Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT.
OBJECTIVE
To compare the performance of two machine learning classifiers (MLCs), artificial neural networks (ANNs) and support vector machines (SVMs), with input based on retinal nerve fibre layer thickness (RNFLT) measurements by optical coherence tomography (OCT), on the diagnosis of glaucoma, and to assess the effects of different input parameters.


METHODS
We analysed Stratus OCT data from 90 healthy persons and 62 glaucoma patients. Performance of MLCs was compared using conventional OCT RNFLT parameters plus novel parameters such as minimum RNFLT values, 10th and 90th percentiles of measured RNFLT, and transformations of A-scan measurements. For each input parameter and MLC, the area under the receiver operating characteristic curve (AROC) was calculated.


RESULTS
There were no statistically significant differences between ANNs and SVMs. The best AROCs for both ANN (0.982, 95%CI: 0.966-0.999) and SVM (0.989, 95% CI: 0.979-1.0) were based on input of transformed A-scan measurements. Our SVM trained on this input performed better than ANNs or SVMs trained on any of the single RNFLT parameters (p < or = 0.038). The performance of ANNs and SVMs trained on minimum thickness values and the 10th and 90th percentiles were at least as good as ANNs and SVMs with input based on the conventional RNFLT parameters.


CONCLUSIONS
No differences between ANN and SVM were observed in this study. Both MLCs performed very well, with similar diagnostic performance. Input parameters have a larger impact on diagnostic performance than the type of machine classifier. Our results suggest that parameters based on transformed A-scan thickness measurements of the RNFL processed by machine classifiers can improve OCT-based glaucoma diagnosis.
Bizios
Dimitrios
D
Department of Clinical Sciences, Ophthalmology, Malmö University Hospital, Lund University, Malmoe, Sweden. dimitrios.bizios@med.lu.se
Heijl
Anders
A
Hougaard
Jesper Leth
JL
Bengtsson
Boel
B
D000328:Adult; D000368:Aged; D019540:Area Under Curve; D001185:Artificial Intelligence; D005260:Female; D005901:Glaucoma / Q000145:classification* / Q000175:diagnosis*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009412:Nerve Fibers / Q000473:pathology*; D016571:Neural Networks, Computer; D012372:ROC Curve; D012160:Retina / Q000473:pathology*; D041623:Tomography, Optical Coherence; D055815:Young Adult