title: Analysis with support vector machine shows HIV-positive subjects without infectious retinitis have mfERG deficiencies compared to normal eyes.
abstract: OBJECTIVE
  To test the following hypotheses: (1) eyes from individuals with human immunodeficiency virus (HIV) have electrophysiologic abnormalities that manifest as multifocal electroretinogram (mfERG) abnormalities; (2) the retinal effects of HIV in immune-competent HIV individuals differ from the effects in immune-incompetent HIV individuals; (3) strong machine learning classifiers (MLCs), like support vector machine (SVM), can learn to use mfERG abnormalities in the second-order kernel (SOK) to distinguish HIV from normal eyes; and (4) the mfERG abnormalities fall into patterns that can be discerned by MLCs. We applied a supervised MLC, SVM, to determine if mfERGs in eyes from patients with HIV differ from mfERGs in HIV-negative controls.
  
  
  METHODS
  Ninety-nine HIV-positive patients without visible retinopathy were divided into 2 groups: (1) 59 high-CD4 individuals (H, 104 eyes), 48.5 +/- 7.7 years, whose CD4 counts were never observed below 100, and (2) 40 low-CD4 individuals (L, 61 eyes), 46.2 +/- 5.6 years, whose CD4 counts were below 100 for at least 6 months. The normal group (N, 82 eyes) had 41 age-matched HIV-negative individuals, 46.8 +/- 6.2 years. The amplitude and latency of the first positive curve (P1, hereafter referred to as a) and the first negative curve (N1, referred to as b) in the SOK of 103 hexagon patterns of the central 28 degrees of the retina were recorded from the eyes in each group. SVM was trained and tested with cross-validation to distinguish H from N and L from N. SOK was chosen as a presumed detector of inner retinal abnormalities. Classifier performance was measured with the area under the receiver operating characteristic (AUROC) curve to permit comparison of MLCs. Improvement in performance and identification of subsets of the most important features were sought with feature selection by backward elimination.
  
  
  RESULTS
  In general, the SOK b-parameters separated L from N and H from N better than a-parameters, and latency separated L from N and H from N better than amplitude. In the HIV groups, on average, amplitude was diminished and latency was extended. The parameter that most consistently separated L from N and H from N was b-latency. With b-latency, SVM learned to distinguish L from N (AUROC = 0.7.30 +/- 0.044, P = .001 against chance [0.500 +/- 0.051]) and H from N (0.732 +/- 0.038, P = .0001 against chance) equally well. With best-performing subsets (21 out of 103 hexagons) derived by backward elimination, SVM distinguished L from N (0.869 +/- 0.030, P < .00005 against chance) and H from N (0.859 +/- 0.029, P <.00005 against chance) better than SVM with the full set of hexagons. Mapping the top 10 hexagon locations for L vs N and H vs N produced no apparent pattern.
  
  
  CONCLUSIONS
  This study confirms that mfERG SOK abnormalities develop in the retina of HIV-positive individuals. The new finding of equal severity of b-latency abnormalities in the low- and high-CD4 groups indicates that good immune status under highly active antiretroviral therapy may not protect against retinal damage and, by extension, damage elsewhere. SOKs are difficult for human experts to interpret. Machine learning classifiers, such as SVM, learn from the data without human intervention, reducing the need to rely on human skills to interpret this test.
authors.affiliation: Ophthalmic Informatics Laboratory, University of California at San Diego, USA.
authors.forename: Michael H
authors.initials: MH
authors.lastname: Goldbaum
authors.forename: Irina
authors.initials: I
authors.lastname: Falkenstein
authors.forename: Igor
authors.initials: I
authors.lastname: Kozak
authors.forename: Jiucang
authors.initials: J
authors.lastname: Hao
authors.forename: Dirk-Uwe
authors.initials: DU
authors.lastname: Bartsch
authors.forename: Terrance
authors.initials: T
authors.lastname: Sejnowski
authors.forename: William R
authors.initials: WR
authors.lastname: Freeman
mesh_terms: D023241:Antiretroviral Therapy, Highly Active; D019540:Area Under Curve; D001185:Artificial Intelligence; D018791:CD4 Lymphocyte Count; D004596:Electroretinography / Q000379:methods*; D015828:Eye Infections, Viral / Q000175:diagnosis*; D005260:Female; D015658:HIV Infections / Q000175:diagnosis* / Q000276:immunology / Q000503:physiopathology; D006801:Humans; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D012160:Retina / Q000473:pathology*; D012164:Retinal Diseases / Q000175:diagnosis* / Q000276:immunology / Q000503:physiopathology; D012173:Retinitis / Q000821:virology