Phenotyping of Patients with Age-Related Macular Degeneration Using Artificial Intelligence–Driven Biomarker Patterns in OCT

Purpose: To leverage artificial intelligence-based OCT analysis to classify age-related macular degeneration (AMD) images into distinct subgroups based on retinal layer and fluid biomarkers. Design: Retrospective analysis of a data set of retinal OCT images. Participants: Anonymized data from 157 patients with AMD and 58 healthy volunteers. Methods: This study analyzed OCT scans from patients with AMD and healthy volunteers to extract retinal layer thickness and fluid biomarkers by artificial intelligence-driven image segmentation. We performed dimensionality reduction using parametric Uniform Manifold Approximation and Projection, followed by k-means clustering to partition the continuous disease space into 9 subphenotypes for analysis. Statistical analyses included linear mixed-effects modeling, analysis of variance, and Fisher exact tests to assess biomarker differences, visual acuity (VA), and lesion size across clusters. We mapped International Classification of Diseases, 10th Revision (ICD-10) codes to validate disease staging. Main Outcome Measures: We examined the potential of artificial intelligence-driven biomarker patterns based on the comparison to traditional clinical measures such as VA, lesion size, and ICD-10 codes for different disease stages. Results: By dividing the continuous landscape into 9 regions for interpretability, we observed that each cluster was characterized by a distinct biomarker pattern. Clusters exhibited outer retinal thinning of varying degrees and showed significant differences in inner retinal layers, choroidal thickness, subretinal pigment epithelium material, and fluid presence compared with a healthy cohort, suggesting diverse AMD subtypes beyond simple characterization by VA and atrophic lesion size alone. The distribution of ICD-10 codes further supported the interpretation of the AMD landscape as a representation of AMD progression. Conclusions: This study reveals a continuous AMD landscape based on OCT-derived biomarkers, suggesting the existence of subphenotypes beyond traditional staging methods. The findings further highlight the limitations of fundus-based disease classification and support the role of OCT scans in personalized disease management. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.