Wu Qiong, Kiakou Dimitra, Mueller Karsten, Köhler Wolfgang, Schroeter Matthias L
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Clinic for Neurology, University of Leipzig Medical Center, Leipzig, Germany.
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic.
Neuroimage Clin. 2025;45:103757. doi: 10.1016/j.nicl.2025.103757. Epub 2025 Feb 17.
Frontotemporal lobar degeneration (FTLD) as the second most common dementia encompasses a range of syndromes and often shows overlapping symptoms with other subtypes or neurodegenerative diseases, which poses a significant clinical diagnostic challenge. Recent advancements in artificial intelligence (AI), specifically the application of machine learning (ML) algorithms to neuroimaging, have significantly progressed in addressing this challenge. This study aims to assess the diagnostic and predictive efficacy of neuroimaging feature-based AI algorithms for FTLD.
We conducted a systematic review and meta-analysis following PRISMA guidelines. We searched Pubmed, Scopus, and Web of Science for English-language, peer-reviewed studies using the following three umbrella terms: artificial intelligence, frontotemporal lobar degeneration, and neuroimaging modality. Our survey focused on computer-aided diagnosis for FTLD, employing machine/deep learning with neuroimaging radiomic features.
The meta-analysis includes 75 articles with 20,601 subjects, including 8,051 FTLD patients. The results reveal that FTLD can be automatically classified against healthy controls (HC) with pooled sensitivity and specificity of 86% and 89%, respectively. Likewise, FTLD versus Alzheimer's disease (AD) classification exhibits pooled sensitivity and specificity of 84% and 81%, while FTLD versus Parkinson's disease (PD) demonstrates pooled sensitivity and specificity of 84% and 75%, respectively. Classification performance distinguishing FTLD from atypical Parkinsonian syndromes (APS) showed pooled sensitivity and specificity of 84% and 79%, respectively. Multiclass classification sensitivity ranges from 42% to 100%, with lower sensitivity occurring in higher class distinctions (e.g., 5-class and 11-class).
Our study demonstrates the effectiveness of utilizing neuroimaging features to distinguish FTLD from HC, AD, APS, and PD in binary classification. Utilizing deep learning with multimodal neuroimaging data to differentiate FTLD subtypes and perform multiclassification among FTLD and other neurodegenerative disease holds promise for expediting diagnosis. In sum, the meta-analysis supports translation of machine learning tools in combination with imaging to clinical routine paving the way to precision medicine.
额颞叶变性（FTLD）是第二常见的痴呆症，涵盖一系列综合征，且常与其他亚型或神经退行性疾病表现出重叠症状，这给临床诊断带来了重大挑战。人工智能（AI）的最新进展，特别是机器学习（ML）算法在神经影像学中的应用，在应对这一挑战方面取得了显著进展。本研究旨在评估基于神经影像学特征的AI算法对FTLD的诊断和预测效能。
我们按照PRISMA指南进行了系统评价和荟萃分析。我们在PubMed、Scopus和Web of Science中搜索了使用以下三个总括词的英文、同行评审研究：人工智能、额颞叶变性和神经影像学模态。我们的调查重点是FTLD的计算机辅助诊断，采用具有神经影像学放射组学特征的机器学习/深度学习。
荟萃分析包括75篇文章，涉及20,601名受试者，其中包括8,051名FTLD患者。结果显示，FTLD与健康对照（HC）相比，合并敏感度和特异度分别为86%和89%，能够自动分类。同样，FTLD与阿尔茨海默病（AD）的分类中，合并敏感度和特异度分别为84%和81%，而FTLD与帕金森病（PD）的分类中，合并敏感度和特异度分别为84%和75%。区分FTLD与非典型帕金森综合征（APS）的分类性能，合并敏感度和特异度分别为84%和79%。多类分类敏感度范围为42%至100%，在较高类别区分（如5类和11类）中敏感度较低。
我们的研究证明了在二元分类中利用神经影像学特征区分FTLD与HC、AD、APS和PD的有效性。利用深度学习结合多模态神经影像学数据来区分FTLD亚型，并在FTLD和其他神经退行性疾病之间进行多类分类，有望加快诊断。总之，荟萃分析支持将机器学习工具与影像学结合应用于临床常规，为精准医学铺平道路。
