Performance of off-the-shelf machine learning architectures and biases in low left ventricular ejection fraction detection.

ABSTRACT:

Background

Artificial intelligence-machine learning (AI-ML) has demonstrated the ability to extract clinically useful information from electrocardiograms (ECGs) not available using traditional interpretation methods. There exists an extensive body of AI-ML research in fields outside of cardiology including several open-source AI-ML architectures that can be translated to new problems in an "off-the-shelf" manner.

Objective

We sought to address the limited investigation of which if any of these off-the-shelf architectures could be useful in ECG analysis as well as how and when these AI-ML approaches fail.

Methods

We applied 6 off-the-shelf AI-ML architectures to detect low left ventricular ejection fraction (LVEF) in a cohort of ECGs from 24,868 patients. We assessed L

SUBMITTER: Bergquist JA 

PROVIDER: S-EPMC11524967 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature