Background: Transthyretin amyloid cardiomyopathy (ATTR-CM) remains largely under-recognized, under-diagnosed, and under-treated. We hypothesized that the myocardial remodeling of ATTR-CM may be detectable through artificial intelligence (AI) applied to 12-lead electrocardiographic (ECG) images. Methods: Across 5 hospitals of a large U.S.-based hospital system, we identified patients with ATTR-CM, defined by the presence of a positive nuclear scan with an approved bone radiotracer or pharmacotherapy with an approved transthyretin stabilizer between 2015 and the first half of 2023. The development cohort consisted of 1,011 ECGs from 234 patients (age 79 [IQR:70-85] years, n=176 [17.4%] women), who were age- and sex-matched in a 10:1 ratio to 10,110 ECGs from 10,110 controls (age 79 [IQR:70-85] years, n=1,800 [17.7%] female). A convolutional neural network (CNN) pre-trained using a bio-contrastive pretext on ECGs before 2015 was fine-tuned for ATTR-CM using 5-fold cross-validation and subsequently tested in an independent set of cases (139 ECGs in 47 patients; age 80 [75-86] years, n=44 (31.7% women)) and matched controls (1390 ECGs and patients) from the second half of 2023. Results: The AUROC (area under the receiver operating characteristic curve) of the AI-ECG model for discriminating ATTR-CM in the leave-out, temporally distinct dataset was 0.906 [95%CI: 0.89-0.94] (A), with a sensitivity of 0.85 [95%CI: 0.79-0.91] and specificity 0.80 [95%CI 0.78-0.82]. Conclusions: We demonstrate that AI applied directly to ECG images represents a promising and scalable approach for the screening of ATTR-CM.

V.S. is a coinventor of 63/346, 610 and 63/484,426, and a co-founder of Ensight-AI, Inc. E.K.O. is an academic co-founder of Evidence2Health LLC, a co-inventor in patent applications (18/813,882, 17/720,068, 63/619,241, 63/177,117, 63/580,137, 63/606,203, 63/562,335, US11948230B2), has been a consultant for Caristo Diagnostics Ltd and Ensight-AI Inc, and has received royalty fees from technology licensed through the University of Oxford, outside the submitted work. R.K. is an Associate Editor of JAMA and receives research support, through Yale, from the Blavatnik Foundation, Bristol-Myers Squibb, Novo Nordisk, and BridgeBio. He is a coinventor of U.S. Provisional Patent Applications 63/177,117, 63/428,569, 63/346,610, 63/484,426, 63/508,315, 63/580,137, 63/606,203, 63/562,335, and a co-founder of Ensight-AI, Inc and Evidence2Health, LLC.

The authors acknowledge support from the National Heart, Lung, And Blood Institute of the National Institutes of Health (under award numbers F32HL170592 to Dr Oikonomou, and R01HL167858 and K23HL153775 to Dr Khera), the National Institute on Aging of the National Institutes of Health (under award number R01AG089981 to Dr Khera), and the Doris Duke Charitable Foundation (under award number 2022060 to Dr Khera).

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The Yale Institutional Review Board approved the study protocol and waived the need for informed consent as the study represents a secondary analysis of existing data. Patients who opted out of research studies at the Yale New Haven Hospital (YNHH) were excluded.

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An online version of the model is publicly available for research use at https://www.cards-lab.org/ecgvision-attrcm. This web application represents a prototype of the eventual application of the model, with instructions for required image standards and a version that demonstrates an automated image standardization pipeline.