Advances in computer-aided analyses, including deep learning (DL), are transforming medical imaging by enabling automated disease risk predictions and aiding clinical interpretation. However, DL's outputs and latent variables (LVs) often lack interpretability, impeding clinical trust and biological insight. In this study, we evaluated RETFound, a foundation model for retinal images, using a dataset annotated with clinically interpretable tangible image features (TIFs). Our findings revealed that individual LVs poorly represent complex TIFs but achieve higher accuracy when combined linearly. Fine-tuning RETFound to predict TIFs, providing "deep TIFs" provided better, but far from perfect surrogates, highlighting the limitations of DL approaches to fully characterise retinal images. Yet, our genetic analyses showed that deep TIFs exhibit heritability comparable to or exceeding measured TIFs but highlighted non-genetic variability in LVs. While measured and deep TIFs, as well as LVs, showed overlapping genetic and disease associations, their complementarity enhances prediction models. Notably, deep TIFs excelled in ocular disease prediction, emphasising their potential to refine retinal diagnostics and bridge gaps in conventional assessments of vascular morphology.

The authors have declared no competing interest.

This work was supported by the Swiss National Science Foundation grant no. CRSII5_209510 (see https://data.snf.ch/grants/grant/209510).

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The study used only openly available human data that were originally located at: https://www.ukbiobank.ac.uk/

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Genetic and phenotypic data are available upon application to the UK Biobank. GWAS sumstats and deep-learning models are available upon reasonable request.

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