Key result
Deep-learning echo model detects HCM with an AUC of ~0.98.
Why the study?
Hypertrophic cardiomyopathy remains underdiagnosed due to limited access to expert imaging, motivating development of a scalable deep-learning echocardiographic screening model.
Does a deep-learning-based echocardiographic model accurately detect hypertrophic cardiomyopathy using point-of-care ultrasound in hospitalized patients?
Observational (n=73,598)
No
Does a deep-learning-based echocardiographic model accurately detect hypertrophic cardiomyopathy using point-of-care ultrasound in hospitalized patients?
Effect estimate: AUC 0.982 (95% CI 0.966-0.993)
A deep-learning echocardiographic model accurately identifies hypertrophic cardiomyopathy and demonstrates feasibility for point-of-care ultrasound screening by non-cardiologists.
May enable POCUS HCM screening by non-cardiologists; hypothesis-generating and requires prospective validation before adoption.
Aims Hypertrophic cardiomyopathy (HCM) remains underdiagnosed due to limited access to expert imaging. We developed and validated a deep-learning (DL)-based echocardiographic model adaptable to point-of-care ultrasound (POCUS) for scalable HCM screening. Methods and results We retrospectively analysed 134 956 expert transthoracic echocardiograms (TTE) from 73 598 patients at Sheba Medical Center (2007–2022). A TTE-trained DL model integrating structural features and temporal motion patterns from parasternal long-axis and apical four-chamber views estimated HCM probability. Performance was evaluated in an independent test cohort and clinical subgroups. External validation used bedside POCUS studies from non-cardiologists with handheld devices. The test cohort included 12 096 patients with 119 confirmed HCM cases (prevalence 0.98%; median age 75 years, 57% male). HCM-positive patients showed increased expert TTE-measured septal (1.67 [1.5, 2.0] vs. 1.01 [0.9, 1.19] cm) and posterior wall thickness (1.1 [1.0, 1.3] vs. 0.9 [0.8, 1.0] cm) (P < 0.001). The model achieved excellent discrimination with an area under the curve of 0.982 (95% CI 0.966–0.993), sensitivity 88.2%, and specificity 97.3%, robust across subgroups. The POCUS cohort (n = 1047, median age 73 years, 55% male) represented multimorbid inpatients with 65 (6.2%) classified as screen-positive by the algorithm. These showed higher expert TTE-measured septal thickness (1.26 [1.07, 1.46] vs. 1.06 [0.9, 1.2] cm; 22% vs. 4% with IVS ≥1.5 cm; P ≤ 0.01). Among 49 (75%) POCUS-flagged positive patients with formal TTE and clinical data, 8 (16%) were confirmed by expert adjudication to have HCM. Specificity is limited by occasional confounding amyloidosis detection (4% of POCUS-flagged patients). Conclusion This DL-based model identifies HCM and demonstrates feasibility for POCUS screening, supporting earlier detection and broader diagnostic access.
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Karra et al. (2026) conducted an observational in Hypertrophic cardiomyopathy (n=73,598). Deep-learning-based echocardiographic model vs. Expert adjudication and clinical diagnosis was evaluated on Discrimination of hypertrophic cardiomyopathy (Area Under the Curve) (AUC 0.982, 95% CI 0.966-0.993). A deep-learning-based echocardiographic model accurately identified hypertrophic cardiomyopathy with an area under the curve of 0.982, a sensitivity of 88.2%, and a specificity of 97.3%.
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