ECG for high-throughput screening of multiple diseases: Proof-of-concept using multi-diagnosis deep learning from population-based datasets

TL;DR

This study demonstrates that deep learning models can analyze large-scale ECG data to accurately diagnose a wide range of diseases beyond cardiac conditions, using a population-based dataset of over 250,000 patients.

Contribution

It introduces a proof-of-concept for multi-diagnosis deep learning from ECGs, covering 128 diseases across various categories in a large, diverse dataset.

Findings

128 diseases identified with strong performance

68 disease categories accurately diagnosed

ECG-based multi-disease screening is feasible

Abstract

Electrocardiogram (ECG) abnormalities are linked to cardiovascular diseases, but may also occur in other non-cardiovascular conditions such as mental, neurological, metabolic and infectious conditions. However, most of the recent success of deep learning (DL) based diagnostic predictions in selected patient cohorts have been limited to a small set of cardiac diseases. In this study, we use a population-based dataset of >250,000 patients with >1000 medical conditions and >2 million ECGs to identify a wide range of diseases that could be accurately diagnosed from the patient's first in-hospital ECG. Our DL models uncovered 128 diseases and 68 disease categories with strong discriminative performance.