shown in This preliminary study assessed the feasibility of performing a comprehensive CMR exam with PTC triggering and compared its performance with ECG triggering. Although the PTC calibration process failed in some subjects, the PTC triggered acquisitions showed good agreement with ECG triggering in terms of consistency of trigger timing, image quality, and quantitative measurements. Complete CMR exams including a variety of applications were performed using PTC triggering in volunteers at Site A, and in patients at Site B. All patient studies were of diagnostic quality and the PTC triggered images were all read and reported without exception.A delay was observed in the PTC trigger with respect to ECG based R-wave triggering. This was expected as the PTC trigger is based on the mechanical contraction of the heart, which is preceded by the R-wave. The length of delay varied somewhat between subjects, potentially due to subtle differences in cardiac anatomy and function; this variation was also observed in other PTC studies , 25. TheAmong the 24 subjects with successful PTC calibration, only three trigger discrepancies were found between PTC and ECG, and two of these appeared to be due to premature ventricular contraction (PVC). Arrhythmias can be problematic for cardiac synchronization regardless of the triggering technique ; howeverAlthough there was no difference in cine, LGE, and DB-TSE image quality scores between the two triggering methods in terms of blurring and artifacts, it is worth noting that the PTC triggered cine and flow series started at mid-systole, rather than end diastole as is the case for ECG triggering. This timing difference did not affect quantification of global cardiac function or flow, as standard commercial software can typically choose the end-systolic and end-diastolic frames , regardlIn static imaging applications including parametric mapping, the trigger jitter was negligible compared to the duration of diastole and caused no significant difference in myocardial mapping values in volunteers. However, in patients the post contrast T1 values measured using PTC were found to be higher than those measured using ECG. Given that PTC images in this patient cohort were consistently obtained after conventional ECG images, the significance of this finding could largely be attributed to the difference in post-contrast injection timing.The PTC delay time was estimated during calibration and the trigger timing automatically adjusted accordingly. However, this process may not be effective for the dark-blood preparation pulse which is typically applied at end-diastole (the ECG R-wave) with data acquisition timed to late diastole of the same cardiac cycle ; a misma®. The elimination of the time associated with ECG lead placement and the challenges of ECG interference, especially at higher field, make the PTC a promising cardiac synchronization technique.The PTC requires a small loop antenna known as the PT generator; such a device has been embedded in the anterior body array coil of several current Siemens MRI systems operating at various field strengths. No hardware or software modifications are required to obtain PTC signals on these scanners. No additional patient preparation is required beyond positioning the coil so that the PT generator is placed directly above the heart. At the time of the study, PTC signal and triggering were detected using a research algorithm, which is now commercially available as Beat SensorWhile the potential advantages of PTC are substantial, this study has several limitations. The small sample size limits the strength of conclusions that can be drawn from a number of comparisons. LGE and parametric maps with both triggering methods were available in only six patients from one center; no definite conclusions should be drawn based on such a limited sample. It must also be noted that PTC calibration failed in several subjects. PTC signal polarity was inverted in three subjects with high heart rates, causing the PTC trigger time to be shifted to peak diastolic filling rather than peak systolic contraction, introducing additional delay relative to the R-wave. This difference in trigger timing did not impact cine, flow, and parametric mapping image analyses, since the delay was consistent from beat-to-beat. However, a more robust determination of PT signal polarity is desirable. Poor PTC performance in the subject with situs inversus may have been due to the position and orientation of the heart relative to the PT generator, and perhaps could have been corrected by repositioning the coil. Although the PTC algorithm has been improved since the time of this study, adjusting the coil position and recalibrating may still be required. If these steps do not improve PTC triggering, then falling back to conventional triggering methods should be considered.The current study included three patients with BMI over 40, and two patients with EF less than 35%, including one patient in whom both factors coexisted. No issues were observed in PTC triggered sequences in these patients. Although the patients in this study were referred for CMR with a variety of indications, further evaluation is needed in patients with a range of body habitus, cardiac orientations, and heart rates, and in various conditions that can alter cardiac motion and blood flow. The motion-derived PT signal could be attenuated in severely obese or small pediatric patients, or in patients with heart failure with reduced EF, or ventricular dyssynchrony. On the other hand, ECG triggering may be challenging in patients with conduction system disorders. Further investigation is required to compare the performance of PTC to ECG under the above circumstances.PTC triggering was successfully evaluated across a wide range of CMR applications in healthy volunteers and in cardiac patients at two unaffiliated imaging centers. PTC performance was compared with standard ECG triggering and found to provide accurate triggering as well as comparable image quality and quantitative results. PTC may offer a more efficient and effective method than ECG for CMR cardiac synchronization.


In this review, we summarize state-of-the-art artificial intelligence applications for non-invasive cardiovascular imaging modalities including CT, MRI, echocardiography, and nuclear myocardial perfusion imaging. In parallel, non-invasive imaging modalities, which have a key role in the diagnosis and management of cardiovascular disease, have seen rapid technological advancements over the past decade. With respect to nomenclature, AI refers to the use of computer systems to perform tasks that normally require human intelligence. Machine learning (ML) is a subdiscipline of AI which enables these computer systems to automatically learn to perform a task and improve from experience through exposure to large amounts of data.Deep learning (DL) is a specific type of ML which utilizes artificial neural networks to generate automated predictions directly from input data; in medical image analysis, the most commonly used DL networks are convolutional neural networks. Explainable AI refers to ML techniques wherein the results of the learning can be understood by humans, and aims to increase the transparency of ML models. In this paper, we summarize recent AI applications for non-invasive cardiovascular imaging modalities including cardiac CT, cardiac magnetic resonance (CMR), echocardiography, and nuclear myocardial perfusion imaging (MPI), and focus on the potential of AI for enhancing image analysis and risk stratification.Artificial intelligence (AI) applications continue to permeate into cardiovascular medicine, with the potential to increase workflow efficiency, improve cost effectiveness, and inform decision-making. In clinical practice, CAC scoring is performed by experts who manually identify high-density voxels in the coronary arteries. Rapid, automated, AI-enabled CAC scoring has the potential to reduce the workload of physicians and technicians and improve the clinical workflow. Wolterink et al used a random forest ML model to automatically delineate between coronary and non-coronary calcifications based on position, shape, size, and intensity features. The resultant ML-enabled CAC score showed strongly correlation with expert manual measurements (r = 0.94). Recently, several DL applications for automatic CAC segmentation and scoring have emerged. Zeleznik et al trained a U-Net-based convolutional neural network to quantify CAC scores in close correlation with the manual reference standard (r = 0.92) and at a speed of 1.94 s per scan. Notably, the DL-based CAC score had strong predictive value for future cardiovascular events in 20,084 individuals from distinct asymptomatic and chest pain cohorts, independent of clinical risk factors. Other investigators have demonstrated the feasibility of DL-based CAC scoring across a diverse range of cardiac and chest CT scan protocols, including coronary CT angiography, positron emission tomography CT attenuation correction scans, lung cancer screening CT, and radiotherapy planning CT in patients with breast cancer. This greatly expands the range of real-world CT scans in which the presence and extent of coronary artery disease (CAD) can be rapidly and accurately