# Deep-learningmodel detecteert hypertrofische cardiomyopathie met point-of-care echocardiografie

*geplaatst 2026-09-18 · Hartfalen · European heart journal. Digital health · doi 10.1093/ehjdh/ztag140 · https://hartvaat.nl/2026/10/01/deep-learningmodel-detecteert-hypertrofische-cardiomyopathie-met-point-of-care-e/*

Onderzoekers ontwikkelden en valideerden een deep-learningmodel voor het screenen op hypertrofische cardiomyopathie (HCM) met point-of-care echocardiografie (POCUS). In een retrospectieve analyse van bijna 135.000 TTE's bereikte het model een AUC van 0,982 (sensitiviteit 88,2%, specificiteit 97,3%). Bij externe validatie op 1.047 POCUS-scans van niet-cardiologen bleek 6,2% screenpositief; van de 49 patiënten met follow-up TTE had 16% daadwerkelijk HCM. Het model is veelbelovend voor bredere diagnostische toegang, maar de lagere bevestigingsgraad en overlap met amyloidose vragen om prospectieve validatie en klinische interpretatie.

## English: Point-of-care echocardiography screening for hypertrophic cardiomyopathy using automated deep-learning analysis.

Researchers developed and validated a deep-learning model for hypertrophic cardiomyopathy (HCM) screening using point-of-care ultrasound (POCUS). Trained on nearly 135,000 expert echocardiograms, the algorithm achieved an AUC of 0.982 (sensitivity 88.2%, specificity 97.3%). In an external POCUS cohort of 1,047 scans performed by non-cardiologists, 6.2% were flagged as positive; among 49 patients with formal follow-up echocardiography, 16% had confirmed HCM. While promising for expanding diagnostic access, the modest confirmation rate and potential overlap with cardiac amyloidosis highlight the need for prospective validation and careful clinical interpretation before routine implementation.

## Abstract (original, from the publication)

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.

Auteurs: Nour Karra, Yarin Klempfner, Viana Copeland, Michael Fiman, Harel Doitch, Roei Merin, Robert Klempfner, Ehud Schwammenthal, Michael Arad, Elad Maor

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Bron: European heart journal. Digital health, https://doi.org/10.1093/ehjdh/ztag140. Bijgewerkt 2026-09-12T01:01:33Z. Citeer vrij, met bronvermelding en een link naar hartvaat.nl (de url van het record). Samenvattingen zijn redactioneel werk van HartVaat; de oorspronkelijke publicaties blijven van hun uitgevers (doi). Geen medisch advies.