Development and validation of a multi-stage self-supervised learning model for optical coherence tomography image classification.

ABSTRACT:

Objective

This study aimed to develop a novel multi-stage self-supervised learning model tailored for the accurate classification of optical coherence tomography (OCT) images in ophthalmology reducing reliance on costly labeled datasets while maintaining high diagnostic accuracy.

Materials and methods

A private dataset of 2719 OCT images from 493 patients was employed, along with 3 public datasets comprising 84 484 images from 4686 patients, 3231 images from 45 patients, and 572 images. Extensive internal, external, and clinical validation were performed to assess model performance. Grad-CAM was employed for qualitative analysis to interpret the model's decisions by highlighting relevant areas. Subsampling analyses evaluated the model's robustness with varying labeled data a

SUBMITTER: Shim S 

PROVIDER: S-EPMC12012341 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature