Artificial intelligence applied to electrocardiographic images for scalable screening of cardiac amyloidosis

Abstract Background Transthyretin amyloid cardiomyopathy (ATTR-CM) remains largely under-recognized, under-diagnosed, and under-treated. We hypothesized that the myocardial remodeling seen in ATTR-CM may be detectable through artificial intelligence (AI) applied to 12-lead electrocardiographic (ECG) images. Purpose To develop an AI-ECG algorithm that can detect ATTR-CM from images of 12-lead electrocardiograms. 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 bone scintigraphy scan 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], 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 cross-validation set was 0.90 [95%CI: 0.89-0.91], and in the leave-out, temporally distinct dataset was 0.915 [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]. Representative saliency maps are shown in panel B. In simulations, the positive predictive value ranged from 0.12 [95%CI: 0.10-0.13] at a 3% prevalence level to 0.59 [95%CI: 0.56-0.62] at 25% prevalence. Conclusions We demonstrate that AI applied directly to ECG images represents a promising approach for the screening of ATTR-CM.