assessed, informing risk stratification and potential initiation of preventative therapy.Coronary artery calcium (CAC), a direct marker of coronary atherosclerosis and strong predictor of future cardiovascular events, can be readily assessed using non-contrast cardiac CT. Commandeur et al trained a DL algorithm for fully automated EAT quantification, and testing in a large multicenter study and yielded excellent correlation between DL and expert manual measurements . DL anp = 0.02). Serum biomarkers provided incremental prognostic value beyond clinical data and CT measures in the ML model . A major strength of this ML approach the in-built ability to explicitly describe the contribution of each variable to the final patient-specific prediction and provide explainable AI  and 46 clinical features for predicting cardiovascular mortality at 10 years. ML (AUC 0.85) outperformed both the atherosclerotic cardiovascular disease risk score alone (AUC 0.82) and CAC score alone  for outcome prediction.Metrics derived from non-contrast CAC scoring CT can also be input into ML models for risk stratification. In a prospective study of 1069 subjects with long-term follow-up, ML integration of clinical risk factors, CT imaging measures  and circulating biomarkers (area under the receiver operating characteristic curve [AUC] 0.81) outperformed the CAC score (AUC 0.75) and ASCVD risk score (AUC 0.74) alone  with manual expert measurements. Meanwhile, Choi et al leveraged a series of convolutional neural networks to categorize stenosis severity by CAD-RADS (Coronary Artery Disease–Reporting and Data System) in close agreement with expert consensus (κ = 0.72 per-vessel level and 0·81 per-patient level).Coronary computed tomography angiography (CCTA) is now a first-line modality for assessing luminal stenosis severity in stable chest pain patients. While this relies on visual estimation by clinicians, The DL system demonstrated good agreement with expert readers for quantitative measurements of percent diameter stenosis (intraclass correlation coefficient 0.88). Further, CAD-RADS categorization of stenosis by DL exhibited close agreement with clinician interpretation of CCTA (κ = 0.81 at the patient level)  and intravascular ultrasound (ICC 0.95)  for automated coronary plaque segmentation was developed and externally validated in an international multicenter study of 1196 patients (6946 lesions).t level)  and highCC 0.95) . Importask score . Such a sk score  and augm However, current CFD-based CT-FFR is computationally demanding and requires off-site analysis in core laboratories. To address these limitations, Itu et al developed a fully automated DL algorithm for the on-site calculation of CT-FFR, which exhibited a sensitivity of 82% and specificity of 85%, referenced by invasive FFR. The DL computation time per-patient was much more rapid than CFD (2.4 vs 196.3 s). This DL-based CT-FFR method was then validated in a multicenter study of 351 patients and 525 vessels, where it improved the diagnostic performance of CCTA and was comparable to conventional CFD-based CT-FFR. This DL application has the potential to facilitate CT-FFR calculation at a standard workstation at the point-of-care.AI techniques have also been applied for the functional assessment of coronary stenosis on CCTA. Non-invasive CCTA-derived fractional flow reserve (CT-FFR) is routinely performed in clinical practice with the aid of DL algorithms which delineate lumen boundaries and thus enable simulation of computational fluid dynamics (CFD). trained a ML classifier integrating 25 clinical variables and 44 visually assessed CCTA parameters of CAD extent, severity, and composition to predict of all-cause mortality at 5 years in 10,030 patients with suspected CAD. The calculated ML score (AUC 0.79) was superior to current CCTA segment scores (AUC 0.64) and the Framingham risk score  . According to feature important, the highest-ranked predictors in the ML model were the number of segments with non-calcified plaque and number of vessels with non-obstructive stenosis. ML models incorporating anatomical plaque measurements can also be used to assess the functional significance of coronary stenosis. In a substudy of the NXT  trial, Dey et al combined CCTA-derived quantitative plaque parameters using ML to compute a vessel-specific “Ischemia Risk Score”, which was the continuous probability of an abnormal invasive FFR (≤0.80). The resultant ML score had a higher AUC (0.84) than luminal stenosis severity  or a clinical risk score  for predicting an abnormal FFR. The plaque features with greatest contribution to ML preddfiction were non-calcified plaque volume and low-attenuation plaque volume   . used ML algorithms combined with marginal space learning—a 3D learning-based object detection method—to automatically generate individualized models of all four heart valves from 4DCT cine images, trained using expert reader manual annotations as ground truth. For the aortic and mitral valves, differences between ML and expert measurements of valve position and orientation only were slightly above the interreader variability. Meanwhile, Liang and colleagues showed how a ML algorithm using leaflet meshes could automatically reconstruct patient-specific 3D models of the aortic valve in close agreement with expert manual measurements .Cardiac CTA is now integrated into procedural planning for transcatheter valve replacement, enabling 3D native valve modeling of the native valve and device sizing. Grbic et al employed a regression-based ML algorithm to precisely localize eight different aortic valve landmarks at rapid speeds (≤12 ms) on pre-procedural planning CTA for transcatheter aortic valve replacement. In a hybrid imaging approach, Zheng et al utilized marginal space learning to automatically segment the aorta on rotational C-arm CT images, generating a 3D aorta model which was super-imposed on 2D fluoroscopic images in real-time during live transcatheter aortic valve replacement cases. The resultant patient-specific valve models were then used to assess the biomechanical interaction between the valve bioprosthesis and the surrounding tissue, which has the potential to facilitate device selection improve the likelihood of device success.Al et al showed a convolutional neural network to perform comparably to human experts for the segmentation of all four cardiac chambers on cine images, with a Dice coefficient  ranging from 0.90 to 0.96. Various DL-enabled fully automated segmentation algorithms have achieved excellent agreement with expert readers in performing measurements of left and right ventricular volumes and ejection fractions. Further, the scan-rescan reproducibility of DL-enabled cardiac structure and function measurements was recently demonstrated by Davies et al, whose U-Net-based convolutional neural network outperformed three clinicians  in a fraction of the analysis time (20 s vs 13 min). Importantly, the performance of DL for left ventricular segmentation can be affected by various disease states, with a recent study demonstrating stronger correlation between DL and manual measurements of ejection fraction in hypertrophic versus dilated cardiomyopathy (r = 0.94 vs 0.88)  . and hypertrophic cardiomyopathies. Fully automated DL-based techniques have also been developed to measure the burden of left atrial scar in patients with atrial fibrillation, potentially helping to inform patient selection for catheter ablation. Native T1 mapping on CMR enables the quantification of diffuse myocardial fibrosis, and a DL algorithm which automatically measures global and regional myocardial T1 values has demonstrated strong agreement with expert manual measurements.AI has also shown promise in CMR tissue characterization. Left ventricular scar volume on late gadolinium enhancement imaging is an important biomarker in risk stratification and procedural planning. DL convolutional neural networks can delineate left ventricular scar from normal myocardium using image-based features, enabling accurate quantification of scar volume in patients with ischemic used a random forest algorithm to show that among 725 clinical and imaging variables, CMR-derived left ventricular structure and function were highly ranked predictors of incident heart failure in 6814 subjects from the MultiEthnic Study of Atherosclerosis. The ML model prediction surpassed established risk scores and had higher prediction accuracy. In survivors of myocardial infarction, Kotu et al employed a ML model integrating CMR-derived scar characteristics such as size, location, and texture features to predict the occurrence of cardiac arrhythmias. This resultant AUC of 0.92 for ML was comparable to currently used criteria such as left ventricular ejection fraction. Finally, Bello et al trained a DL classifier using 3D models of right ventricular motion on CMR to accurately predict survival in patients with pulmonary hypertension; outperforming conventional statistical Cox models containing right ventricular volume and strain parameters.CMR biomarkers can also be incorporated into ML models to enhance risk prediction. Ambale-Venkatesh et al have been established and are well-primed for AI applications.With rapid image acquisition, relatively low cost, and lack of ionizing