Pattern recognition can detect subtle field defects in eyes of HIV individuals without retinitis under HAART.

OBJECTIVES

To use machine learning classifiers (MLCs) to seek differences in visual fields (VFs) between normal eyes and eyes of HIV+ patients; to find the effect of immunodeficiency on VFs and to compare the effectiveness of MLCs to commonly-used Statpac global indices in analyzing standard automated perimetry (SAP).

METHODS

The high CD4 group consisted of 70 eyes of 39 HIV-positive patients with good immune status (CD4 counts were never <100/ml). The low CD4 group had 59 eyes of 38 HIV-positive patients with CD4 cell counts <100/ml at some period of time lasting for at least 6 months. The normal group consisted of 61 eyes of 52 HIV-negative individuals. We used a Humphrey Visual Field Analyzer, SAP full threshold program 24-2, and routine settings for evaluating VFs. We trained and tested support vector machine (SVM) machine learning classifiers to distinguish fields from normal subjects and high and CD4 groups separately. Receiver operating characteristic (ROC) curves measured the discrimination of each classifier, and areas under ROC were statistically compared.

RESULTS

Low CD4 HIV patients: with SVM, the AUROC was 0.790 ± 0.042. SVM and MD each significantly differed from chance decision, with p < .00005. High CD4 HIV patients: the SVM AUROC of 0.664 ± 0.047 and MD were each significantly better than chance (p = .041, p = .05 respectively).

CONCLUSIONS

Eyes from both low and high CD4 HIV+ patients have VFs defects indicating retinal damage. Generalized learning classifier, SVM, and a Statpac classifier, MD, are effective at detecting HIV eyes that have field defects, even when these defects are subtle.