Highly Accurate and Precise Automated Cup-to-Disc Ratio Quanti fi cation for Glaucoma Screening

Objective: An enlarged cup-to-disc ratio (CDR) is a hallmark of glaucomatous optic neuropathy. Manual assessment of the CDR may be less accurate and more time-consuming than automated methods. Here, we sought to develop and validate a deep learning- based algorithm to automatically determine the CDR from fundus images. Design: Algorithm development for estimating CDR using fundus data from a population-based observational study. Participants: A total of 181 768 fundus images from the United Kingdom Biobank (UKBB), Drishti_GS, and EyePACS. Methods: FastAI and PyTorch libraries were used to train a convolutional neural network- based model on fundus images from the UKBB. Models were constructed to determine image gradability (classification analysis) as well as to estimate CDR (regression analysis). The best-performing model was then validated for use in glaucoma screening using a multiethnic dataset from EyePACS and Drishti_GS. Main Outcome Measures: The area under the receiver operating characteristic curve and coefficient of determination. Results: Our gradability model vgg19_batch normalization (bn) achieved an accuracy of 97.13% on a validation set of 16 045 images, with 99.26% precision and area under the receiver operating characteristic curve of 96.56%. Using regression analysis, our best-performing model (trained on the vgg19_bn architecture) attained a coefficient of determination of 0.8514 (95% confidence interval [CI]: 0.8459- 0.8568), while the mean squared error was 0.0050 (95% CI: 0.0048- 0.0051) and mean absolute error was 0.0551 (95% CI: 0.0543- 0.0559) on a validation set of 12 183 images for determining CDR. The regression point was converted into classification metrics using a tolerance of 0.2 for 20 classes; the classification metrics achieved an accuracy of 99.20%. The EyePACS dataset (98 172 healthy, 3270 glaucoma) was then used to externally validate the model for glaucoma classification, with an accuracy, sensitivity, and specificity of 82.49%, 72.02%, and 82.83%, respectively. Conclusions: Our models were precise in determining image gradability and estimating CDR. Although our artificial intelligence- derived CDR estimates achieve high accuracy, the CDR threshold for glaucoma screening will vary depending on other clinical parameters. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. Ophthalmology Science 2024;4:100540 (c) 2024 by the American Academy of Ophthalmology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).