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ORIGINAL RESEARCH article

Front. Comput. Neurosci.
Volume 18 - 2024 | doi: 10.3389/fncom.2024.1452457
This article is part of the Research Topic Deep Learning and Neuroimage Processing in Understanding Neurological Diseases View all 5 articles

Sex Differences in Brain MRI using Deep Learning towards Fairer Healthcare Outcomes

Provisionally accepted
Mahsa Dibaji Mahsa Dibaji 1*Johanna Ospel Johanna Ospel 2Roberto Souza Roberto Souza 1Mariana Bento Mariana Bento 1,3
  • 1 Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada
  • 2 Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
  • 3 Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada

The final, formatted version of the article will be published soon.

    This study leverages deep learning to analyze sex differences in brain MRI data, aiming to further advance fairness in medical imaging. We employed 3D T1-weighted Magnetic Resonance images from four diverse datasets: Calgary-Campinas-359, OASIS-3, Alzheimer's Disease Neuroimaging Initiative, and Cambridge Centre for Ageing and Neuroscience, ensuring a balanced representation of sexes and a broad demographic scope. Our methodology focused on minimal preprocessing to preserve the integrity of brain structures, utilizing a Convolutional Neural Network model for sex classification. The model achieved an accuracy of 87% on the test set without employing total intracranial volume (TIV) adjustment techniques. We observed that while the model exhibited biases at extreme brain sizes, it performed with less bias when the TIV distributions overlapped more. Saliency maps were used to identify brain regions significant in sex differentiation, revealing that certain supratentorial and infratentorial regions were important for predictions. Furthermore, our interdisciplinary team, comprising machine learning specialists and a radiologist, ensured diverse perspectives in validating the results. The detailed investigation of sex differences in brain MRI in this study, highlighted by the sex differences map, offers valuable insights into sex-specific aspects of medical imaging and could aid in developing sex-based bias mitigation strategies, contributing to the future development of fair AI algorithms. Awareness of the brain's differences between sexes enables more equitable AI predictions, promoting fairness in healthcare outcomes. Our code and saliency maps are available at https://github.com/mahsadibaji/sex-differences-brain-dl.

    Keywords: sex differences, brain MRI, deep learning, Explainable AI, Healthcare Fairness, Convolutional Neural Networks, Neuroimaging

    Received: 20 Jun 2024; Accepted: 10 Sep 2024.

    Copyright: © 2024 Dibaji, Ospel, Souza and Bento. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mahsa Dibaji, Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 4V8, Alberta, Canada

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.