While utilizing machine learning models, one of the most crucial aspects is how bias and fairness affect model outcomes for diverse demographics. This becomes especially relevant in the context of machine learning for medical imaging applications as these models are increasingly being used for diagnosis and treatment planning. In this paper, we study biases related to sex when developing a machine learning model based on brain magnetic resonance images (MRI). We investigate the effects of sex by performing brain age prediction considering different experimental designs: model trained using only female subjects, only male subjects and a balanced dataset. We also perform evaluation on multiple MRI datasets (Calgary-Campinas(CC359) and CamCAN) to assess the generalization capability of the proposed models. We found disparities in the performance of brain age prediction models when trained on distinct sex subgroups and datasets, in both final predictions and decision making (assessed using interpretability models). Our results demonstrated variations in model generalizability across sex-specific subgroups, suggesting potential biases in models trained on unbalanced datasets. This underlines the critical role of careful experimental design in generating fair and reliable outcomes.

利用机器学习模型时，如何处理偏差和公平性对不同人群的模型结果产生的影响是至关重要的。本文研究了在基于脑磁共振图像（MRI）开发机器学习模型时与性别相关的偏差问题，通过使用仅女性、仅男性和平衡数据集进行的脑龄预测实验设计，在多个MRI数据集上（Calgary-Campinas和CamCAN）对所提出的模型泛化能力进行了评估，并发现了在不同性别子组和数据集上训练的模型在脑龄预测的性能和决策（使用可解释性模型评估）方面存在差异，结果表明在不平衡数据集上训练的模型的泛化能力在性别特定子组之间存在差异，突出了谨慎实验设计在产生公平可靠结果中的关键作用。

研究性别差异对脑年龄预测的影响：利用脑磁共振成像