Self-Supervised Learning for Improved Optical Coherence Tomography Detection of Macular Telangiectasia Type 2
Authors: Gholami Shahrzad , Scheppke Lea , Kshirsagar Meghana , Wu Yue , Dodhia Rahul , Bonelli Roberto , Leung Irene , Sallo Ferenc B. , Muldrew Alyson , Jamison Catherine , Peto Tunde , Lavista Ferres Juan , Weeks William B. , Friedlander Martin , Lee Aaron Y. ,   MacTel Research Group, Okada Mali , Gaudric Alain , Schwartz Steven , Constable Ian , Yannuzzi Lawrence A. , Egan Cathy , Singerman Lawrence , Gillies Mark , Friedlander Martin , Lange Clemens , Holz Frank , Comer Grant , Brucker Alexander , Bernstein Paul , Rosenfeld Philip , Miller Joan , Yan Jiong , Duncan Jacque , Weinberg David , Sallo Ferenc , Hoyng CB , Charbel Issa Peter , Bucher Felicitas , Berger Brian , Rich Ryan , Miller Daniel , Lee Cecilia , Do Diana , Bakri Sophie , Higgins Patrick , Zhuk Stanislav A. , Randhawa Sandeep , Raphaelian Paul V. , Sneed Scott , Khanani Arshad , Lee Michael , Warrow David , Fawzi Amani , Goldberg Roger , Barb Scott M. , Elman Michael J. , Wykoff Charles , Finley Thomas , Wells, III John A. , Fish Gary , Randolph John , Boyer David , Qureshi Jawad , Blinder Kevin 
Published: February 2024

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 networks were pretrained using the Bootstrap Your Own Latent algorithm on unlabeled training data and fine-tuned with labeled training data to predict MacTel (self-supervised method). ResNet18 and ResNet50 models were also trained using all labeled data (supervised method).

Main Outcomes and Measures

The ground truth yes vs no MacTel diagnosis is determined by retinal specialists based on spectral-domain OCT. The models’ predictions were compared against human graders using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under precision recall curve (AUPRC), and area under the receiver operating characteristic curve (AUROC). Uniform manifold approximation and projection was performed for dimension reduction and GradCAM visualizations for supervised and self-supervised methods.

Results

A total of 2636 OCT scans from 780 patients with MacTel and 131 patients without MacTel were included from the MacTel Project (mean [SD] age, 60.8 [11.7] years; 63.8% female), and another 2564 from 1769 patients without MacTel from the University of Washington (mean [SD] age, 61.2 [18.1] years; 53.4% female). The self-supervised approach fine-tuned on 100% of the labeled training data with ResNet50 as the feature extractor performed the best, achieving an AUPRC of 0.971 (95% CI, 0.969-0.972), an AUROC of 0.970 (95% CI, 0.970-0.973), accuracy of 0.898%, sensitivity of 0.898, specificity of 0.949, PPV of 0.935, and NPV of 0.919. With only 419 OCT volumes (185 MacTel patients in 10% of labeled training dataset), the ResNet18 self-supervised model achieved comparable performance, with an AUPRC of 0.958 (95% CI, 0.957-0.960), an AUROC of 0.966 (95% CI, 0.964-0.967), and accuracy, sensitivity, specificity, PPV, and NPV of 90.2%, 0.884, 0.916, 0.896, and 0.906, respectively. The self-supervised models showed better agreement with the more experienced human expert graders.

Conclusions and Relevance

The findings suggest that self-supervised learning may improve the accuracy of automated MacTel vs non-MacTel binary classification on OCT with limited labeled training data, and these approaches may be applicable to other rare diseases, although further research is warranted.