Sudri, Karin

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.

BACKGROUND: While clinical practice guidelines advocate for multidisciplinary heart team (MDHT) discussions in coronary revascularization, variability in implementation across health care settings remains a challenge. This variability could potentially be addressed by language learning models like ChatGPT, offering decision-making support in diverse health care environments. Our study aims to critically evaluate the concordance between recommendations made by MDHT and those generated by language learning models in coronary revascularization decision-making. METHODS: From March 2023 to July 2023, consecutive coronary angiography cases (n=86) that were referred for revascularization (either percutaneous or surgical) were analyzed using both ChatGPT-3.5 and ChatGPT-4. Case presentation formats included demographics, medical background, detailed description of angiographic findings, and SYNTAX score (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery; I and II), which were presented in 3 different formats. The recommendations of the models were compared with those of an MDHT. RESULTS: ChatGPT-4 showed high concordance with decisions made by the MDHT (accuracy 0.82, sensitivity 0.8, specificity 0.83, and kappa 0.59), while ChatGPT-3.5 (0.67, 0.27, 0.84, and 0.12, respectively) showed lower concordance. Entropy and Fleiss kappa of ChatGPT-4 were 0.09 and 0.9, respectively, indicating high reliability and repeatability. The best correlation between ChatGPT-4 and MDHT was achieved when clinical cases were presented in a detailed context. Specific subgroups of patients yielded high accuracy (>0.9) of ChatGPT-4, including those with left main disease, 3 vessel disease, and diabetic patients. CONCLUSIONS: The present study demonstrates that advanced language learning models like ChatGPT-4 may be able to predict clinical recommendations for coronary artery disease revascularization with reasonable accuracy, especially in specific patient groups, underscoring their potential role as a supportive tool in clinical decision-making.