Abstract #1885

A Support Vector Machine Prediction Model for HIV-Status in Adults Using Magnetic Resonance Angiography and Arterial Spin Labeling of the Brain

Kyle Murray¹, Igor B Titoff², Henry Wang³, Jianhui Zhong^1,3, and Giovanni Schifitto^2,3

¹Physics and Astronomy, University of Rochester, Webster, NY, United States, ²Neurology, University of Rochester, Rochester, NY, United States, ³Imaging Sciences, University of Rochester, Rochester, NY, United States

HIV-infection is known to be related to vascular diseases, which can be explored via cerebral imaging techniques such as magnetic resonance angiography (MRA) and arterial spin labeling (ASL). In this abstract, we use quantitative features extracted from demographic information and vascular imaging data, only, to predict HIV-status in adults using a support vector machine (SVM). This is the first SVM to reasonably predict HIV-status in an aging HIV-population on combination antiretroviral therapy, which may have future biological implications in HIV research.

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