Diagnostic Performance of Artificial Intelligence-Assisted Echocardiography in Identifying Hypertrophic Cardiomyopathy: A Systematic Review and Meta-Analysis

Hypertrophic cardiomyopathy (HCM), the most common genetic cardiac disease, remains underdiagnosed most of the time due to overlapping echocardiographic characteristics and subjective interpretations. This systematic review and meta-analysis aimed to assess the diagnostic performance of artificial intelligence (AI)-assisted echocardiography interpretations for identifying HCM and to explore factors contributing to variability and validity. After a comprehensive search through various databases, eligible studies reporting diagnostic metrics such as sensitivity, specificity, or area under the curve (AUC) were included into our analyses. Data were pooled using a bivariate random-effects model, and heterogeneity was quantified with the I 2 statistic. Twenty-five studies were included into our meta-analysis. The pooled AUC for AI-based echocardiographic detection of HCM was 0.93 [95% confidence interval (CI), 0.90–0.95]. After trim-and-fill correction, the pooled AUC increased to 0.96 (95% CI, 0.93–0.97). Overall sensitivity and specificity were 0.89 (95% CI, 0.83–0.93) and 0.87 (95% CI, 0.76–0.94), respectively. Meta-regression revealed that convolutional neural network, support vector machine, and ensemble learning algorithms exhibited variable performance, with convolutional neural network-based models favoring higher sensitivity. We demonstrated that AI-based models evaluating echocardiographic data could be an accurate diagnostic tool for HCM. This highlights the potential of recent advancements to improve clinical decision-making.