Abstract 13912: ECG-DualNet: A MultiLabel Model for Automated Diagnosis on Both Electrocardiographic Signals and Printed 12-lead Electrocardiograms

Abstract

Background: Electrocardiograms (ECGs) are frequently stored as printed images, but tools developed for artificial intelligence (AI)-based automated diagnosis and patient phenotyping exclusively rely on signal data, limiting their application. Methods: We developed a model that simultaneously incorporates both signal and image-based ECG data in clinical diagnosis and patient phenotyping. We used all 12-lead ECGs from a large deidentified database from Brazil, with signals transformed to a standard 12-lead ECG image. Both ECG signals and images were randomly subset into training, validation and test sets (90%-5%-5%). We used 6 physician-defined clinical labels spanning rhythm and conduction disorders as labels (atrial fibrillation [AF], sinus tachy/bradycardia [ST/SB], 1 st degree AV block [1dAVB], RBBB, LBBB). In addition, ECG-based identification of patient sex was assessed as a higher dimensional label. Convolutional Neural Networks were constructed with custom Inception net architecture for signals and EfficientNet-B3 for images. Model performance was evaluated in the holdout test set and US-based external validation dataset, PTB-XL. Results: The derivation dataset included 2,228,236 ECGs in 1,506,112 individuals (mean age 54y, 60% female). The ECG-DualNet model successfully labeled all 6 clinical diagnoses and patient gender with a high model performance (AUC of .81-.99 for signal and .92-.99 for images, F1 scores, .31-.78, and .49-82) in the 111,412 held-out test ECGs (Fig A-B). In external validation, with 21,785 ECGs, the model had excellent performance on both ECG signals and images (AUCs .75-.99 and .89-.99, F1 scores, .25-82, and .44-.86) (Fig C-D). Conclusion: A novel multilabel dual signal and image-based model achieved high performance in both clinical diagnosis and higher dimensional phenotypic labels across distinct ECG sources, with the ability to standardize automated diagnosis and patient phenotyping across ECG data formats.