radiation, echocardiography remains the most widely used cardiovascular imaging modality. Large, well-structured and labeled data sets such as the National Echocardiography Database Australia detects cancer therapy-related cardiac dysfunction, acts as a gatekeeper for cardiac devices, and provides a surrogate endpoint in drug development and outcome prediction. Current human assessment of ejection fraction requires tracing of left ventricular end systolic and end diastolic volumes which can lead to considerable interobserver variability. There has been great interest in using DL techniques for echocardiography to determine ejection fraction. View classification, the essential first step in the process, was automated by Madani et al, who designed a convolutional neural network which could classify 15 standard views on transthoracic echocardiograms with 91.7% accuracy, compared with 70.2–84.0% for board-certified echocardiographers. Similarly, Zhang et al trained a DL network using 14,035 echocardiograms to identify 23 standard views and flag partially obscured cardiac chambers. Ghorbani et al developed a DL model for the prediction of left ventricular volumes and ejection fraction from sampled apical-four-chamber view images, achieving R2 scores of 0.70, 0.74, and 0.50 for end-diastolic volume, end-diastolic volume, and ejection fraction, respectively, compared to clinician measurements. However, these described DL approaches rely on manually curated still images at systole and diastole instead of using the actual echocardiogram videos. Ouyang et al proposed a video-based DL algorithm (EchoNet-Dynamic) for beat-to-beat estimations of left ventricular ejection fraction. Trained on 10,030 apical-four-chamber echocardiogram videos, the convolutional network identified each cardiac cycle, generated a clip of 32 frames, and averaged estimates of the ejection fraction for each beat. EchoNet-Dynamic achieved Dice coefficients of 0.93 and 0.90 for end-diastolic and end-systolic volumes, respectively, referenced by human experts. For the prediction of ejection fraction, the algorithm had an R2 of 0.81 and mean absolute error of 4.1% compared with expert annotations, and reliably classified heart failure with reduced ejection fraction (AUC of 0.97).A fundamental metric of cardiac function is left ventricular ejection fraction, which determines selection of drug therapy in heart failure, However, 3D quantification is time-consuming and requires expertise, limiting its integration into clinical practice. ML techniques can automate this process, providing rapid and reproducible measurements of left ventricular and left atrial volumes which are comparable to CMR reference values and generalizable to external centers and patient populations. Further tuning of these ML algorithms has enabled the calculation of LV mass and right ventricular volume and ejection fraction. Using AI-based software to analyze 3D echocardiography may improve workflow efficiency as more institutions adopt this technology.3D echocardiography is now the recommended over 2D echocardiography for cardiac chamber quantification due to its superior accuracy and reproducibility. Moreover, the etiology of increased left ventricular wall thickness is often difficult to determine on routine echocardiographic images by human observation. To address these limitations, Duffy et al trained a DL algorithm to measure left ventricular wall thickness on echocardiogram videos with high accuracy when tested in an external data set . This DL algorithm also accurately classified cardiac amyloidosis (AUC 0.79) and hypertrophic cardiomyopathy (AUC 0.89) separately from other causes of left ventricular hypertrophy.Echocardiography also enables quantification of cardiac structure and identification of morphological abnormalities in different disease states. Left ventricular wall thickness measurement, a marker of the degree of left ventricular hypertrophy, is subject to substantial intra- and interobserver variability. trained a ML model integrating 20 different echocardiographic variables acquired at end systole to outperform conventional diastolic and strain parameters in differentiating physiological from pathological patterns of left ventricular hypertrophy. Lancaster et al employed ML hierarchical clustering of diastolic echocardiographic parameters for the classification of diastolic dysfunction, with ML clusters providing improved prediction of event-free survival over guideline-based classification. Recently, Kobayashi et al used K-means clustering of clinical, biochemical, and echocardiographic parameters to identify three distinct phenotypic groups , each with different long-term risk of heart failure hospitalization or cardiac death. These studies demonstrate the potential of such ML techniques for grouping directly observed echocardiographic parameters and uncovering patient phenotypes with a similar behavior  for predicting future adverse events. Such algorithms could help idenfity patients with high-risk phenotypes who may benefit from more careful follow-up or targeted heart failure therapies.With respect to cardiovascular risk stratification, Narula et al Arsanjani et al used a ML classifier to combine MPI parameters (including stress TPD and stress-induced ECG changes) with clinical variables for the diagnosis of ≥70 stenosis on invasive coronary angiography. The resultant ML model (AUC 0.94) outperformed stress TPD alone (AUC 0.89) and clinician interpretation . MPI data have also been incorporated into ML models for outcome prediction. In an analysis of REFINE-SPECT , a collaborative international multicenter registry of over 20,000 patients undergoing stress MPI, Hu et al used ML to combine quantitative measures of regional perfusion deficits with stress test and clinical variables to predict early coronary revascularization. They showed ML prediction to outperform both clinician interpretation and stress TPD assessment. Similarly, Betancur et al employed a ML classifier for predicting 3 year cardiac event risk in 2619 patients undergoing stress MPI. The ML model integrating imaging, stress test, and clinical parameters was superior to clinician interpretation, TPD assessment, or ML with only imaging variables  for predicting events. Moreover, the comprehensive ML model provided a 26% risk reclassification for events compared to clinician interpretation. These studies highlight the role of ML in selecting and ranking the most important predictor variables, allowing clinicians and researchers to interpret the relative influence of each variable on individual patient risk.The domain of nuclear cardiology has seen rapid advancements in AI applications in recent years. Single-photon emission computed tomography (SPECT) MPI is widely used for patients with suspected CAD. Total perfusion deficit (TPD) on stress MPI is a standardized quantitative measurement of the extent and severity of myocardial ischemia which has similar accuracy to clinical visual interpretation for detecting obstructive stenosis. trained a convolutional neural network using standard polar map images to predict obstructive coronary stenosis. The DL prediction exhibited a higher AUC than TPD assessment  and was computed at a speed of <0.5 s per scan. In a pragmatic study, Otaki et al implemented an explainable AI model for the detection of obstructive CAD on SPECT MPI into routine clinical reporting software. The DL algorithm automatically calculated the probability of obstructive CAD using stress myocardial perfusion, wall motion, and wall thickening maps, as well as left ventricular volumes, age, and sex (p < 0.0001). DL results were generated in real-time (<12 s on a standard workstation) and the attention maps facilitated the acceptance of AI results by clinicians. Subsequently, Singh et al applied this DL algorithm to polar maps of 20,401 patients from the comprehensive REFINE SPECT clinical-imaging database for the direct prediction of future adverse cardiac events at 4.4 years. Here, DL achieved an AUC of 0.75 compared with 0.70 (p < 0.0001) for stress total perfusion deficit, and patients in the highest quartile of DL-predicted risk were at 10-times greater hazard of a cardiac event compared with patients in the lowest quartile of DL-predicted risk (9.7% vs  0.9%).DL algorithms can perform direct, automated analysis of MPI images for diagnosis and prognostication of outcome in CAD. In 1638 patients from REFINE SPECT, Betancur et al and sex . For cliNon-invasive cardiovascular imaging contains a wealth of high-dimensional data and is well-suited to AI applications. ML and DL algorithms are increasingly being employed for tasks such as image segmentation, automated measurements, diagnosis, and outcome prediction. Ongoing rapid advancements in computing hardware and software will facilitate the future implementation of these algorithms into clinical workflow. The ultimate goal would be for AI algorithms to be embedded into routine cardiac image analysis and reporting software, automatically performing measurements and calculating risk scores in real-time to function as clinical decision support tools for physicians. Explainable AI will enable clinicians and researchers to interpret ML models and interact with the outputs for an individual patient prediction.


