Abstract Background Left ventricular hypertrophy (LVH) is a common finding in cardiovascular diseases, necessitating precise etiological differentiation for tailored treatment strategies. Echocardiography, crucial for detecting LVH, often exhibits limited accuracy in differentiating LVH etiology. Artificial Intelligence (AI) holds promise in augmenting echocardiographic feature analysis, thereby enhancing LVH assessment. Objectives We aimed to develop an explainable AI algorithm detecting LVH and differentiating its common etiologies, such as hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), or hypertensive heart disease (HHD), based on echocardiographic images. Methods The developmental datasets were sourced from diverse medical centers (867 subjects), while an independent external validation set was obtained from a single tertiary medical center (619 subjects). Utilizing the four fundamental echocardiographic views, conventional and harmonization-driven myocardial textures and geographic features were extracted, and the explainable classification algorithm was developed. (Figure 1) Results The developmental dataset (mean age, 52.0±17.9 years; 60.3% male) comprised with LVH present in 67.6% of the training set and 63.2% of the internal validation set (P=0.323). The external validation set (mean age, 51.3±14.6; 48.6% male) showed a significantly lower LVH prevalence than the internal validation dataset (33.1% vs. 63.2%; P <0.001). In internal validation, the classification model accurately detected LVH with an area under the curve (AUC) of 1.00 (95% confidence interval [CI], 1.00–1.00). For HCM, CA, and HHD classification, it achieved AUCs of 0.97 (95% CI, 0.94–0.99), 0.95 (95% CI, 0.90–0.99), and 0.86 (95% CI, 0.78–0.93), respectively. In external validation, the model maintained impressive performance: LVH, 1.00 (95% CI, 0.99–1.00); HCM, 0.96 (95% CI, 0.92–0.98); CA, 0.89 (95% CI, 0.83–0.93); and HHD, 0.86 (95% CI, 0.81–0.91). (Figure 2) Overall, the AI-enhanced echocardiographic feature analysis successfully differentiated the causes of LVH, demonstrating high accuracy in internal validation and external validation cohorts (89.6 and 92.7%, respectively). Harmonization-driven textures were pivotal in LVH detection and HCM differentiation, while conventional textures and myocardial thickness were influential in CA and HHD differentiation. Conclusion This study showed that AI-enhanced echocardiographic feature analysis accurately identified LVH and its etiologies, highlighting the value of AI-driven texture and spatial analysis in LVH evaluation. Process of Echo feature-extraction AUC of Model in LVH differentiation
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