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While utilizing machine learning models, one of the most crucial aspects ishow bias and fairness affect model outcomes for diverse demographics. Thisbecomes especially relevant in the context of machine learning for medicalimaging applications as these models are increasingly being used for diagnosisand treatment planning. In this paper, we study biases related to sex whendeveloping a machine learning model based on brain magnetic resonance images(MRI). We investigate the effects of sex by performing brain age predictionconsidering different experimental designs: model trained using only femalesubjects, only male subjects and a balanced dataset. We also perform evaluationon multiple MRI datasets (Calgary-Campinas(CC359) and CamCAN) to assess thegeneralization capability of the proposed models. We found disparities in theperformance of brain age prediction models when trained on distinct sexsubgroups and datasets, in both final predictions and decision making (assessedusing interpretability models). Our results demonstrated variations in modelgeneralizability across sex-specific subgroups, suggesting potential biases inmodels trained on unbalanced datasets. This underlines the critical role ofcareful experimental design in generating fair and reliable outcomes.

Studying the Effects of Sex-related Differences on Brain Age Prediction using brain MR Imaging