Abstract
Convolutional neural networks (CNN), while effective in medical diagnostics, have shown concerning biases against underrepresented patient groups. In this study, we provide an in-depth exploration of these biases in the realm of image-based Alzheimer’s disease (AD) diagnosis using state-of-the-art CNNs, building upon and extending prior investigations. We dissect performance-based and calibration-based biases across patient subgroups differentiated by sex, ethnicity, age, educational qualifications, and APOE4 status. Our findings reveal substantial disparities in model performance and calibration, underscoring the challenges intersectional identities impose. Such biases highlight the importance of fairness analysis in fostering equitable AI applications in the AD domain. Appropriate mitigation actions should be carried out to ensure that, those who need it, receive healthcare attention independently from the subgroup they belong to.
Data used in preparation of this article was obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://www.adni-info.org/). The investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data, but did not participate in analysis or writing of this report.
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Acknowledgements
VND, JHG, and KL received funding from the EU’s Horizon 2020 research and innovation programme under Grant Agreement No 848158, EarlyCause, and MPAS under Grant Agreement No 952103, EuCanImage. AC received funding from Ministry of Universities and Recovery, Transformation and Resilience Plan, through UPC (Grant No 2021UPC-MS-67573). JHG is a Serra Húnter fellow.
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Dang, V.N. et al. (2023). Auditing Unfair Biases in CNN-Based Diagnosis of Alzheimer’s Disease. In: Wesarg, S., et al. Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging. CLIP EPIMI FAIMI 2023 2023 2023. Lecture Notes in Computer Science, vol 14242. Springer, Cham. https://doi.org/10.1007/978-3-031-45249-9_17
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