In recent years, a significant improvement in left ventricular assist device (LVAD) technology has occurred, and the continuous-flow devices currently used can last more than 10 years in a patient. Current studies report that the 5-year survival rate after LVAD implantation approaches that after a heart transplant. However, the outcome is influenced by the correct selection of the patients, as well as the choice of the optimal time for implantation. This review summarizes the indications, the red flags for prompt initiation of LVAD evaluation, and the principles for appropriate patient screening. Management options for patients with advanced heart failure with reduced ejection fraction (advHFrEF) include orthotopic heart transplantation ,2,3, lonGenerally, consideration of LVAD implantation is indicated in all patients with persistent NYHA class III–IV despite guideline-directed medical therapy (GDMT) and poor medium-term prognoses  11]. Ho. Ho11]. ►Bridge to Transplant (BTT): Indicated in patients on the orthotopic heart transplant list unable to maintain adequate end-organ perfusion with GDMT alone;►Bridge to Candidacy (BTC): Suitable in selected patients with temporary contraindications to orthotopic heart transplantation that could be reversed after support with an LVAD ;►Destination Therapy (DT): Recommended in patients with an absolute and permanent contraindication to orthotopic heart transplantation, in whom an LVAD is used as a long-term therapy.There are three main indications for LVAD implantation:Deriving the maximal mortality and morbidity reduction from LVAD implantation depends on the timing of the implant . Proceedworsening heart failure in the last 12 months indicates a poor prognosis in patients with advHFrEF [p < 0.0001) [Two or more episodes of advHFrEF . A retroadvHFrEF . More re 0.0001) .intolerance to disease-modifier drugs due to symptomatic hypotension, resulting in the reduction or discontinuation of guideline-directed medical therapy (GDMT), is associated with a poor prognosis [p = 0.0001) [The onset of rognosis  unless irognosis . In a re 0.0001) .Inotrope dependence  is a clear negative prognostic marker in patients with worsening advHFrEF [advHFrEF  and shouA high diuretic dose or the need to escalate the diuretic dose is also associated with poor prognosis. A hallmark study that enrolled 1354 patients with advHFrEF showed that patients in the highest quartile for diuretic dose  had the worst prognosis [rognosis .Arrhythmic storms (>3 episodes of sustained ventricular tachycardia or ventricular fibrillation in 24 h) are associated with a poor prognosis, mainly if they occur in patients in New York Heart Association (NYHA) functional class III–IV [s III–IV  or in a s III–IV . Carefulhyponatremia is a marker of poor prognosis in patients with acute heart failure [Numerous studies have shown that  failure ,29. More failure .elevated plasma levels of NT-proBNP despite GDMT is associated with poor prognosis in patients with advHFrEF. In a prospective study that enrolled 550 patients with heart failure of ischemic and non-ischemic etiology, NT-proBNP levels of >5000 pg/mL were associated with increased mortality , which markedly discriminated advHFrEF candidates for LVAD implantation or urgent heart transplantation [The persistence of antation . Furtherantation .►Marked left ventricular dilatation (left ventricular end-diastolic diameter of >80 mm);►Left ventricular ejection fraction of ≤25%;►Persistent restrictive mitral filling patterns or echocardiographic findings suggesting pulmonary hypertension despite aggressive diuretic therapy and therapy with inodilators  .Certain echocardiographic features may suggest the presence of an end-stage form of heart failure, such as the following :►Marked Despite the importance of echocardiographic red flags, they should always be contextualized in the clinical scenario to best determine whether a patient warrants referral for the consideration of LVADs or other advanced therapies.Right heart catheterization is often used in patients with suspected advHFrEF to quantify the degree of hemodynamic dysfunction .The persistence of a reduced cardiac index or increased left ventricular filling pressures despite the aggressive use of intravenous diuretics and inodilators is an important predictor of poor outcomes  and warrAlthough the evaluation of patients for LVADs should be performed in specialized centers for advHFrEF treatment, we consider it useful for the clinical cardiologist to be familiar with the basic principles of LVAD preimplant evaluation. Therefore, in this session, we practically cover the rationale for the primary investigations that a patient candidate for LVAD implantation undergoes in the preoperative phase. The class of recommendations (COR) and level of evidence (LOE) reported for each single indication are provided according to the latest recommendations by the International Society for Heart and Lung Transplantation .►Kidney function: Because the presence of advanced chronic kidney disease (CKD) strongly affects the LVAD post implantation prognosis [2), the implantation of an LVAD should also be carefully evaluated by a multidisciplinary team  as the anticipation of permanent dialysis is a contraindication to LVAD implantation .rognosis , evaluat►Hepatic function: Severe hepatic dysfunction is strongly related to an adverse prognosis post-LVAD implantation [antation , so a li►Blood glucose and glycated hemoglobin: Diabetes has been associated with infection and late mortality in LVAD patients [patients , so in a►Platelet count and coagulation parameters: Because major post-LVAD complications include both thrombotic (particularly pump thrombosis) and hemorrhagic  events [) events ,41, all Blood analysis represents an important step for the evaluation of patients with advHFrEF as potential candidates for LVAD implantation; mainly, the following parameters should be evaluated:All patients should undergo echocardiography before LVAD implantation to assess their right ventricular size and function and to identify and quantify any coexisting valvulopathies .►Right ventricle evaluation: Most echocardiography-based predictors of right ventricular failure (RVF) post-LVAD implantation are based on assessing RV systolic–diastolic function. However, it was recently demonstrated that evaluating the RV–pulmonary artery (PA) parameters may increase the possibility of predicting postimplant RVF [lant RVF . A systolant RVF ,44; similant RVF  and an Rlant RVF  are assolant RVF . Finallylant RVF , have belant RVF  or an RVlant RVF  identifi►Valvular evaluation: Aortic valve regurgitation results in recirculation and reduced forward flow in patients with LVADs [th LVADs . Therefoth LVADs ; therefoth LVADs ; therefoth LVADs ,54; on tth LVADs ,56, repaRecommendations for patients with preexisting prosthetic valves are shown in Concomitant valve surgery with LVAD implantation increases the surgical risk due to the prolonged extracorporeal circulation time and increased incidence of postoperative RVF . These f►Chest imaging: Evaluation of the intrathoracic anatomy is mandated in the preoperative assessment for LVAD surgery [ surgery . Therefo►Liver imaging: In patients whose clinical history and/or laboratory investigations suggest hepatic dysfunction, screening for fibrosis or cirrhosis via ultrasonography or CT scan  is necessary. Since fibrosis in the absence of significant portal hypertension may not be a contraindication to LVAD implantation [antation , especiaantation . For thi►Gastrointestinal imaging: Gastrointestinal bleeding is present in 15–30% of LVAD patients; the rate is higher in patients with previous gastrointestinal bleeding or colon polyposis [olyposis . Therefoolyposis , screeniIn addition to transthoracic echocardiography, other imaging techniques should be used for the appropriate selection of candidate patients for LVAD implantation:An invasive hemodynamics assessment by right heart catheterization should be performed in all patients before LVAD implantation  to measure their intracardiac filling pressures and assess their RV function . The mai►Nonadherence to drug therapy;►Unsafe home environment;►Absence of social support;►Presence of mental illness;►Presence of drug or alcohol dependence.All patients who are candidates for LVADs should undergo a comprehensive psychosocial assessment  to identify the following:All the above factors are associated with worse outcomes after LVAD implantation, so the candidacy for an LVAD of a patient with one or more of these factors should be carefully considered by a multidisciplinary team  ,66.LVAD implantation represents a valuable therapeutic option in carefully selected patients with advHFrEF. Technological innovations and improved implantation techniques ensure an immediate improvement in symptoms, quality of life, and short- and medium-term survival. However, the high frequency of postimplant adverse events and the high economic and resource costs are still barriers to adopting this treatment for a broader population of patients with advHFrEF. Therefore, improving the patient selection process is crucial to making LVAD implantation an acceptable and cost-effective alternative to orthotopic heart transplantation.


