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Operationalizing fairness in medical AI adoption: Detection of early Alzheimer’s Disease with 2D CNN

Heising, L.M.; Angelopoulos, S.

Operationalizing fairness in medical AI adoption: Detection of early Alzheimer’s Disease with 2D CNN Thumbnail


Authors

L.M. Heising



Abstract

Objectives: To operationalize fairness in the adoption of medical artificial intelligence (AI) algorithms in terms of access to computational resources, the proposed approach is based on a two-dimensional (2D) Convolutional Neural Networks (CNN), which provides a faster, cheaper, and accurate-enough detection of early Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI), without the need for use of large training datasets or costly high-performance computing (HPC) infrastructures. Methods: The standardized ADNI datasets are used for the proposed model, with additional skull stripping, using the BET2 approach. The 2D CNN architecture is based on LeNet-5, the LReLU activation function and a Sigmoid function were used, and batch normalization was added after every convolutional layer to stabilize the learning process. The model was optimized by manually tuning all its hyperparameters. Results: The model was evaluated in terms of accuracy, recall, precision, and f1-score. The results demonstrate that the model predicted MCI with an accuracy of .735, passing the random guessing baseline of .521, and predicted AD with an accuracy of .837, passing the random guessing baseline of .536. Discussion: The proposed approach can assist clinicians in the early diagnosis of AD and MCI, with high-enough accuracy, based on relatively smaller datasets, and without the need of HPC infrastructures. Such an approach can alleviate disparities and operationalize fairness in the adoption of medical algorithms. Conclusion: Medical AI algorithms should not be focused solely on accuracy but should also be evaluated with respect to how they might impact disparities and operationalize fairness in their adoption.

Citation

Heising, L., & Angelopoulos, S. (2022). Operationalizing fairness in medical AI adoption: Detection of early Alzheimer’s Disease with 2D CNN. BMJ Health & Care Informatics, 29, Article e100485. https://doi.org/10.1136/bmjhci-2021-100485

Journal Article Type Article
Acceptance Date Jan 9, 2022
Online Publication Date Apr 27, 2022
Publication Date Apr 27, 2022
Deposit Date Jan 11, 2022
Publicly Available Date Jan 11, 2022
Journal BMJ Health & Care Informatics
Publisher BMJ Publishing Group
Peer Reviewed Peer Reviewed
Volume 29
Article Number e100485
DOI https://doi.org/10.1136/bmjhci-2021-100485
Public URL https://durham-repository.worktribe.com/output/1219153

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Accepted Journal Article (351 Kb)
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Publisher Licence URL
http://creativecommons.org/licenses/by-nc/4.0/

Copyright Statement
© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.








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