Keywords: Infectious disease, Machine Learning/Artificial IntelligenceIdentifying and monitoring viral habitats of HIV in the brain is crucial to the advancement of treatment strategies for containing the infection. However, no in-vivo methods currently exist for this purpose. In this study, we demonstrate the use of a machine learning model that integrates brain measurements from MRSI, DTI, and DKI for identifying the severity and anatomical location of microstructural and metabolic abnormalities in the brain. This information may provide important clinical and diagnostic value for the treatment of people living with HIV.
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