Machine Learning Models for Diagnosing Glaucoma from Retinal Nerve Fiber Layer Thickness Maps.

Ophthalmology. Glaucoma
Peiyu WangGrace M Richter

Abstract

To assess the diagnostic accuracy of multiple machine learning models using full retinal nerve fiber layer (RNFL) thickness maps in detecting glaucoma. Case-control study. A total of 93 eyes from 69 patients with glaucoma and 128 eyes from 128 age- and sex-matched healthy controls from the Los Angeles Latino Eye Study (LALES), a large population-based, longitudinal cohort study consisting of Latino participants aged ≥40 years residing in El Puente, California. The 6×6-mm RNFL thickness maps centered on the optic nerve head (Cirrus 4000; Zeiss, Dublin, CA) were supplied to 4 different machine learning algorithms. These models included 2 conventional machine learning algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), and 2 convolutional neural nets, ResNet-18 and GlaucomaNet, which was a custom-made deep learning network. All models were tested with 5-fold cross validation. Area under the curve (AUC) statistics to assess diagnostic accuracy of each model compared with conventional average circumpapillary RNFL thickness. All 4 models achieved similarly high diagnostic accuracies, with AUC values ranging from 0.91 to 0.92. These values were significantly higher than those for average circumpapillary RNFL thickne...Continue Reading

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