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

• Published in: Heart rhythm O2 Vol. 5; no. 9; pp. 644 - 654
• Main Authors: Bergquist, Jake A., Zenger, Brian, Brundage, James, MacLeod, Rob S., Bunch, T. Jared, Shah, Rashmee, Ye, Xiangyang, Lyons, Ann, Torre, Michael, Ranjan, Ravi, Tasdizen, Tolga, Steinberg, Benjamin A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2024; Elsevier
• Subjects: Artificial intelligence; Cardiovascular; Clinical; Electrocardiogram; Explainability; Heart failure; Machine learning; Heart failure; Artificial intelligence; Explainability; Electrocardiogram; Machine learning
• ISSN: 2666-5018; 2666-5018
• DOI: 10.1016/j.hroo.2024.07.009

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. 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. 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 LVEF classification and explored patient characteristics associated with inaccurate (false positive or false negative) LVEF prediction. We found that all of these network architectures produced LVEF detection area under the receiver-operating characteristic curve values above 0.9 (averaged over 5 instances per network), with the ResNet 18 network performing the highest (average area under the receiver-operating characteristic curve of 0.917). We also observed that some patient-specific characteristics such as race, sex, and presence of several comorbidities were associated with lower LVEF prediction performance. This demonstrates the ability of off-the-shelf AI-ML architectures to detect clinically useful information from ECGs with performance matching contemporary custom-build AI-ML architectures. We also highlighted the presence of possible biases in these AI-ML approaches in the context of patient characteristics. These findings should be considered in the pursuit of efficient and equitable deployment of AI-ML technologies moving forward.