BRIEF RESEARCH REPORT article
Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/frai.2025.1653153
Assessment of demographic bias in retinal age prediction machine learning models
Provisionally accepted
- University of Calgary, Calgary, Canada
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The retinal age gap, defined as the difference between the predicted retinal age and chronological age, is an emerging biomarker for many eye conditions and even non-ocular diseases. Machine learning (ML) models are commonly used for retinal age prediction. However, biases in ML models may lead to unfair predictions for some demographic groups, potentially exacerbating health disparities. This retrospective cross-sectional study evaluated demographic biases related to sex and ethnicity in retinal age prediction models using retinal imaging data (color fundus photography [CFP], optical coherence tomography [OCT], and combined CFP+OCT) from 9,668 healthy individuals (mean age 56.8 years; 52% female) in the UK Biobank. The RETFound foundation model was fine-tuned to predict retinal age, and bias was assessed by comparing mean absolute error (MAE) and retinal age gaps across demographic groups. The combined CFP+OCT model achieved the lowest MAE (3.01 years), outperforming CFP-only (3.40 years)and OCT-only (4.37 years) models. Significant sex differences were observed only in the CFP model (p<0.001), while significant ethnicity differences appeared only in the OCT model (p<0.001). No significant sex/ethnicity differences were observed in the combined model. These results demonstrate that retinal age prediction models can exhibit biases, and that these biases, along with model accuracy, are influenced by the choice of imaging modality (CFP, OCT, or combined). Identifying and addressing sources of bias is essential for safe and reliable clinical implementation. Our results emphasize the importance of comprehensive bias assessments and prospective validation, ensuring that advances in machine learning and artificial intelligence benefit all patient populations.
Keywords: retinal age prediction, machine learning, Bias, Multimodal Imaging, retinal imaging
Received: 24 Jun 2025; Accepted: 26 Aug 2025.
Copyright: © 2025 Nielsen, Stanley, Wilms and Forkert. 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: Christopher Nielsen, University of Calgary, Calgary, 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.