Christodoulou Rafail C, Woodward Amanda, Pitsillos Rafael, Ibrahim Reina, Georgiou Michalis F
Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Department of Neurophysiology, The Cyprus Institute of Neurology and Genetics, 2371 Nicosia, Cyprus.
J Clin Med. 2025 Aug 21;14(16):5913. doi: 10.3390/jcm14165913.
Artificial intelligence (AI) is reshaping neuroimaging workflows for Alzheimer's disease (AD) diagnosis, particularly through PET and MRI analysis advances. Since the FDA approval of Tauvid, a PET tracer targeting tau pathology, there has been a notable increase in studies applying AI to neuroimaging data. This narrative review synthesizes recent, high-impact literature to highlight clinically relevant AI applications in AD imaging. This review examined peer-reviewed studies published between January 2020 and January 2025, focusing on the use of AI, including machine learning, deep learning, and hybrid models for diagnostic and prognostic tasks in AD using PET and/or MRI. Studies were identified through targeted PubMed, Scopus, and Embase searches, emphasizing methodological diversity and clinical relevance. A total of 111 studies were categorized into five thematic areas: Image preprocessing and segmentation, diagnostic classification, prognosis and disease staging, multimodal data fusion, and emerging innovations. Deep learning models such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based architectures were widely employed by the research community in the field of AD. At the same time, several models reported strong diagnostic performance, but methodological challenges such as reproducibility, small sample sizes, and lack of external validation limit clinical translation. Trends in explainable AI, synthetic imaging, and integration of clinical biomarkers are also discussed. AI is rapidly advancing the field of AD imaging, offering tools for enhanced segmentation, staging, and early diagnosis. Multimodal approaches and biomarker-guided models show particular promise. However, future research must focus on reproducibility, interpretability, and standardized validation to bridge the gap between research and clinical practice.
人工智能（AI）正在重塑用于阿尔茨海默病（AD）诊断的神经影像学工作流程，特别是通过正电子发射断层扫描（PET）和磁共振成像（MRI）分析的进展。自美国食品药品监督管理局（FDA）批准Tauvid（一种靶向tau蛋白病理学的PET示踪剂）以来，将AI应用于神经影像学数据的研究显著增加。本叙述性综述综合了近期具有高影响力的文献，以突出AD成像中与临床相关的AI应用。 本综述考察了2020年1月至2025年1月发表的同行评审研究，重点关注AI的应用，包括使用PET和/或MRI对AD进行诊断和预后任务的机器学习、深度学习和混合模型。通过针对性的PubMed、Scopus和Embase搜索确定研究，强调方法的多样性和临床相关性。 总共111项研究被分为五个主题领域：图像预处理和分割、诊断分类、预后和疾病分期、多模态数据融合以及新兴创新。卷积神经网络（CNN）、生成对抗网络（GAN）和基于Transformer的架构等深度学习模型在AD领域被研究界广泛采用。同时，一些模型报告了强大的诊断性能，但诸如可重复性、样本量小和缺乏外部验证等方法学挑战限制了临床转化。还讨论了可解释AI、合成成像和临床生物标志物整合的趋势。 AI正在迅速推动AD成像领域的发展，提供用于增强分割、分期和早期诊断的工具。多模态方法和生物标志物引导的模型显示出特别的前景。然而，未来的研究必须专注于可重复性、可解释性和标准化验证，以弥合研究与临床实践之间的差距。
