| Title: | Assessment of demographic bias in retinal age prediction machine learning models |
| Journal: | Frontiers in Artificial Intelligence |
| Published: | 10 Oct 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41141904/ |
| DOI: | https://doi.org/10.3389/frai.2025.1653153 |
| URL: | https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1653153/pdf |
Publication 19216
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Abstract
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.</p>
4 Authors
- Christopher Nielsen
- Emma A. M. Stanley
- Matthias Wilms
- Nils D. Forkert
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