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Optic Cup and Disc Segmentation of Fundus Images Using Artificial Intelligence Externally Validated With Optical
Scott Kinder1, Steve McNamara1, Christopher Clark1
1Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Purpose:
To develop an artificial intelligence (AI) optic cup and disc segmentation pipeline for obtaining optic nerve head (ONH) measurements such as vertical cup-to-disc ratio (VCDR) from fundus images and externally validate performance against optical coherence tomography (OCT) measurements.
Methods:
This diagnostic study used a retrospectively collected dataset of 27,252 fundus images associated with 12,477 OCT reports and 21,714 expert assessments of VCDR from electronic health records (EHRs) for 4289 patients inclusive of glaucoma suspects, primary and secondary glaucoma. The AI pipeline was trained on nine public glaucoma datasets and externally validated on a private hospital dataset and a publicly available dataset.
Results:
AI VCDR predictions against OCT yielded mean absolute error (MAE), Pearson's R, and concordance correlation coefficient (CCC) values of 0.097 (95% confidence interval [CI], 0.095-0.099), 0.80 (95% CI, 0.79-0.81), and 0.66 (95% CI, 0.64-0.67), respectively. EHR VCDRs against OCT had MAE, Pearson's R, and CCC values of 0.086 (95% CI, 0.084-0.087), 0.77 (95% CI, 0.76-0.78), and 0.74 (95% CI, 0.73-0.75), respectively. The coefficient of variation (CV) of the AI pipeline on same-day images was 2.79%.
Conclusions:
The proposed AI pipeline had strong correlation with OCT measurements and performed comparably to EHR assessments, with high repeatability. Increased diversity and cardinality of training data improved performance and generalizability to unseen datasets.
Translational Relevance:
AI pipelines for fundus images can provide ONH measurements such as VCDR near expert level in new patient populations without the need for additional model training.
