An Artificial Intelligence Method for Phenotyping of OCT-Derived Thickness Maps Using Unsupervised and Self-supervised Deep Learning.

The objective of this study is to enhance the understanding of ophthalmic disease physiology and genetic architecture through the analysis of optical coherence tomography (OCT) images using artificial intelligence (AI). We introduce a novel AI methodology that addresses the challenge of transferring OCT phenotypes across datasets. The approach employs unsupervised and self-supervised learning techniques to phenotype and cluster OCT-derived retinal layer thicknesses, using glaucoma as a model disease. Our method integrates deep learning, manifold learning, and a Gaussian mixture model to identify distinct phenotypic clusters. Across two large datasets-Massachusetts Eye and Ear (MEE; 18,985 images) and UK Biobank (UKBB; 86,115 images)-the model identified 9 to 11 phenotypic clusters per retinal layer, which were clinically meaningful and showed consistent patterns across datasets. Pearson correlation analysis confirmed the intra-cluster similarity, with within-cluster correlations exceeding inter-cluster correlations (Supplemental Figs. 4-5). Clinical associations showed that specific phenotypes correlated strongly with glaucoma severity markers, including visual field mean deviation (e.g., 12.57±10.1 for phenotype 6) and cup-to-disc ratio (e.g., 0.694±0.237). These results validate the robustness of the model and its ability to generalize across datasets. This work advances OCT-based phenotyping, enabling phenotype transfer and facilitating translational research in disease mechanisms and genetic discovery.