结构性MRI的卷积神经网络模型用于区分认知障碍类别:系统性审查和元分析
Xinxiu Dong1,2, Yang Li1,2, Jianbo Hao3
1School of Nursing, Hubei University of Chinese Medicine, Wuhan, Hubei, China.
BMC neurology
|September 30, 2025
概括
卷积神经网络 (CNNs) 显示出使用结构性MRI的阿尔茨海默病 (AD) 和轻度认知障碍 (MCI) 的强烈诊断准确性. 虽然对AD和正常认知有效,但MCI亚型的准确性较低,需要进一步研究.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 阿尔茨海默病 (AD) 和轻度认知障碍 (MCI) 构成重大公共卫生挑战,需要改进早期诊断工具.
- 结构磁共振成像 (sMRI) 与卷积神经网络 (CNN) 结合,为诊断这些神经退行性疾病提供了一种有前途的方法.
研究的目的:
- 对sMRI数据应用CNN算法的诊断性能进行系统的审查和元分析.
- 评估CNN在区分AD,MCI和正常认知 (NC) 的有效性.
主要方法:
- 根据PRISMA-DTA指南,在PubMed和Web of Science (2018-2024) 中进行了全面的文献搜索.
- 包括使用CNN用于基于sMRI的AD,MCI和NC的分类的研究.
- 使用 QUADAS-2 和 METRICS 评估方法质量;计算了汇总的诊断准确度指标.
主要成果:
- 该分析包括了21项研究,共有16139名参与者.
- 对于AD与NC,CNN的综合灵敏度和特异性为0.92和0.91,对于MCI与NC则为0.74和0.79.
- 在AD与MCI (0.73/0.79) 和pMCI与sMCI (0.69/0.81) 之间发现了适度的准确性,观察到显著的异质性.
结论:
- 使用sMRI的CNN算法显示了AD,MCI和NC的有希望的诊断性能.
- 最高的准确度是AD与NC,最低的pMCI与sMCI.
- 基于CNN的放射学可以成为神经退行性疾病的有价值的诊断工具,但由于研究异质性,需要进行方法的改进和验证.
相关概念视频
01:24Magnetic Resonance Imaging
7.6K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
7.6K
01:24Prosopagnosia
1.3K
Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
1.3K