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

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...

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Bibliographic Details
Published in:Disease markers 2022-06, Vol.2022, p.1-7
Main Authors: Fu, Danhui, Mo, Kai, Deng, Wenjuan, Zhao, Yang, Ding, QianLin, Hong, Sen, Zhang, Wei, Su, Danke
Format: Article
Language:English
Subjects:
Acquired immune deficiency syndrome
Age
AIDS
Brain
CD4 antigen
Classification
Cognition
Correlation analysis
Hand
HIV
Human immunodeficiency virus
Infections
Learning algorithms
Machine learning
Magnetic resonance imaging
Statistical analysis
Substantia grisea
Support vector machines
Volume measurement
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Summary: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.
ISSN:0278-0240
1875-8630
DOI:10.1155/2022/1210002