Raza Muhammad Liaquat, Hassan Syed Tawassul, Jamil Subia, Hyder Noorulain, Batool Kinza, Walji Sajidah, Abbas Muhammad Khizar
Department of Infection Prevention Control, Ministry of National Guard Health Affairs (MNGHA), Riyadh, Saudi Arabia.
IPC, King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia.
Front Neuroinform. 2025 May 2;19:1557177. doi: 10.3389/fninf.2025.1557177. eCollection 2025.
Alzheimer's disease is a progressive neurodegenerative disorder challenging early diagnosis and treatment. Recent advancements in deep learning algorithms applied to multimodal brain imaging offer promising solutions for improving diagnostic accuracy and predicting disease progression.
This narrative review synthesizes current literature on deep learning applications in Alzheimer's disease diagnosis using multimodal neuroimaging. The review process involved a comprehensive search of relevant databases (PubMed, Embase, Google Scholar and ClinicalTrials.gov), selection of pertinent studies, and critical analysis of findings. We employed a best-evidence approach, prioritizing high-quality studies and identifying consistent patterns across the literature.
Deep learning architectures, including convolutional neural networks, recurrent neural networks, and transformer-based models, have shown remarkable potential in analyzing multimodal neuroimaging data. These models can effectively process structural and functional imaging modalities, extracting relevant features and patterns associated with Alzheimer's pathology. Integration of multiple imaging modalities has demonstrated improved diagnostic accuracy compared to single-modality approaches. Deep learning models have also shown promise in predictive modeling, identifying potential biomarkers and forecasting disease progression.
While deep learning approaches show great potential, several challenges remain. Data heterogeneity, small sample sizes, and limited generalizability across diverse populations are significant hurdles. The clinical translation of these models requires careful consideration of interpretability, transparency, and ethical implications. The future of AI in neurodiagnostics for Alzheimer's disease looks promising, with potential applications in personalized treatment strategies.
阿尔茨海默病是一种进行性神经退行性疾病，对早期诊断和治疗构成挑战。应用于多模态脑成像的深度学习算法的最新进展为提高诊断准确性和预测疾病进展提供了有前景的解决方案。
本叙述性综述综合了关于深度学习在使用多模态神经成像诊断阿尔茨海默病中的应用的当前文献。综述过程包括对相关数据库（PubMed、Embase、谷歌学术和ClinicalTrials.gov）进行全面搜索、选择相关研究以及对研究结果进行批判性分析。我们采用了最佳证据方法，优先考虑高质量研究并确定文献中的一致模式。
深度学习架构，包括卷积神经网络、循环神经网络和基于Transformer的模型，在分析多模态神经成像数据方面显示出显著潜力。这些模型可以有效地处理结构和功能成像模态，提取与阿尔茨海默病病理学相关的特征和模式。与单模态方法相比，多种成像模态的整合已证明诊断准确性有所提高。深度学习模型在预测建模、识别潜在生物标志物和预测疾病进展方面也显示出前景。
虽然深度学习方法显示出巨大潜力，但仍存在一些挑战。数据异质性、小样本量以及不同人群中有限的可推广性是重大障碍。这些模型的临床转化需要仔细考虑可解释性、透明度和伦理影响。人工智能在阿尔茨海默病神经诊断中的未来看起来很有前景，在个性化治疗策略中具有潜在应用。
