Machine learning derived retinal pigment score from ophthalmic imaging shows ethnicity is not biology

Anand E. Rajesh, Abraham Olvera-Barrios, Alasdair N. Warwick, Yue Wu, Kelsey V. Stuart, Mahantesh I. Biradar, Chuin Ying Ung, Anthony P. Khawaja, Robert Luben, Paul J. Foster, Charles R. Cleland, William U. Makupa, Alastair K. Denniston, Matthew J. Burton, Andrew Bastawrous, Pearse A. Keane, Mark A. Chia, Angus W. Turner, Cecilia S. Lee, Adnan Tufail, Aaron Y. Lee and Catherine Egan ()
Additional contact information
Anand E. Rajesh: University of Washington
Abraham Olvera-Barrios: Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology
Alasdair N. Warwick: Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology
Yue Wu: University of Washington
Kelsey V. Stuart: Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology
Mahantesh I. Biradar: Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology
Chuin Ying Ung: Guy’s and St Thomas’ NHS Foundation Trust
Anthony P. Khawaja: Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology
Robert Luben: Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology
Paul J. Foster: Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology
Charles R. Cleland: London School of Hygiene & Tropical Medicine
William U. Makupa: Kilimanjaro Christian Medical Centre
Alastair K. Denniston: NIHR Birmingham Biomedical Research Centre
Matthew J. Burton: Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology
Andrew Bastawrous: Kilimanjaro Christian Medical Centre
Pearse A. Keane: Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology
Mark A. Chia: Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology
Angus W. Turner: University of Western Australia
Cecilia S. Lee: University of Washington
Adnan Tufail: Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology
Aaron Y. Lee: University of Washington
Catherine Egan: Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as a surrogate marker for biological variability. We derived a continuous, measured metric, the retinal pigment score (RPS), that quantifies the degree of pigmentation from a colour fundus photograph of the eye. RPS was validated using two large epidemiological studies with demographic and genetic data (UK Biobank and EPIC-Norfolk Study) and reproduced in a Tanzanian, an Australian, and a Chinese dataset. A genome-wide association study (GWAS) of RPS from UK Biobank identified 20 loci with known associations with skin, iris and hair pigmentation, of which eight were replicated in the EPIC-Norfolk cohort. There was a strong association between RPS and ethnicity, however, there was substantial overlap between each ethnicity and the respective distributions of RPS scores. RPS decouples traditional demographic variables from clinical imaging characteristics. RPS may serve as a useful metric to quantify the diversity of the training, validation, and testing datasets used in the development of AI algorithms to ensure adequate inclusion and explainability of the model performance, critical in evaluating all currently deployed AI models. The code to derive RPS is publicly available at: https://github.com/uw-biomedical-ml/retinal-pigmentation-score .

Date: 2025
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DOI: 10.1038/s41467-024-55198-7

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