Boostering diagnosis of frontotemporal lobar degeneration with AI-driven neuroimaging – A systematic review and meta-analysis

• Published in: NeuroImage clinical Vol. 45; p. 103757
• Main Authors: Wu, Qiong, Kiakou, Dimitra, Mueller, Karsten, Köhler, Wolfgang, Schroeter, Matthias L.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.01.2025; Elsevier
• Subjects: Artificial Intelligence; Frontotemporal lobar degeneration; Frontotemporal Lobar Degeneration - diagnosis; Frontotemporal Lobar Degeneration - diagnostic imaging; Humans; Machine Learning; Meta-analysis; Neuroimaging; Neuroimaging - methods; Radiology; Regular; Neuroimaging; Frontotemporal lobar degeneration; Machine Learning; Artificial Intelligence; Meta-analysis
• ISSN: 2213-1582; 2213-1582
• DOI: 10.1016/j.nicl.2025.103757

•First meta-analysis assessing diagnostic efficacy of neuroimaging-based AI algorithms to identify FTLD and its subtypes.•Effects of various neuroimaging modalities and AI/ML algorithms on diagnostic performance were evaluated.•Meta-analysis includes 75 articles with 20,601 subjects, including 8,051 FTLD patients, ensures high statistical validity.•FTLD can be diagnosed with high accuracy with imaging compared to controls and other diseases.•AI/ML combined with multimodal neuroimaging improves diagnostic accuracy for FTLD. 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.