Artificial Intelligence Applied to Electrocardiographic Images for Scalable Screening of Transthyretin Amyloid Cardiomyopathy

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.