Integrating Artificial Intelligence for Mitral Regurgitation Assessment: A systematic review and meta-analysis

Background Mitral regurgitation (MR) is a prevalent and potentially progressive cardiovascular condition, necessitating early detection to facilitate timely intervention and optimize patient outcomes. The increasing demand for efficient and precise diagnostic strategies has underscored the potential of artificial intelligence (AI) in clinical practice. By leveraging advanced AI algorithms, automated MR screening has the capacity to enhance the detection and classification of disease severity, thereby assisting clinicians in making well-informed decisions. This study aims to evaluate the accuracy and efficacy of AI-based echocardiographic analysis in the early diagnosis of MR. Main Text A comprehensive literature search was conducted across five databases, PubMed, Scopus, ScienceDirect, ProQuest, and Cochrane. Studies employing AI algorithms to analyze echocardiographic images for MR detection and severity classification were included. The methodological quality of each study was assessed using the QUADAS-2 tool for diagnostic accuracy studies. A quantitative meta-analysis was performed utilizing Meta-DiSc with a random-effects model. In total, nine studies met the inclusion criteria. Utilization of AI in echocardiographic detection of MR yield a pooled sensitivity of 0.85 (95% CI: 0.86–0.86), specificity of 0.83 (95% CI: 0.82–0.83), and an area under the curve (AUC) of 0.9745. Accurate detection and severity classification of MR could significantly improve the efficiency of treatment strategies. By reducing reliance on specialized personnel and enabling the use of portable imaging devices, AI can lower operational costs and expand access to high-quality diagnostics. Furthermore, AI integration has the potential to streamline clinical workflows, decrease diagnostic delays, and optimize resource allocation. However, successful implementation requires addressing challenges related to model generalizability, regulatory standards, clinician training, and integration into existing healthcare systems. Conclusion In conclusion, AI-assisted echocardiographic analysis presents a promising advancement in MR diagnostics, with the potential to enhance healthcare accessibility, particularly in resource-limited settings.