Prospects for AI-Enhanced ECG as a Unified Screening Tool for Cardiac and Non-Cardiac Conditions -- An Explorative Study in Emergency Care
This addresses the need for a unified screening tool in emergency care to improve diagnostic efficiency for both cardiac and non-cardiac conditions, though it is exploratory and incremental in extending existing methods.
The study tackled the problem of narrow focus in deep learning ECG analysis by developing a single model to predict a diverse range of cardiac and non-cardiac discharge diagnoses from a single ECG in emergency care, achieving reliable prediction for 253 ICD codes with AUROC scores exceeding 0.8.
Current deep learning algorithms designed for automatic ECG analysis have exhibited notable accuracy. However, akin to traditional electrocardiography, they tend to be narrowly focused and typically address a singular diagnostic condition. In this exploratory study, we specifically investigate the capability of a single model to predict a diverse range of both cardiac and non-cardiac discharge diagnoses based on a sole ECG collected in the emergency department. We find that 253, 81 cardiac, and 172 non-cardiac, ICD codes can be reliably predicted in the sense of exceeding an AUROC score of 0.8 in a statistically significant manner. This underscores the model's proficiency in handling a wide array of cardiac and non-cardiac diagnostic scenarios which demonstrates potential as a screening tool for diverse medical encounters.