Artificial Intelligence for Significant Mitral Regurgitation Screening and Diagnosis: A Systematic Review and Meta-analysis
Abstract
Objectives
To evaluate performance of artificial intelligence (AI) models using electrocardiogram (ECG) and echocardiogram (ECHO) for predicting significant mitral regurgitation (MR).
Materials and methods
We performed a systematic review and meta-analysis of studies assessing AI models based on ECG or ECHO for detection of significant MR. Search was conducted in PubMed, Scopus, and Cochrane. Endpoints included: sensitivity, specificity of the models. Area under the summary receiver-operating characteristic curve (AUC) was calculated using a bivariate random-effects model.
Results
Fifteen studies (n = 2,470,826) were included: seven using ECG (n = 2,467,390) and eight using ECHO (n = 3,436). For AI-ECG models validated on external datasets, pooled sensitivity was 87.7% (95% CI 80.4 to 92.5) and specificity was 54.0% (95% CI 40.3 to 67.1), with an AUC of 81%. For AI-ECHO, sensitivity was 89.7% (95% CI 78.2 to 95.5) and specificity was 92.8% (95% CI 81.8 to 97.4), with an AUC of 96%.
Conclusion
AI models applied to ECG and ECHO demonstrate strong performance for detecting significant MR and may support clinicians’ diagnosis of MR. Clinical implementation, however, requires further validation and external testing across diverse populations.
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