Why the study?
Tools using deep learning on ECG data may bridge the applicability of ECGs and echocardiography to screen, diagnose, predict, and prioritize LV and RV dysfunction.
Can deep-learning algorithms applied to ECG data identify right and left ventricular dysfunction?
Can deep-learning algorithms applied to ECG data identify right and left ventricular dysfunction?
Deep learning applied to ECGs may serve as an inexpensive tool to screen and diagnose both left and right ventricular dysfunction.
May support low-cost ventricular dysfunction screening; leaves open prospective validation before clinical adoption.
DL on ECG data can be used to create inexpensive screening, diagnostic, and predictive tools for both LV and RV dysfunction. Such tools may bridge the applicability of ECGs and echocardiography and enable prioritization of patients for further interventions for either sided failure progressing to biventricular disease.
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Vaid et al. (2021) studied this question.
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