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Abstract #1884

Regional gray matter volume predicts HIV-positive patients with HAND using a machine learning approach

Danhui Fu1, Guanqiao Jin1, Lidong Liu1, Wenjuan Deng1, Wei Zhang1, Qianlin Ding1, Weiyin Vivian Liu2, Long Qian2, and Danke Su1
1GuangXi Medical University Cancer Hospital, Nanning, China, 2MR Research, GE Healthcare, Beijin, China

Conventional neuropsychometric tests could not early detect HIV-associated neurocognitive disorder (HAND) on HIV-patients. More objective indicators with neuroimaging techniques have offered to sub-classify patients with specific symptoms. Significant differences of regional gray matter volumes (rGMV) betweenHIV-positive group and age-matched healthy group was found in our preliminary study. Ten major rGMV well distinguished HIV-positive patients with HAND from those without HAND. Our study showed rGMV might be an indicator to explain for neurocognitive impairments.

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