在饮食障碍中神经成像和机器学习:系统性审查
Francesco Monaco1,2, Annarita Vignapiano1,2, Benedetta Di Gruttola3
1Department of Mental Health, Azienda Sanitaria Locale Salerno, Salerno, Italy.
Eating and weight disorders : EWD
|June 1, 2025
概括
应用于神经成像的机器学习 (ML) 显示出诊断诸如神经性厌食症之类的饮食障碍 (ED) 的前景. 然而,目前的研究存在局限性,需要更大规模的,多中心的临床研究.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 计算机科学 计算机科学
背景情况:
- 饮食障碍 (ED) 是一种严重的精神疾病,具有很高的死亡率.
- 神经成像和机器学习 (ML) 提供了改善EDs诊断和治疗预测的潜力.
研究的目的:
- 系统地审查ML技术对神经成像数据的应用,用于神经性厌食症 (AN),神经性嗜食症 (BN) 和过度饮食障碍 (BED).
主要方法:
- 在PubMed和APA PsycINFO (2014-2024) 上进行了系统的文献搜索,遵循PRISMA指南.
- 包括使用神经成像和ML对AN,BN或BED的人类受试者的研究.
- 使用GRADE和ROBINS-I工具进行质量评估.
主要成果:
- 五项研究符合纳入标准,主要使用支持矢量机 (SVM) 或其他监督的ML模型与结构MRI或扩散张力成像.
- 观察到的皮层厚度和扩散指标区分了ED亚型.
- 研究是观察性的,异质的,样本规模小,并且具有中度至严重的偏差风险,缺乏外部验证.
结论:
- 神经成像中的ML具有提高ED特征和预测结果的潜力.
- 目前,方法上的局限性阻碍了通用性.
- 未来的研究需要更大,多中心,多模式的研究,以提高临床适用性.
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