The objective of this study is to verify the reliability and the concurrent and discriminant validity of the measurements of spasticity offered by the robotic device, quantifying the (1) test–retest reliability, (2) correlation with the clinical evaluation using the Modified Ashworth Scale (MAS), (3) inter-rater reliability between the two physiotherapists, and (4) ability to discriminate between healthy and stroke patients.A total of 20 stroke patients and 20 healthy volunteers participated in the study. Two physical therapists (PT1 and PT2) independently evaluated the hand spasticity of stroke subjects using the MAS. Spasticity was assessed, both in healthy and stroke patients, with the Amadeo device at three increasing velocities of passive movement for three consecutive repeated assessments, while raw data of force and position were collected through an external program.The intraclass correlation coefficient (ICC) and the weighted kappa were computed to estimate the reliability of the Amadeo device measurements, the inter-rater reliability of MAS, and the correlation between the MAS and Amadeo device measurements. The discriminant ability of the Amadeo device was assessed by comparing the stroke and healthy subjects' spasticity measurements with the percentage of agreements with 0 in MAS for healthy subjects.The test–retest reliability of the Amadeo device was high with ICC at all three velocities  but lower if analyzed with weighted kappa correlation  as mean values for each velocity. The correlation between Amadeo and the clinical scale for stroke patients with weighted kappa correlation was poor (0.280 ± 0.212 for PT1 and 0.290 ± 0.155 for PT2). The inter-rater reliability of the clinical MAS was high (ICC = 0.911).Both MAS and Amadeo spasticity scores showed good reliability. The Amadeo scores did not show a strong clinical correlation with the MAS in stroke patients. Hitherto, Amadeo evaluation shows trends that are consistent with the characteristics of spasticity, such as an increase in spasticity as the speed of muscle stretching increases. The ability of the device to discriminate between stroke patients and healthy controls is low. Future studies adopting an instrumental gold standard for spasticity may provide further insight into the validity of these measurements. Spasticity is usually defined as a motor alteration or disorder characterized by an increase in the excitability of the myotatic or stretch reflex, causing an increase in tone. The speed is a determining factor, as the higher the speed, the greater the stretch-resistant reflex contraction  are the most common motor disorders after stroke and markedly influence the patient, becoming a challenge during the rehabilitation process  on one side in a separate way and the other fingers (from finger two to finger five) on the other side. The Amadeo selects a time window during finger extension, in which it performs the spasticity measurement . If an unexpected finger flexion reaction is detected during the evaluation and the finger cannot reach the full ROM, the finger slide automatically stops, and the spasticity evaluation finishes, flexing the fingers again to end the assessment, attributing the corresponding degree of spasticity based on the force exerted against that extension. During the Amadeo spasticity assessment, the fingers are extended at three different velocities as follows: slow (v1 ≈ 0.01 m/s), medium (v2 ≈ 0.05 m/s), and fast (v3 ≈ 0.1 m/s). Although the MAS is performed clinically at a single speed, here, we retained the Amadeo spasticity measurements at the three velocities, to test the effect of speed on the assessment.For data collection, data acquisition software is used. This program records the force and position data of the fingers in real time, while the subject is performing the therapy with the Amadeo device. After collecting data, these were processed with Matlab .Physical therapists used the MAS to assess the patients' hand spasticity at a single high speed, as established by the scale UAB UA, .In this study, we analyzed data obtained from 40 volunteer participants, recruited through the Hospital Los Madroños (Madrid), divided into two groups:The control group, composed of 20 healthy subjects, was selected based on the inclusion criteria as follows: (1) being aged between 18 and 80 years (2), their age being close to the mean age of the experimental group, and (3) acceptance of informed consent. The exclusion criteria for this group were as follows: (1) having previously suffered neurological pathologies, (2) presenting pathologies affecting the mobility and strength of the upper limb, (3) photosensitive epilepsy, (4) rejection of new technologies, and (5) cognitive deficits preventing them from understanding the program.The experimental group was composed of 20 stroke patients from the advanced neurorehabilitation unit of hospital los madroños. the inclusion criteria for this group were as follows: (1) diagnosis of hemispheric stroke with upper limb involvement; (2) being aged between 18 and 80 years; (3) ability to provide informed consent; (4) sufficient trunk control to maintain prolonged sitting for at least the minimum time necessary to perform the robotic therapy; (5) preserved vision; (6) patient conscious and able to understand verbal commands and instructions; and (7) patients with no other concomitant pathologies affecting motor and/or sensory function. The exclusion criteria were as follows: (1) hemiparesis caused by other diagnoses; (2) pregnancy or lactation; (3) photosensitive epilepsy; (4) severe medical or psychiatric disorder; and (5) refusal of new technologies.All participants gave their informed consent; the procedures had the approval of the institutional ethics committee  and were conducted in accordance with the Declaration of Helsinki.Each participant was independently assessed on the same day by two physical therapists (PT1 and PT2) who scored the spasticity of the full hand and each finger using the MAS. The therapists were mutually blind to each other's assessment. To minimize the modulation of muscle tone induced by mobilization, the assessors were asked to estimate spasticity in <5 repetitions. Then, the participant was assessed using the Amadeo device. The positioning on the device was carried out according to the indications in the user manual provided by Tyromotion for the correct use of Amadeo. The subjects were held in a seated position, in a comfortable and relaxed posture, in a chair with backrest and armrests, and with the forearm resting on the device in pronation. The straps were adjusted to the arm and wrist, and the magnets were placed on the distal phalanx of the fingers, leaving the distal interphalangeal joint free. The experimental room was set between 21°C and 23°C, according to regulatory bodies in Spain.Before starting the assessment, Amadeo needs a reference of the passive ROM of each finger to establish the limits of movement in which the device will move the subject's fingers during the session. The therapist passively opened the participant's hand coupled to the device until reaching the limit of flexion–extension movements. Then, for each velocity, the Amadeo device performs a cycle consisting of the extension of fingers, maintenance of this extension, and flexion (one cycle for velocity). Amadeo provides spasticity estimation and delivers a score for both the full hand and each finger, that is assumed to be equivalent to the MAS. Raw data of position and force were acquired while spasticity assessment saved in an external device. The result of Amadeo's evaluation for each finger and the full hand was recorded in the data collection notebook at the end of the test, together with relevant observations, if applicable. The time-course of the whole evaluation is presented in The three velocities mentioned above were measured, starting with the slowest (v1) and ending with the fastest (v3). At each velocity, the group from finger two to finger five was recorded first, followed by the evaluation of finger one. This procedure was repeated three times  see .The PTs carried out the assessment in succession with ~1-min rest between the two evaluations. 