Highly Accurate and Precise Automated Cup-to-Disc Ratio Quantification for Glaucoma Screening

Abstract

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 disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Keywords: Computer Vision; Deep Learning; Fundus Image; Glaucoma; UK Biobank.

Figure 1

Overview of our study design used for model development. A model for gradability assessment was developed (model 1), and then a model for cup-to-disc ratio quantification was generated based on gradable images (model 2). These 2 models were then combined to assess the utility of diagnosing glaucoma in 2 separate cohorts. CDR = cup-to-disc ratio; QC = quality control; UK = United Kingdom.

Figure 2

Comparative analysis and final dataset distribution. The Bland-Altman plot displays the cup-to-disc ratio (CDR) estimation on the validation dataset with the corresponding CDR ground truth shown in (A). The mean difference between true and predicted CDR was 0.022 (95% confidence interval: 0.009–0.034). The red dashed line represents the mean difference, while the yellow dashed lines represent the 95% confidence interval for the mean difference for upper and lower limits. The distribution of the CDR in the final analysis data is graphically presented in (B).

Figure 3

The confusion matrix for cup-to-disc ratio classification is illustrated in (A). In contrast, (B) presents the conversion of regression points into classification metrics utilizing a particular threshold. Publicly available datasets were used for external validation of the glaucoma screening at the globally accepted cut-off threshold, as shown in (C).