机器学习方法在测量艾滋病患者灰质体积中的应用价值。
Application Value of Machine Learning Method in Measuring Gray Matter Volume of AIDS Patients.
机构信息
Departments of Radiology, Guangxi Medical University Cancer Hospital, Nanning, 530021 Guangxi Zhuang Autonomous Region, China.
Guangxi Key Clinical Specialty (Medical Imaging Department), Dominant Cultivation Discipline of Guangxi Medical University Cancer Hospital (Medical Imaging Department), China.
出版信息
Dis Markers. 2022 Jun 16;2022:1210002. doi: 10.1155/2022/1210002. eCollection 2022.
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.
背景
使用机器学习方法从正常健康对照中研究灰质 (GM) 体积在识别 HIV 阳性合并 HIV 相关神经认知障碍 (HAND) 患者中的作用。
方法
本研究纳入了 27 名 HIV 感染患者和 14 名健康对照者。使用 DPARSF3.1 对 BRAVO 图像进行后处理，将所有大脑配准到 MNI 模板上，并使用定制设计的代码提取 90 个脑区的体积。使用 PRoNTo2.1.1 工具箱进行机器学习方法。分析 HAND 和非 HAND 组之间的脑容量差异。
结果
GM 体积可有效区分 HIV 阳性患者和健康受试者，AUC 等于 0.73。建立的分类的灵敏度、特异性和准确性分别为 85.19%、42.86%和 70.73%。前十个脑区的 GM 体积值与数字符号、连线测试、数字跨度、词汇流畅性、Stroop C 时间、Stroop CW 时间、CD4 和神经心理学组有关。
结论
通过 MRI 测量 GM 体积，机器学习方法有助于 HIV 患者 HAND 的早期诊断。
Zotero 插件