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Phenotyping of Patients with Age-Related Macular Degeneration Using Artificial Intelligence-Driven Biomarker Patterns
Adrian Kaufmann1, Joseph Blair1, Romina Lasagni Vitar1
1Ikerian AG, Bern, Switzerland.
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.