1-min rest was also assured before the assessment by Amadeo.The raw data were recorded by the software with a 200 Hz sampling rate. These were analyzed automatically by MATLAB for their interpretation. To verify whether the number of repetitions of both the PTs and Amadeo affected the evaluation, we analyzed the time-course of the MAS ratings using a Friedman test.Amadeo's quality of data used for MAS estimation was descriptively reported in the result section. Statistical analysis was made for (1) Amadeo reproducibility assessment, (2) correlation between Amadeo and each PT evaluation, (3) correlation between clinical assessment obtained from the two PTs, and (4) capability of the Amadeo to distinguish between healthy and stroke patients. The Amadeo reproducibility assessment was obtained by correlating each velocity  over the three runs of evaluation  of the whole hand using the weighted kappa coefficient.Moreover, we also computed the one-way random effect interclass correlation coefficient (ICC), with a 95% confidence interval . For this reason, the position and force curves for the finger one have been excluded from the data analysis.To evaluate the capability of Amadeo to distinguish between healthy and stroke subjects, we also calculated the percentage of agreement between PTs and between Amadeo and PTs. We considered the value as correct assigned by Amadeo which agrees with the value offered by the therapists. For this purpose, the percentage of success between the Amadeo and PTs for each velocity in each run was calculated. Two variables about the agreement were calculated as follows: (i) the percentage of absolute agreement ; (ii) the percentage of agreement by considering agreement values that oscillate between ±1 of the MAS .All participants completed the whole examination. Clinical and demographic data are presented in p > 0.05).To confirm if the number of repetitions of the PTs and Amadeo affects our evaluation, we analyzed the time-course of the MAS ratings. To confirm the quality of the recorded data and speed-dependency of the spasticity assessment, Amadeo's MAS estimation vs. velocity was performed. As observed in The reproducibility of the Amadeo spasticity estimation using weighted kappa was 0.584 for v1, 0.748 for v2, and 0.749 for v3. These scores indicate a substantial agreement at least for data obtained at v2 and v3  for both the whole hand and all fingers and ICC and weighted kappa. For example, the ICC was 0.76 with PT1  and 0.72 with PT2 . On the other hand, the worst correlations were obtained with lower velocity (v1). For example, the ICC was 0.27 with PT1 (run 1 v1 being the worst correlation) and 0.18 with PT2 .MAS evaluations between therapists (PT1 vs. PT2) showed high correlations for the hand assessment . When individual fingers were evaluated, a satisfactory result was obtained , these data being the highest correlations found in the analysis of each finger. The percentage of agreement between PTs was also very high see .As described above, some curves obtained by Amadeo were of poor quality (inconsistencies). However, when the analysis was conducted after removing the curves with inconsistencies, the results were similar. No relationship was observed between the quality of the biomechanical curves and the agreement between the PTs and Amadeo assessment.In this analysis, we considered as correct the value assigned by Amadeo that agrees with the value offered by the therapists. For this purpose, the percentage of success between the Amadeo and therapists for each velocity in each run was calculated see . In healAs for the spasticity assessment in stroke patients, the percentage of agreement between the Amadeo and the PTs scores  was 26% for run 1, 31% for run 2, and 30% for run 3, when considering PT1 see , and 25%Our data can be summarized as follows: (1) the reproducibility of the Amadeo grading is high ; (2) the correlation between Amadeo and PTs is higher when Amadeo evaluates spasticity at high velocity (v3), while it is very low at lower velocities (v1 and v2); (3) the reproducibility of the PT grading is high; and (4) the percentage of agreement between Amadeo and PTs is lower than the agreement between PTs. Moreover, we observed that Amadeo evaluation of the spasticity of finger one is often of very poor quality, with many inconsistencies and the absence of an observed correlation between spasticity scores and velocity. Thus, finger one should be discarded from the Amadeo spasticity assessment. The other fingers also have occasional inconsistencies, but the elimination of inconsistencies does not improve the correlation between Amadeo and PTs.The Amadeo spasticity assessment returned the best results, both in terms of reproducibility and correlation with clinical scores, for v3. This is not surprising since, among the three speeds, v3 (0.1 m/s) is the closest one to the velocity that should be adopted in the clinical test, according to Bohannon and Smith  full RO.Early finger extension capacity after stroke is a critical motor sign of recovery. This capacity can be used for direct therapy to those who will most benefit from it  for the hand spasticity assessment reported a moderate-substantial weighted kappa of 0.586, and an ICC of 0.911. According to Hager , ICC scoWe also found good reliability for the robotic device, with ICC > 0.900 at all three velocities and weighted kappa >0.6 at v2 and v3. These values are much higher than those reported by Germanotta et al. , who fouIn the study by Esquenazi et al. , the valThis study has some limitations. First, PTs and Amadeo evaluation were performed sequentially during a single session of assessment. As we know that training may affect spasticity, it is possible that the real evaluation conditions were not the same. To avoid interfering with the reproducibility of the scale, some authors suggest that MAS should not be repeated more than five times in each record (de Raadt et al., Another limitation is that the three acquisitions with the Amadeo device were performed in succession, without repositioning the patient's arm and hand. We chose this procedure because we were interested in studying the reproducibility of the Amadeo measurements during passive finger extension, avoiding the influence of other potential sources of variability. Of course, both arm positioning and ROM setting depend on the rater and have a great impact on the measurements, so they could greatly increase variability.Moreover, the whole evaluation was performed on the same day, and no longitudinal study was performed. Considering that assessment of spasticity usually requires multiple longitudinal measurements, future studies should confirm our data in a longitudinal way and possibly on a larger sample of patients. Future studies should also include a more accurate standardization of the position of the hand and fingers and the ROM setting, together with stronger control over parameters such as the temperature of the environment and the time elapsed since the pathological event, to reduce errors during acquisitions. Finally, we chose the MAS as the gold standard, but the validity and reproducibility over time of this scale have been thoroughly questioned. Possibly, the Amadeo scores and the clinical MAS scores should be compared with instrumental measurements of spasticity, providing a more reliable and valid reference standard. Moreover, since the device offers an assessment also on the Tardieu scale, future studies should verify the reliability and the concurrent and discriminant validity of the measurements of spasticity offered by the robotic device for this scale. Indeed, while the MAS focuses on the resistance of the muscle to stretching, the Tardieu scale is based on the velocity exerted during stretching. It is possible that the three speeds of assessment proposed by the Amadeo protocol are therefore specifically designed for the Tardieu-like assessment, possibly returning better results.In conclusion, both the clinical and the Amadeo MAS scores were reproducible, although further studies are needed to test reproducibility over different days. However, the Amadeo MAS scores did not show a strong clinical correlation with the MAS in stroke patients. This may suggest that some aspects of spasticity are engaged by Amadeo and not by the PTs and/or vice versa. It is possible that Amadeo scores related to finger 2–5 at high velocities measure a performance consistent with spasticity, but this should be verified by further research. Future studies, including standardization of the position of the hand and finger in order to reduce errors during acquisitions and adopting an instrumental gold standard for measuring spasticity, may provide more insight into the validity of these measurements.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Comité de Ética de la Investigación con medicamentos del Hospital Universitario Severo Ochoa. The patients/participants provided their written informed consent to participate in this study.RU, AM, and JT: conceptualization. RU, AM, and CS-M: data curation and formal analysis. RU and AG: methodology. RU, AG, AO, and NL: investigation. JT: project administration. AM: software. AO and JT: supervision. RU, AM, AP, MB, FC, FL, AO, and JT: writing. RU, AG-M, FL, and JT: critical revisions. All authors contributed to the article and approved the submitted version.


