Sex-Related Performance Disparities in Convolutional Neural Networks for Imaging-Based Assessment of Coronary Atherosclerosis and Ischemic Heart Disease: A Systematic Review.

Sex-related disparities persist in the diagnosis and management of ischemic heart disease (IHD), raising concern that convolutional neural networks (CNNs) used in coronary imaging may perpetuate these inequities. This systematic review evaluated sex-related performance differences in CNN-based models using medical imaging to assess coronary atherosclerosis, coronary artery disease, or myocardial ischemia. A systematic literature search of PubMed, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library was conducted through June 7, 2026, in accordance with PRISMA guidelines. Peer-reviewed original studies were included if they evaluated sex-related performance of CNN-based models using medical imaging inputs, either by reporting performance metrics separately for women and men or by assessing sex as a determinant of model error, misclassification, calibration, or agreement. Nine studies met the inclusion criteria, covering noncontrast cardiac CT, coronary CT angiography, PET-CT, chest radiography, and SPECT-based approaches. Overall, model performance was generally similar between men and women across imaging modalities. Nevertheless, several studies identified clinically relevant sex-related discrepancies, including higher error rates in men for PET-CT calcium scoring and sex-related differences in error patterns during automated CCTA interpretation. Notably, one SPECT-based study showed that targeted augmentation of training data improved calibration and reduced false-positive predictions in women. While CNNs generally show comparable performance between sexes, subtle sex-related biases can persist, often driven by data imbalance and reference standard limitations. Sex-stratified evaluation and bias-mitigation strategies are essential to ensure equitable clinical implementation.