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. 2025 Jul 3;14(13):4718.
doi: 10.3390/jcm14134718.

Advanced Diagnosis of Hypertrophic Cardiomyopathy with AI-ECG and Differences Based on Ethnicity and HCM Subtype

Myra Lewontin  1 , Emily Kaplan  1 , Kenneth C Bilchick  1 , Anita Barber  1 , Derek Bivona  1 , Christopher M Kramer  1 , Anna Parrish  1 , Karen McClean  1 , Matthew Thomas  1 , Allison Perry  1 , Kaitlyn Amos  1 , Michael Ayers  1
Affiliations

Advanced Diagnosis of Hypertrophic Cardiomyopathy with AI-ECG and Differences Based on Ethnicity and HCM Subtype

Myra Lewontin et al. J Clin Med. .

Abstract

Background/Objective: Hypertrophic cardiomyopathy (HCM) often presents later in the disease course, with frequent misdiagnoses and population-level underdiagnoses. Underserved patients may have even greater diagnostic delays. We aimed to test the hypothesis in a retrospective cohort that artificial intelligence analysis of ECGs (AI-ECG) could have afforded the opportunity for earlier diagnosis of HCM in one health system. Methods: We collected all available ECGs from patients referred to an HCM Center of Excellence over 15 years, both before and after HCM diagnosis. We applied AI-ECG to each ECG in a blinded fashion to predict the probability of HCM. We calculated the time between each patient's AI-ECG diagnosis and clinical diagnosis. We examined the sensitivity and specificity of AI-ECG for all patients, and by septal subtype and genetic test result. Results: 3499 ECGs were analyzed in 404 patients (age 56 ± 18 years, 52% female). AI-ECG correctly identified HCM in 155 patients with a sensitivity of 67%, specificity of 95%, positive predictive value of 94%, and a negative predictive value of 69%. The AUC was similar using mean probability from all ECGs for each patient (AUC 0.91 [0.88, 0.94]) or using probability from the first ECG (AUC 0.91 [0.87,0.93]). AI-ECG diagnosed 27 patients over 1 year before clinical diagnosis, and up to 16.3 years early. Black patients were more likely than White patients to have an AI-ECG diagnosis before a clinical diagnosis (p = 0.005). Conclusions: AI-ECG offers the potential for advanced HCM diagnosis. Differences in identification timing between subgroups highlight inequities in current care and show the potential of AI-ECG for the greatest benefit in underserved ethnic groups.

Keywords: artificial intelligence; electrocardiogram; hypertrophic cardiomyopathy; magnetic resonance imaging.

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Conflict of interest statement

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Receiver operating characteristic (ROC) plots for the detection of hypertrophic cardiomyopathy (HCM). The ROC plots are shown for the mean probability of HCM from all ECGs (A) and probability from the earliest ECG (B).
Figure 2
Figure 2
Receiver operating characteristic (ROC) plots for the detection of hypertrophic cardiomyopathy (HCM) in demographic subgroups. The ROC plots are shown for the probability from the earliest ECG in women (A) and men (B), non-white (C) and white individuals (D), and patients ≤ 60 years old (E) and >60 years old (F).
Figure 3
Figure 3
Distribution of lead times in patients with hypertrophic cardiomyopathy potentially identified by artificial intelligence prior to clinical diagnosis.
Figure 4
Figure 4
Distribution of artificial intelligence lead times in (A) Black and White patients; and (B) patients with and without a left ventricular outflow tract (LVOT) obstruction. Dashed vertical lines represent mean lead time.
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