Machine learning classifiers detect subtle field defects in eyes of HIV individuals.

PURPOSE To test the following hypotheses: (1) eyes from patients with human immunodeficiency virus (HIV) have retinal damage that causes subtle field defects, (2) sensitive machine learning classifiers (MLCs) can use these field defects to distinguish fields in HIV patients and normal subjects, and (3) the subtle field defects form meaningful patterns. We have applied supervised MLCs--support vector machine (SVM) and relevance vector machine (RVM)--to determine if visual fields in patients with HIV differ from normal visual fields in HIV-negative controls. METHODS HIV-positive patients without visible retinopathy were divided into 2 groups: (1) 38 high-CD4 (H), 48.5 +/- 8.5 years, whose CD4 counts were never below 100; and (2) 35 low-CD4 (L), 46.1 +/- 8.5 years, whose CD4 counts were below 100 at least 6 months. The normal group (N) had 52 age-matched HIV-negative individuals, 46.3 +/- 7.8 years. Standard automated perimetry (SAP) with the 24-2 program was recorded from one eye per individual per group. SVM and RVM were trained and tested with cross-validation to distinguish H from N and L from N. Area under the receiver operating characteristic (AUROC) curve permitted comparison of performance of MLCs. Improvement in performance and identification of subsets of the most important features were sought with feature selection by backward elimination. RESULTS SVM and RVM distinguished L from N (L: AUROC = 0.804, N: 0.500, P = .0002 with SVM and L: .800, P = .0002 with RVM) and H from N (H: 0.683, P = .022 with SVM and H: 0.670, P = .038 with RVM). With best-performing subsets derived by backward elimination, SVM and RVM each distinguished L from N (L: 0.843, P < .00005 with SVM and L: 0.870, P < .00005 with RVM) and H from N (H: 0.695, P = .015 with SVM and H: 0.726, P = .007 with RVM). The most important field locations in low-CD4 individuals were mostly superior near the blind spot. The location of important field locations was uncertain in high-CD4 eyes. CONCLUSIONS This study has confirmed that low-CD4 eyes have visual field defects and retinal damage. Ranking located important field locations superiorly near the blind spot, implying damage to the retina inferiorly near the optic disc. Though most fields appear normal in high-CD4 eyes, SVM and RVM were sufficiently sensitive to distinguish these eyes from normal eyes with SAP. The location of these defects is not yet defined. These results also validate the use of sensitive MLC techniques to uncover test differences not discernible by human experts.