Schwammenthal, Ehud

Abstract Background Mitral regurgitation (MR) and tricuspid regurgitation (TR) are prevalent valvular heart diseases associated with significant morbidity and mortality. Traditional echocardiography faces limitations in availability, cost, consistency, and reliability, leading to misdiagnosis and undertreatment. The application of artificial intelligence (AI) to echocardiographic scans has the potential to address these challenges. Methods This study evaluates the performance of an AI algorithm on an external population. The algorithm utilizes deep learning networks to analyze echocardiographic exams for diagnosing atrioventricular valve disorders. We tested the algorithm on transthoracic echocardiography data collected from a single center between 2013 and 2023. The model's performance was compared to ground truth values using two classification schemes: distinguishing between normal-mild and moderate-severe regurgitation, and categorizing results into four groups: normal, mild, moderate, and severe. Results The MR cohort included 280 patients, while the TR cohort comprised 298 patients. The model demonstrated a robust ability to identify clinically significant (moderate and above) atrioventricular valve regurgitation. The MR model achieved an area under the curve (AUC) of 0.98 (95% CI: 0.97–0.99), with 91% accuracy, 95% sensitivity, and 89% specificity. In comparison, the TR model exhibited an AUC of 0.96 (95% CI: 0.94–0.98), with 84% accuracy, 91% sensitivity, and 80% specificity. Conclusion The model demonstrated high diagnostic accuracy and reliability in assessing atrioventricular valve regurgitation severity, highlighting its potential as a valuable clinical tool. The findings underscore the role of AI in complementing expert evaluations and improving access to effective diagnostics, with future applications potentially including point-of-care diagnosis and monitoring of disease progression.

Abstract Aims Mitral and tricuspid regurgitation (MR and TR) are common in older adults and associated with substantial morbidity and mortality. While transthoracic echocardiography (TTE) is the diagnostic gold standard, access remains limited in many care settings. Artificial intelligence (AI)–based echocardiographic analysis may help address this diagnostic gap. Methods and results We externally validated a deep learning algorithm developed by Aisap.ai using TTE studies from the Mayo Clinic Health System (2013–23). The model analyses echocardiographic images to classify atrioventricular regurgitation severity and was evaluated against cardiologist interpretations. Performance was assessed using binary (normal–mild vs. moderate–severe) and ordinal (normal, mild, moderate, severe) classification schemes. Among 1541 eligible TTEs, the model returned predictions for 578 studies (38%). Performance analysis was limited to these cases. The MR cohort included 280 studies and the TR cohort 298. For MR, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.98 [95% confidence interval (CI): 0.97–0.99], with 91% accuracy, 95% sensitivity, and 89% specificity. For TR, the AUC was 0.96 (95% CI: 0.94–0.98), with 84% accuracy, 91% sensitivity, and 80% specificity. Conclusion In cases where a prediction was generated, the model demonstrated high diagnostic performance in identifying clinically significant atrioventricular regurgitation. These findings support the feasibility of AI-assisted echocardiography in diverse populations, while underscoring the need for technical alignment between model requirements and local acquisition practices to ensure real-world applicability.

Introduction: Transcatheter Mitral Valve Replacement (TMVR) using a trans-septal approach is a new therapeutic option for patients with severe mitral regurgitation (MR). However, both surgical as well as currently under-testing trans-catheter approach, are shown to cause decrease in LV function. The Innovalve TMVR is a novel self-expandable transcatheter mitral valve prosthesis, delivered trans-septally using femoral venous access. It is deployed in an intravalvular posterior position after subvalvular anchoring by rotation of 6 circumferential arms Hypothesis: TMVR procedure (Innovalve system) leads to LV anatomical and functional changes Methods: CT was performed before the Innovalve TMVR procedure, and after 3 to 6 months. CT protocol included retrospective ECG gating with full cardiac cycle coverage. LV end diastolic and end systolic diameters (LVEDD, LVESD) were measured in the left ventricular outflow tract orientation at the most basal level. Tip to tip papillary muscle distance was measured in the short axis orientation at the most basal slice demonstrating the papillary muscle tips. LV ejection fraction (LVEF) was calculated using the end systolic and end diastolic volumes. All measurements were performed at end systole and end diastole before MTVR and after 3-6 months using a dedicated platform (3Mensio). Results: Ten patients (7 males) average age 69+/-10 years, underwent the Innovalve TMVR procedure. Average LVESD decreased from 52±15 mm at baseline to 44±15mm (P=0.016) at 3-6 months. Similarly, LVEDD decreased from 60±14 mm at baseline, to 52±13mm at 3-6 months (P=0.039). Average papillary muscle tip to tip distance at end diastole (ED) decreased from 31.7±7mm at baseline to 28.4±6.5mm at 3-6 months (P=0.047).A trend of LVEF improvement was evident with an increase from 35%±12 average at baseline compared with 39%±12 average after 3-6 months (P=0.346). Conclusions: These preliminary results show that the TMVR procedure using the Innovalve system may result in favourable LV reverse remodelling changes including reduction of LVESD, LVEDD and ED papillary muscle distances, with improved LVEF. This encouraging data might suggest favourable postprocedural structural LV change.