Whether myocardial inflammation causes long-term sequelae potentially affecting myocardial blood flow (MBF) is unknown. We aimed to assess the effect of myocardial inflammation on quantitative MBF parameters, as assessed by 13N-ammonia positron emission tomography myocardial perfusion imaging (PET-MPI) late after myocarditis.n = 469), healed , and scarred . Additionally, apparently healed segments but with scar at PET were classified as PET discordant (n = 18).Fifty patients with a history of myocarditis underwent cardiac magnetic resonance (CMR) imaging at diagnosis and PET/MR imaging at follow-up at least 6 months later. Segmental MBF, myocardial flow reserve (MFR), and 13N-ammonia washout were obtained from PET, and segments with reduced 13N-ammonia retention, resembling scar, were recorded. Based on CMR, segments were classified as remote , MFR , and washout . While PET discordant segments did not differ from healed segments regarding MBF and MFR, washout was higher by ~ 30% (p < 0.014). Finally, 10 (20%) patients were diagnosed by PET-MPI as presenting with a myocardial scar but without a corresponding LGE.Compared to remote segments, healed segments showed higher stress MBF (2.71 mL*minIn patients with a history of myocarditis, quantitative measurements of myocardial perfusion as obtained from PET-MPI remain altered in areas initially affected by inflammation.CMR = cardiac magnetic resonance; PET = positron emission tomography; LGE = late gadolinium enhancementThe online version contains supplementary material available at 10.1007/s00259-023-06314-0. Myocarditis is an entity that clinically manifests in very diverse ways, rendering the true incidence of the disease difficult to assess. However, it is not uncommon and has been found in up to 9% of routine autopsy cases and in up to 30% of those performed for unexplained sudden cardiac death in young individuals , 2. It iThe present study aimed to evaluate whether quantitative MBF parameters, as assessed by the gold-standard positron emission tomography myocardial perfusion imaging (PET-MPI), remain permanently altered in myocardial regions affected by inflammation in patients with a history of myocarditis.Fifty patients with a history of clinically suspected and cardiac magnetic resonance imaging (CMR)-confirmed myocarditis deemed to be caused by a viral infection  and diagCMR datasets were acquired on a 1.5- or 3.0-Tesla scanner  using an electrocardiogram-gated breath-hold protocol. The diagnosis of myocarditis was based on cine-CMR, T2-weighted imaging, and T1-weighted late gadolinium enhancement (LGE) imaging. LGE images were generated 10 min after the intravenous administration of a GBCA. For the present study, all datasets were analyzed with commercially available software . Myocardial LGE and/or T2 hyperintensities of the left ventricle (LV) were visually identified, and the software’s tissue characterization module was used to assess enhancement in myocardial segments with LGE and/or T2 hyperintensities using a threshold of 5 and 2 standard deviations (SD) over remote myocardium, respectively. Polar plots based on the American Heart Association (AHA) 16-segment LV model were then generated, allowing for a direct segmental comparison between baseline and follow-up CMR and PET-MPI. For calculation of LV and right ventricular (RV) end-diastolic volumes (LVEDV and RVEDV), LV and RV end-systolic volumes (LVESV and RVESV), LV mass index , and LV and RV ejection fractions (LVEF and RVEF), endocardial contours were traced in short-axis views.PET and CMR datasets were acquired on a hybrid PET/MR device incorporating a 3.0-Tesla MR and a latest-generation PET scanner . All patients refrained from caffeine intake for at least 12 h before the examination. The stress protocol consisted of 6 min of adenosine infusion (140 μg*kg-1*min-1). A body mass index-adapted dose of 13N-ammonia ) and a weight-adapted dose of GBCA  were simultaneously injected after 3 min into adenosine stress. Dynamic PET data acquisition consisted of 21 frames , followed by an electrocardiogram (ECG)-gated acquisition over 10 min. Resting perfusion imaging was performed thereafter using an identical protocol. All PET data were acquired in 3D mode and reconstructed using time-of-flight reconstruction with VUE Point FX (2 iterations and 16 subsets) and a 5 mm Hanning filter. Standard Dixon-based maps were used for attenuation correction. CMR datasets were acquired in parallel using an ECG-gated breath-hold protocol, and LGE images were generated 10 min after the administration of GBCA. The PET/MR imaging acquisition protocol is depicted in Fig. −1 × g−1) was obtained from stress and rest scans and calculated and analyzed using QPET  using a simplified 2-compartment model. Rest MBF (rMBF), stress MBF (sMBF), and myocardial flow reserve  were recorded per patient and per segment. rMBF was adjusted by the rate-pressure product. Additionally, the K2 constant  was calculated for every patient and segment for rest (rK2) and stress (sK2) using PMOD version 3.7 . Polar plots based on the AHA 16-segment LV model were generated to display the results of the quantitative analysis. Patients and segments with myocardial scar were defined as having a visually perceivable relative reduction in 13N-ammonia retention as per clinical routine. CMR analysis was performed in an identical manner as the baseline datasets.Quantitative MBF (in mL × minThe following routine laboratory parameters were recorded at baseline and at follow-up: high-sensitivity troponin T (Troponin T-hs), creatine kinase (CK), myoglobin, NT-proBNP , C-reactive protein (CRP), and leukocyte count.Based on the hypothesis of this study that myocardial perfusion remains permanently altered after inflammation, we placed a special focus on myocardial segments that were affected by inflammation at baseline. A combination of segmental T2 hyperintensity and/or with LGE at baseline but without LGE at follow-up was considered a sign of inflammation. This methodology is based on the evidence that LGE in the acute phase of myocarditis is not necessarily synonymous with irreversible damage, particularly if it resolves over time , 14. We t-test or Mann-Whitney U test, respectively, and with the Kruskal-Wallis test with post hoc pairwise comparison for comparison of multiple groups. We applied the Benjamini-Hochberg procedure to correct for multiple comparisons and to control the false discovery rate (FDR) at 5% [p-values < 0.05 and at a level below the critical k-value were considered to indicate statistical significance. Otherwise, two-sided p-values < 0.05 were considered significant.Numbers and percentages were calculated for categorical data and means and SD or median and interquartile range (IQR) for continuous data, as appropriate. Statistical analysis was performed using SPSS Statistics . The Shapiro-Wilk test was applied to assess the normality of distribution. Correlations between continuous variables were assessed using Spearman’s analysis. A comparison between two continuous variables with normal and non-normal distributions between different groups of patients or segments was performed using the Student’s R) at 5% . AccordiPatients’ demographics and clinical characteristics are provided in Table An overview of CMR and 13N-ammonia PET-MPI findings is given in Table At baseline CMR, 49 (98%) patients presented with at least one segment with LGE, and 29 (58%) patients