Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements

Abstract

Purpose: To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP).

Methods: Seventy-two eyes of 72 healthy control subjects (average age = 64.3 +/- 8.8 years, visual field mean deviation = -0.71 +/- 1.2 dB) and 92 eyes of 92 patients with glaucoma (average age = 66.9 +/- 8.9 years, visual field mean deviation = -5.32 +/- 4.0 dB) were imaged with SLP with variable corneal compensation (GDx VCC; Laser Diagnostic Technologies, San Diego, CA). RVM and SVM learning classifiers were trained and tested on SLP-determined RNFL thickness measurements from 14 standard parameters and 64 sectors (approximately 5.6 degrees each) obtained in the circumpapillary area under the instrument-defined measurement ellipse (total 78 parameters). Ten-fold cross-validation was used to train and test RVM and SVM classifiers on unique subsets of the full 164-eye data set and areas under the receiver operating characteristic (AUROC) curve for the classification of eyes in the test set were generated. AUROC curve results from RVM and SVM were compared to those for 14 SLP software-generated global and regional RNFL thickness parameters. Also reported was the AUROC curve for the GDx VCC software-generated nerve fiber indicator (NFI).

Results: The AUROC curves for RVM and SVM were 0.90 and 0.91, respectively, and increased to 0.93 and 0.94 when the training sets were optimized with sequential forward and backward selection (resulting in reduced dimensional data sets). AUROC curves for optimized RVM and SVM were significantly larger than those for all individual SLP parameters. The AUROC curve for the NFI was 0.87.

Conclusions: Results from RVM and SVM trained on SLP RNFL thickness measurements are similar and provide accurate classification of glaucomatous and healthy eyes. RVM may be preferable to SVM, because it provides a Bayesian-derived probability of glaucoma as an output. These results suggest that these machine learning classifiers show good potential for glaucoma diagnosis.

Figure 1

ROC curves for “best” optimized RVM, “best” optimized SVM, full-dimensional RVM, full-dimensional SVM, and normalized inferior area. AUROC curves are shown in parentheses.

Figure 2

Percentage of healthy or glaucomatous eyes assigned by RVM to each 10% probability bin. Of the healthy eyes, 84.6% were assigned a probability under 51%, and of glaucomatous eyes, 85.8% were assigned a probability over 50%.

Figure 3

Scanning laser polarimetry RNFL thickness maps from three eyes classified as normal based on visual field results. Brighter colors represent a thicker RNFL. Eye (A) was assigned a 0.1% probability of belonging to the glaucoma group by RVM analysis, with a SAP MD of −0.13 dB and a PSD of 1.70 dB. GDx VCC NFI output was 9. Eye (B) was assigned a 48% probability of belonging to the glaucoma group by RVM analysis, with a SAP MD of 0.10 dB and a PSD of 1.56 dB. GDx VCC NFI output was 14. Eye (C) was assigned a 92% probability of belonging to the glaucoma group by RVM analysis, with a SAP MD of −1.58 dB and a PSD of 1.49 dB. GDx VCC NFI output was 36.

Figure 4

Scanning laser polarimetry RNFL thickness maps from three eyes classified as glaucomatous based on visual field results (repeatable SAP Glaucoma Hemifield Test results outside normal limits and/or PSD < 5%). Eye (A) was assigned a 14% probability of belonging to the glaucoma group by RVM analysis, with a SAP MD of −4.41 dB and a PSD of 7.93 dB. GDx VCC NFI output was 26. Eye (B) was assigned a 49% probability of belonging to the glaucoma group by RVM analysis, with a SAP MD of −3.51 dB and a PSD of 3.09 dB. GDx VCC NFI output was 22. Eye (C) was assigned a 99% probability of belonging to the glaucoma group by RVM analysis with a SAP MD of −8.53 dB and a PSD of 7.61 dB. GDx VCC NFI output was 98.