Application Value of Machine Learning Method in Measuring Gray Matter Volume of AIDS Patients

Abstract

Background. To investigate the role of gray matter (GM) volume in the identification of HIV-positive patients with HIV-associated neurocognitive impairment (HAND) using a machine learning approach from normal healthy controls. Methods. Twenty-seven HIV-infected patients and 14 healthy controls were enrolled in our study. Each set of BRAVO images was postprocessed using DPARSF3.1 to coregister all brains on the MNI template, and volume extraction of 90 brain regions was performed using custom-designed code. The machine learning method was performed using PRoNTo2.1.1 toolbox. The differences in brain volume between the HAND and non-HAND groups were analyzed. Results. GM volume effectively distinguished HIV-positive patients from healthy subjects with an AUC equals to 0.73. The sensitivity, specificity, and accuracy of the established classification were 85.19%, 42.86%, and 70.73%, respectively. GM volume value of the top ten brain regions was related to digit symbols, trail making test, digit span, vocabulary fluency, stroop C time, stroop CW time, CD4, and neuropsychological group. Conclusions. A machine learning approach facilitates early diagnosis of HAND in HIV patients by MRI-based GM volume measurement.