presented with at least one segment with T2 hyperintensity. None of the patients showed persistent T2 hyperintensity at follow-up. Persistent LGE, resembling scarred myocardium at follow-up, was found in CMR in 45 (90%) patients. By contrast, 13N-ammonia PET-MPI depicted scarred myocardium at follow-up in 38 (76%) patients. Of note, in one out of the five patients who did not present with any LGE depictable by CMR at follow-up, an area of reduced 13N-ammonia retention apparently corresponding to a myocardial scar was found in 13N-ammonia PET-MPI  patients in whom one or more areas of scar were diagnosed by 13N-ammonia PET-MPI but whereby no LGE was depictable by CMR. An example is provided in Fig. Quantitative measurements of myocardial perfusion as obtained from 13N-ammonia PET-MPI remain altered in myocardial segments initially affected by inflammation in that these segments show higher hyperemic MBF and, consequently, MFR but also higher 13N-ammonia washout rates than remote segments.At a median of 25 months after diagnosis of acute myocarditis, edema resolved in all patients, and apparent complete healing  was observed in 10% of patients.In a small proportion of segments (2.4%) with signs of inflammation at baseline but apparent complete healing at follow-up, 13N-ammonia retention remained substantially reduced. On a patient level, this translated into 10 (20%) patients in whom one or more areas were diagnosed by 13N-ammonia PET-MPI as presenting with a myocardial scar.The fact that the vast majority (94%) of these segments were found to be affected by inflammation at baseline and with 13N-ammonia washout rates increased by up to 53% in these segments as compared to remote segments suggests an association between inflammation at baseline and washout rates during follow-up.The main results of the present study may be summarized as follows:CMR is recommended as an initial imaging modality in suspected myocarditis and has been shown to confer diagnostic and prognostic value in this disease entity , 16. HenThe present work extends our knowledge beyond previous studies by addressing the long-term impact of inflammation on MBF. Through the combination of hybrid PET/MR technology and the implementation of a methodology that allows for the comparison of CMR and PET at a segmental level, our study provides unprecedented insights into the interplay between biomorphological findings from CMR and perfusion properties assessed at the molecular level by PET late after myocarditis. The importance of assessing the final status of myocardium on a regional  level rather than on a per-patient level is, in hindsight, corroborated by the fact that all patients in the present study exhibited at least one segment from each group.Our first main finding reveals an association between the dynamics during and after myocarditis, resulting in myocardium arriving in various states. Segmental MBF at rest did not differ among the groups, likely due to the size of residual fibrosis in most patients being too small to exert any substantial effect in terms of MBF assessment. On the other hand, surprisingly, we found that, on a segmental level, hyperemic MBF and MFR were higher in segments initially affected by inflammation but with (apparent) complete healing at follow-up and in those with discordant findings between 13N-ammonia and CMR at follow-up as compared to remote segments not initially affected by inflammation. This finding contradicts the scarce literature suggesting reduced coronary flow reserve and possibly even ischemia in patients with a history of inflammation. Klein et al. found that in 29 patients with acute biopsy-proven inflammatory myocardial infiltrates, the coronary reserve, as assessed by an argon gas chromatographic method, was reduced as compared to a control group . The expThe second main finding of our study relies on the ability of the kinetic models used for the calculation of MBF to also provide additional information beyond MBF, for example, in the form of the radionuclide washout rate. The washout rate in PET has been identified as a surrogate marker for myocyte metabolic and membrane integrity in the setting of ischemic cardiomyopathy for different radiotracers , 20. OurOur findings also suggest that the common definition of complete healing after myocarditis, currently based on the complete resolution of myocardial edema and LGE, along with normalized biomarkers, may not be completely comprehensive and does not necessarily reflect true restitutio ad integrum. However, it remains speculative as to why myocardium presents with long-term alterations in some patients but not in others and whether our findings have any prognostic implications. While the results of the present study ultimately remain hypothesis-generating in that regard, the fact that a substantial number of patients presented with PET findings corresponding to myocardial scar tissue in the absence of any substrate on concomitant CMR nevertheless has immediate implications on clinical imaging routine because our results indicate that discordant findings between CMR and PET-MPI  in patients with a history of myocarditis and/or inflammation should not readily be interpreted as false-positive  but may indeed be the result of subtle post-inflammatory sequelae on a cellular level not depictable with current CMR techniques. Our findings suggest that future studies, ideally combining molecular and morphological imaging as well as humoral markers, are needed to improve our understanding of the long-term effects of inflammation on the myocardium and the potential implications for our patients.In patients with a history of myocarditis, quantitative measurements of myocardial perfusion as obtained from PET-MPI remain altered in areas initially affected by inflammation. In a minority of patients, increased washout, arguably induced by inflammation, translates into PET findings, namely, areas with apparent scar but without a corresponding substrate on CMR.The main limitation of this study is the absence of PET/MR data at baseline, precluding us from a statement on changes in quantitative myocardial blood flow parameters between baseline and parameters. Unfortunately, reliable T1- and T2-mapping techniques for the PET/MR device were not available at the time of enrollment. Furthermore, it may be seen as a limitation of the study design that several different MR devices, including both 1.5 and 3.0 Tesla devices, were used for baseline imaging, potentially introducing differences in diagnostic accuracy, particularly for LGE detection. However, we aimed to mitigate this issue by performing a thorough and careful re-analysis of all baseline CMR scans, performing side-by-side visual comparisons, and applying a standardized approach by using only one software tool for analysis and by only including segments with LGE at least 5 standard deviations over remote myocardium for both the baseline and follow-up CMR scans. While this semi-automated thresholding should have improved specificity, potentially at the cost of slightly lower sensitivity, this methodology should have equaled out potential sensitivity issues among different scanners. Of note, all patients in the present study exhibited at least one segment from each group, and the contribution of segments to a group varied strongly per patient (Table Supplementary file1 (DOCX 15 KB)Below is the link to the electronic supplementary material.