Self-Supervised Learning for Improved Optical Coherence Tomography Detection of Macular Telangiectasia Type 2.

ABSTRACT:

Importance

Deep learning image analysis often depends on large, labeled datasets, which are difficult to obtain for rare diseases.

Objective

To develop a self-supervised approach for automated classification of macular telangiectasia type 2 (MacTel) on optical coherence tomography (OCT) with limited labeled data.

Design, setting, and participants

This was a retrospective comparative study. OCT images from May 2014 to May 2019 were collected by the Lowy Medical Research Institute, La Jolla, California, and the University of Washington, Seattle, from January 2016 to October 2022. Clinical diagnoses of patients with and without MacTel were confirmed by retina specialists. Data were analyzed from January to September 2023.

Exposures

Two convolutional neural network

SUBMITTER: Gholami S 

PROVIDER: S-EPMC10853868 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature