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

Abstract Purpose To assess the diagnostic accuracy of multiple machine learning models using full retinal nerve fiber layer (RNFL) thickness maps in detecting glaucoma. Design Case-control study Subjects 93 eyes from 69 glaucoma patients and 128 eyes from 128 age- and sex- matched healthy controls from the Los Angeles Latino Eye Study, a large population-based, longitudinal cohort study consisting of Latino participants 40 years of age and over residing in El Puente, California, USA. Methods 6x6mm RNFL thickness maps centered on the optic nerve head (Cirrus 4000; ZEISS) were supplied to four different machine learning algorithms. These models included two conventional machine learning algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), and two convolutional neural nets, ResNet-18 and GlaucomaNet, which was a custom-made deep learning network. All models were tested with 5-fold cross validation. Main Outcome Measures Area-under-curve (AUC) statistics to assess diagnostic accuracy of each model in comparison to that for conventional average circumpapillary RNFL thickness. Results All four models achieved similarly high diagnostic accuracies, with AUC values ranging from 0.91 - 0.92. These values were significantly higher than that for average circumpapillary RNFL thickness, which had an AUC of 0.76 in the same patient population. Conclusions Superior diagnostic performance was achieved with both conventional machine learning and convolutional neural net models, as compared to circumpapillary RNFL thickness. This supports the importance of the spatial structure of RNFL thickness map data in diagnosing glaucoma and further efforts to optimize our use of this data.