Mirabian Sara, Mohammadian Fatemeh, Ganji Zohreh, Zare Hoda, Hasanpour Khalesi Erfan
Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Iran.
Medical Physics Research Center, Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
Neuroradiol J. 2025 Jan 9:19714009251313511. doi: 10.1177/19714009251313511.
The prevalence of neurodegenerative diseases has significantly increased, necessitating a deeper understanding of their symptoms, diagnostic processes, and prevention strategies. Frontotemporal dementia (FTD) and Alzheimer's disease (AD) are two prominent neurodegenerative conditions that present diagnostic challenges due to overlapping symptoms. To address these challenges, experts utilize a range of imaging techniques, including magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). These techniques facilitate a detailed examination of the manifestations of these diseases. Recent research has demonstrated the potential of artificial intelligence (AI) in automating the diagnostic process, generating significant interest in this field.
This narrative review aims to compile and analyze articles related to the AI-assisted diagnosis of FTD and AD. We reviewed 31 articles published between 2012 and 2024, with 23 focusing on machine learning techniques and 8 on deep learning techniques. The studies utilized features extracted from both single imaging modalities and multi-modal approaches, and evaluated the performance of various classification models.
Among the machine learning studies, Support Vector Machines (SVM) exhibited the most favorable performance in classifying FTD and AD. In deep learning studies, the ResNet convolutional neural network outperformed other networks.
This review highlights the utility of different imaging modalities as diagnostic aids in distinguishing between FTD and AD. However, it emphasizes the importance of incorporating clinical examinations and patient symptom evaluations to ensure comprehensive and accurate diagnoses.
神经退行性疾病的患病率显著上升，因此有必要更深入地了解其症状、诊断过程和预防策略。额颞叶痴呆（FTD）和阿尔茨海默病（AD）是两种主要的神经退行性疾病，由于症状重叠，在诊断方面存在挑战。为应对这些挑战，专家们采用了一系列成像技术，包括磁共振成像（MRI）、扩散张量成像（DTI）、功能磁共振成像（fMRI）、正电子发射断层扫描（PET）和单光子发射计算机断层扫描（SPECT）。这些技术有助于详细检查这些疾病的表现。最近的研究表明，人工智能（AI）在自动化诊断过程方面具有潜力，引起了该领域的广泛关注。
本叙述性综述旨在汇编和分析与AI辅助诊断FTD和AD相关的文章。我们回顾了2012年至2024年间发表的31篇文章，其中23篇关注机器学习技术，8篇关注深度学习技术。这些研究利用了从单一成像模态和多模态方法中提取的特征，并评估了各种分类模型的性能。
在机器学习研究中，支持向量机（SVM）在区分FTD和AD方面表现出最有利的性能。在深度学习研究中，ResNet卷积神经网络的表现优于其他网络。
本综述强调了不同成像模态作为区分FTD和AD的诊断辅助手段的实用性。然而，它强调了纳入临床检查和患者症状评估以确保全面准确诊断的重要性。
