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基于磁共振成像的机器学习对精神分裂症谱系障碍的分类:一个元分析

Fabio Di Camillo¹, David Antonio Grimaldi¹, Giulia Cattarinussi^1,2,3

¹Department of Neuroscience (DNS), University of Padova, Padua, Italy.

Psychiatry and clinical neurosciences

|September 18, 2024

概括

多变量模式分析可靠地识别精神分裂症谱系障碍 (SSD) 的神经成像生物标志物,达到约80%的准确性. 诸如患者特征和研究方法等因素会影响分类性能.

科学领域:

神经成像是一种神经成像.
精神病学研究精神病学研究
机器学习在医学中的应用

背景情况:

多变量模式识别推进神经成像生物标志物搜索精神疾病,如精神分裂症.
这些方法捕捉复杂的大脑变化,超越了传统的单变量方法.
一项系统性审查评估了神经成像生物标志物的可靠性,以区分精神分裂症谱系障碍 (SSD) 和健康对照 (HCs).

研究的目的:

评估基于神经成像的生物标志物用于SSD检测的可靠性.
评估研究特征对分类性能的影响.
确定多变量模式分析在区分SSD和HC中的有效性.

主要方法:

在PubMed,Scopus和Web of Science系统搜索相关研究.
使用双变的随机效应模型对灵敏度 (SE) 和特异性 (SP) 的元分析.
评估临床和非临床变量作为分类性能调节器.

主要成果:

分析了119项研究,包括12,723名SSD患者和13,196名HC患者.
总体而言,SE为79.1%,SP为80.0%.
诸如症状得分,年龄,未经治疗的精神病持续时间和方法等因素显著影响了分类.

结论:

多变量模式分析证明了SSD神经成像生物标志物的可靠识别.
尽管异质,大脑修改有效地区分SSD患者和HC患者.
与患者相关的和方法方面的因素对于开发和应用临床分类模型至关重要.

关键词:

这是分类分类的分类.机器学习是机器学习.磁共振成像技术的使用这是一个元分析.精神分裂症是一种精神分裂症.

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