Ethnicity is not biology: retinal pigment score to evaluate biological variability from ophthalmic imaging using machine learning.

Vision loss due to retinal disease is a global problem as populations age and diabetes becomes increasingly prevalent. AI algorithms developed for efficient diagnosis of diabetic retinopathy and age-related macular degeneration rely on large imaging datasets collected from clinical practice. A substantial proportion (more than 80%) of publicly available retinal imaging datasets lack data on participant demographic characteristics. Some ethnic groups are noticeably underrepresented in medical research.Previous findings in dermatology suggest that AI algorithms can show reduced performance on darker skin tones. Similar biases may exist in retinal imaging, where retinal colour has been shown to affect disease detection. We introduce the Retinal Pigment Score (RPS), a measure of retinal pigmentation from digital fundus photographs. This score showed strong, reproducible associations with genetic variants related to skin, eye, and hair colour. Additionally, we identify three genetic loci potentially unique to retinal pigmentation, which warrant further investigation. The RPS provides an accurate and objective metric to describe the biological variability of the retina directly derived from an image. The RPS method represents a valuable metric with importance to harness the detailed information of ophthalmic fundus imaging. Its application implies potential benefits, such as improved accuracy and inclusivity, over human-created sociodemographic classifications used in dataset compilation and in the processes of developing and validating models. The RPS could decouple the distinct social and political categorical constructs of race and ethnicity from image analysis. It is poised to both accurately describe the diversity of a population study dataset or an algorithm training dataset, and for investigate algorithmic bias by assessing outcomes.Further work is needed to characterise RPS across different populations, considering individual ocular factors and different camera types. The development of standard reporting practices using RPS for studies employing colour fundus photography is also critical.