Photo Credit: Marvin Samuel Tolentino Pineda

To use macular OCT imaging to predict future and detect concurrent visual field progression, respectively, using deep learning.
Retrospective cohort study.
A pre-training dataset was comprised of 7,702,201 B-scan images from 151,389 macular OCT studies. The progression detection task included 3,902 macular OCT imaging studies from 1,534 eyes of 828 glaucoma patients and the progression prediction task included 1,346 macular OCT studies from 1,205 eyes of 784.
A novel deep learning method was developed to detect glaucoma progression and predict future progression using macular OCT, based on self-supervised pre-training of a vision transformer (ViT) model on a large, unlabeled dataset of OCT images. Glaucoma progression was defined as mean deviation (MD) rate of change of ≤ -0.5 dB/year over 5 consecutive Humphrey visual field tests, and rapid progression was defined as MD change ≤ -1 dB/year.
Diagnostic performance of the ViT model for prediction of future visual field progression and detection of concurrent visual field progression using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.
The model distinguished stable eyes from progressing eyes, achieving an AUC of 0.90 (95% CI, 0.88-0.91). Rapid progression was detected with an AUC of 0.92 (95% CI, 0.91-0.93). The model also demonstrated high predictive ability for forecasting future glaucoma progression, with an AUC of 0.85 (95% CI 0.83, 0.87). Rapid progression was predicted with an AUC of 0.84 (95% CI 0.81, 0.86).
A deep learning model detected clinically significant functional glaucoma progression using macular OCT imaging studies and was also able to predict future progression. Early identification of patients undergoing glaucoma progression or at high risk for future progression may aid in clinical decision-making.
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