评估机器学习和深度学习在区分神经免疫疾病方面的有效性:系统性审查和元分析
David Petrosian1, Natasa Giedraitiene2, Rasa Kizlaitiene2
1Faculty of Medicine, Vilnius University, Vilnius, Lithuania.
Frontiers in neurology
|January 28, 2026
概括
人工智能 (AI) 在诊断神经免疫学疾病方面表现有前途. 机器学习和深度学习模型,特别是使用MRI数据,在区分多发性硬化症等疾病方面取得了很高的准确性.
科学领域:
- 神经免疫学 神经免疫学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 诊断神经免疫学疾病带来了重大的临床挑战.
- 人工智能 (AI) 越来越多地被探索,以提高诊断准确度.
- 机器学习 (ML) 和深度学习 (DL) 提供了区分复杂神经疾病的潜力.
研究的目的:
- 对神经免疫学疾病的ML和DL技术的诊断性能进行系统的审查和元分析.
- 识别有效的AI方法并比较它们的诊断结果.
- 为临床应用和未来研究提供建议.
主要方法:
- 按照PRISMA 2020指南,对PubMed和科学网络 (2000-2024) 的系统文献搜索.
- 使用QUADAS-2工具进行偏差风险和适用性评估.
- 进行元分析,计算人工智能模型的聚合精度,灵敏度和特异性.
主要成果:
- 19项研究符合纳入标准,其中9项使用ML,10项使用DL.
- 大多数模型使用MRI数据来区分诸如多发性硬化症和神经髓炎等疾病.
- 聚合精度,灵敏度和特异性分别为0.87,0.86和0.84,观察到异质性.
结论:
- 人工智能工具,特别是那些使用MRI数据的工具,在区分神经免疫学疾病方面显示出潜力.
- 未来的人工智能开发应该专注于血清阴性疾病,并整合临床/流行病学数据以提高准确性.
- 虽然抗体测试对许多疾病有效,但AI提供了补充的诊断支持.
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