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An Artificial Intelligence Method for Phenotyping of OCT-Derived Thickness Maps Using Unsupervised and

Saber Kazeminasab¹, Sayuri Sekimitsu², Mojtaba Fazli³

¹Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Harvard University, Boston, MA, USA. skazeminasabhashemabad@meei.harvard.edu.

Journal of Imaging Informatics in Medicine

|May 20, 2025

Summary

Artificial intelligence (AI) advances ophthalmic disease research by analyzing optical coherence tomography (OCT) images. A novel AI method successfully transfers OCT phenotypes across datasets, identifying clinically meaningful retinal layer clusters for glaucoma research.

Keywords:

Clustering Correlation analysis Deep unsupervised learning Dimensionality reduction Image phenotyping for OCT images Self-supervised learning in feature extraction

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Area of Science:

Ophthalmology
Artificial Intelligence
Medical Imaging

Background:

Understanding ophthalmic disease physiology and genetics is crucial.
Optical coherence tomography (OCT) provides detailed retinal imaging.
Developing methods for cross-dataset analysis of OCT data is challenging.

Purpose of the Study:

To enhance understanding of ophthalmic disease physiology and genetic architecture.
To introduce a novel AI methodology for transferring OCT phenotypes across datasets.
To phenotype and cluster OCT-derived retinal layer thicknesses using glaucoma as a model.

Main Methods:

Employed unsupervised and self-supervised learning techniques.
Integrated deep learning, manifold learning, and Gaussian mixture models.
Applied the method to two large datasets: Massachusetts Eye and Ear (MEE) and UK Biobank (UKBB).

Main Results:

Identified 9 to 11 clinically meaningful phenotypic clusters per retinal layer, consistent across datasets.
Demonstrated strong intra-cluster similarity and significant correlations with glaucoma severity markers (visual field mean deviation, cup-to-disc ratio).
Validated the model's robustness and generalizability across diverse datasets.

Conclusions:

The developed AI methodology enables robust OCT-based phenotyping and phenotype transfer.
This approach facilitates translational research in ophthalmic disease mechanisms and genetic discovery.
The findings pave the way for improved understanding and diagnosis of eye diseases.