Mubonanyikuzo Vivens, Yan Hongjie, Komolafe Temitope Emmanuel, Zhou Liang, Wu Tao, Wang Nizhuan
College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China.
J Med Internet Res. 2025 Feb 5;27:e62647. doi: 10.2196/62647.
Alzheimer disease (AD) is a progressive condition characterized by cognitive decline and memory loss. Vision transformers (ViTs) are emerging as promising deep learning models in medical imaging, with potential applications in the detection and diagnosis of AD.
This review systematically examines recent studies on the application of ViTs in detecting AD, evaluating the diagnostic accuracy and impact of network architecture on model performance.
We conducted a systematic search across major medical databases, including China National Knowledge Infrastructure, CENTRAL (Cochrane Central Register of Controlled Trials), ScienceDirect, PubMed, Web of Science, and Scopus, covering publications from January 1, 2020, to March 1, 2024. A manual search was also performed to include relevant gray literature. The included papers used ViT models for AD detection versus healthy controls based on neuroimaging data, and the included studies used magnetic resonance imaging and positron emission tomography. Pooled diagnostic accuracy estimates, including sensitivity, specificity, likelihood ratios, and diagnostic odds ratios, were derived using random-effects models. Subgroup analyses comparing the diagnostic performance of different ViT network architectures were performed.
The meta-analysis, encompassing 11 studies with 95% CIs and P values, demonstrated pooled diagnostic accuracy: sensitivity 0.925 (95% CI 0.892-0.959; P<.01), specificity 0.957 (95% CI 0.932-0.981; P<.01), positive likelihood ratio 21.84 (95% CI 12.26-38.91; P<.01), and negative likelihood ratio 0.08 (95% CI 0.05-0.14; P<.01). The area under the curve was notably high at 0.924. The findings highlight the potential of ViTs as effective tools for early and accurate AD diagnosis, offering insights for future neuroimaging-based diagnostic approaches.
This systematic review provides valuable evidence for the utility of ViT models in distinguishing patients with AD from healthy controls, thereby contributing to advancements in neuroimaging-based diagnostic methodologies.
PROSPERO CRD42024584347; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=584347.
阿尔茨海默病（AD）是一种以认知能力下降和记忆丧失为特征的进行性疾病。视觉Transformer（ViT）作为医学成像领域有前景的深度学习模型正在兴起，在AD的检测和诊断中具有潜在应用价值。
本综述系统地考察了关于ViT在AD检测中应用的近期研究，评估其诊断准确性以及网络架构对模型性能的影响。
我们在包括中国知网、CENTRAL（Cochrane对照试验中心注册库）、ScienceDirect、PubMed、Web of Science和Scopus在内的主要医学数据库中进行了系统检索，涵盖2020年1月1日至2024年3月1日发表的文献。还进行了手动检索以纳入相关灰色文献。纳入的论文基于神经影像数据，使用ViT模型对AD患者与健康对照进行检测，且纳入的研究使用了磁共振成像和正电子发射断层扫描。使用随机效应模型得出合并诊断准确性估计值，包括敏感性、特异性、似然比和诊断比值比。进行了亚组分析以比较不同ViT网络架构的诊断性能。
该荟萃分析纳入了11项研究，给出了95%置信区间和P值，显示合并诊断准确性为：敏感性0.925（95%置信区间0.892 - 0.959；P <.01），特异性0.957（95%置信区间0.932 - 0.981；P <.01），阳性似然比21.84（95%置信区间12.26 - 38.91；P <.01），阴性似然比0.08（95%置信区间0.05 - 0.14；P <.01）。曲线下面积显著较高，为0.924。这些发现突出了ViT作为早期准确诊断AD的有效工具的潜力，为未来基于神经影像的诊断方法提供了见解。
本系统综述为ViT模型在区分AD患者与健康对照方面的效用提供了有价值的证据，从而有助于基于神经影像的诊断方法的进步。
PROSPERO CRD42024584347；https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=584347 。
