biostudies-literatureUnknown5(9)Bergquist JA<h4>Background</h4>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.<h4>Objective</h4>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.<h4>Methods</h4>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 LHeart rhythm O2644-654https://www.ebi.ac.uk/biostudies/studies/S-EPMC11524967biostudies-literaturePerformance of off-the-shelf machine learning architectures and biases in low left ventricular ejection fraction detection.PMC11524967Bergquist JAMacLeod RSBrundage JTorre MShah RBunch TJSteinberg BALyons AZenger BYe XTasdizen TRanjan RfalsePerformance of off-the-shelf machine learning architectures and biases in low left ventricular ejection fraction detection.<h4>Background</h4>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.<h4>Objective</h4>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.<h4>Methods</h4>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 L2024-01-01T00:00:00Z2024 Sep2025-04-26T16:59:48.799Z2025-04-06T15:25:18.325ZS-EPMC115249673949391110.1016/j.hroo.2024.07.009