ArticleAlzheimer's research & therapy2023
Higher performance for women than men in MRI-based Alzheimer's disease detection.
Article in Alzheimer's research & therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- What Do Persistent Misclassifications Tell Us About Alzheimer's Disease Detection using Structural MRI?medRxiv : the preprint server for health sciences · 2026Article
- Decision Strategies in AI-Based Ensemble Models in Opportunistic Alzheimer's Detection from Structural MRI.Journal of imaging informatics in medicine · 2026Article
- Clinical impact of the Alzheimer's Disease Neuroimaging Initiative: A review of studies using ADNI data (2023 to June 2025).Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- Deep learning for Alzheimer's disease: advances in classification, segmentation, subtyping, and explainability.Biomedical engineering online · 2025Review
- Data Leakage in Deep Learning for Alzheimer's Disease Diagnosis: A Scoping Review of Methodological Rigor and Performance Inflation.Diagnostics (Basel, Switzerland) · 2025Review
- Addressing fairness issues in deep learning-based medical image analysis: a systematic review.NPJ digital medicine · 2024Review
- Methotrexate and the Risk of Dementia: A Two-Sample Mendelian Randomization Study.Neurology and therapy · 2024Article
- Sex differences in brain MRI using deep learning toward fairer healthcare outcomes.Frontiers in computational neuroscience · 2024Article
- Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- EEG recordings during visuo-attentive task reduce sex bias in Alzheimer's disease diagnosis.Alzheimer's & dementia (New York, N. Y.)Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
- Features predicting data exclusion in imaging studies of Alzheimer's disease.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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Authors and funding
7 authors.
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Abstract
introductionAlthough machine learning classifiers have been frequently used to detect Alzheimer's disease (AD) based on structural brain MRI data, potential bias with respect to sex and age has not yet been addressed. Here, we examine a state-of-the-art AD classifier for potential sex and age bias even in the case of balanced training data.
methodsBased on an age- and sex-balanced cohort of 432 subjects (306 healthy controls, 126 subjects with AD) extracted from the ADNI data base, we trained a convolutional neural network to detect AD in MRI brain scans and performed ten different random training-validation-test splits to increase robustness of the results. Classifier decisions for single subjects were explained using layer-wise relevance propagation.
resultsThe classifier performed significantly better for women (balanced accuracy [Formula: see text]) than for men ([Formula: see text]). No significant differences were found in clinical AD scores, ruling out a disparity in disease severity as a cause for the performance difference. Analysis of the explanations revealed a larger variance in regional brain areas for male subjects compared to female subjects. DISCUSSION: The identified sex differences cannot be attributed to an imbalanced training dataset and therefore point to the importance of examining and reporting classifier performance across population subgroups to increase transparency and algorithmic fairness. Collecting more data especially among underrepresented subgroups and balancing the dataset are important but do not always guarantee a fair outcome.
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