Unraveling Gender Fairness Analysis in Deep Learning Prediction of Alzheimer’s Disease

Early prediction of Alzheimer's Disease (AD) is widely addressed within dementia research, where the use of deep learning (DL) has received increased popularity across disciplines within healthcare, particularly medical imaging. While unintentional bias in data collection and study design in the field of DL has been a highly debated topic, systematic analysis of the performance of DL models for female and male sub-groups is rarely considered during development. Recently, some studies have shed light on the importance of analyzing classification results for uneven performance with respect to protected characteristics like gender and ethnicity. However, the results are inconsistent and heterogeneous, especially for gender fairness analysis in DL classification of AD. In our work, we seek to present a standardized analysis of gender fairness in the AD classification scenario and discuss the lack of a benchmark to evaluate skewed performance with respect to gender for DL and AD studies. For that purpose, we train two deep neural networks to classify between cognitively normal (CN) and AD subjects from magnetic resonance imaging (MRI) and analyze the classification ability for male and female sub-groups separately. Both networks achieve high classification performance at testing time with an area under the receiver operating characteristics (ROC) curve (AUC) of 1) 0.97±0.05 and 2) 0.96±0.03. Contrary to other studies, our sub-group analyses do not show significant differences in performance between male and female subgroups.