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AI reads heart tracings to help identify hypertrophic cardiomyopathy types

Tests AI reading ECG heart tracings to classify hypertrophic cardiomyopathy in adults.

Plain-language title generated from the registry record
Registry title: AI-Enabled Diagnosis and Prognosis of Hypertrophic Cardiomyopathy

Key facts

Where
Hangzhou, ChinaSee the site

Where in China

  • Second Affiliated Hospital, Zhejiang University School of MedicineHangzhou, ChinaTaking people

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Trial details

Who runs it
Second Affiliated Hospital, Zhejiang University, School of Medicine
Trial code
NCT07263204
Record updated
4 December 2025
Original description
By harnessing artificial intelligence to decode the 12-lead electrocardiogram, the project will enable precise ECG-based phenotyping of hypertrophic cardiomyopathy-accurately classifying septal, apical, and other morphologic subtypes-while simultaneously differentiating HCM from hypertensive heart disease, aortic stenosis, and other phenocopy disorders.Read full original descriptionShow less

Precision Diagnosis and Prognostic Prediction of Hypertrophic Cardiomyopathy Using Artificial Intelligence: A Multicenter Study

By harnessing artificial intelligence to decode the 12-lead electrocardiogram, the project will enable precise ECG-based phenotyping of hypertrophic cardiomyopathy-accurately classifying septal, apical, and other morphologic subtypes-while simultaneously differentiating HCM from hypertensive heart disease, aortic stenosis, and other phenocopy disorders.

Inclusion: Adults aged ≥ 18 years; HCM cohort: Adults diagnosed with hypertrophic cardiomyopathy in accordance with the *2023 Chinese Guidelines for the Diagnosis and Treatment of Hypertrophic Cardiomyopathy in Adults*; HCM phenocopy cohort: Adults with an LV wall thickness ≥ 13 mm at any site on echocardiography; Healthy-control cohort: Adults with no history of cardiac disease and no evidence of myocardial hypertrophy on echocardiography.

Exclusion: Patients from whom analyzable ECG data cannot be obtained.

ClinicalTrials.gov NCT07263204