Prospects for AI-Enhanced ECG as a Unified Screening Tool for Cardiac and Non-Cardiac Conditions -- An Explorative Study in Emergency Care

Nils Strodthoff, Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp
12/18/2023
eess.SPcs.LGstat.ML

No official code found. Best implementation: AI4HealthUOL/ECG-MIMIC (61 stars, MIT, last commit 2025-05-07).

Abstract

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.

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AI4HealthUOL/ECG-MIMICCommunity100%
MIT

Repository for the paper 'Prospects for AI-Enhanced ECG as a Unified Screening Tool for Cardiac and Non-Cardiac Conditions -- An Explorative Study in Emergency Care'.

619C, C++Nov 12, 20231 years agoMIT
cardiologydeep-learningecg-classificationhealthcaretime-series-classification

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Cite this paper

@article{strodthoff2023prospects,
  title  = {Prospects for AI-Enhanced ECG as a Unified Screening Tool for Cardiac and Non-Cardiac Conditions -- An Explorative Study in Emergency Care},
  author = {Nils Strodthoff and Juan Miguel Lopez Alcaraz and Wilhelm Haverkamp},
  year   = {2023},
  eprint = {2312.11050},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.11050v2}
}

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