AI-based diagnosis of frontotemporal dementia using neuroimaging: a systematic review and meta-analysis
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Published on: June 3, 2020
Yuqin Li1, Xuebing He2, Ni Li2
1Department of Neurology, Chengdu Sixth People's Hospital, Chengdu, China. 86478095@qq.com.
Objective:
This meta-analysis aimed to evaluate the diagnostic accuracy of neuroimaging-based artificial intelligence (AI) models for detecting frontotemporal dementia (FTD) and its primary clinical subtypes.
Materials And Methods:
A systematic search of PubMed, Embase, Web of Science, and Cochrane Library was conducted up to October 22, 2025. Studies reporting neuroimaging-based AI model performance for diagnosing FTD variants against cognitively normal (CN) controls were included. Study quality and outcome certainty were assessed using the PROBAST + AI tool and GRADE. A bivariate random-effects model was used to calculate pooled sensitivity, specificity, and the area under the curve (AUC).
Results:
Twenty-three studies involving 3670 subjects were included. AI models demonstrated a pooled sensitivity of 0.91 (95% CI: 0.87-0.93; I2 = 73.2%), specificity of 0.92 (95% CI: 0.89-0.94; I2 = 80.2%), and an AUC of 0.96 (95% CI: 0.94-0.98). Subtype analysis revealed the highest performance for semantic variant primary progressive aphasia (svPPA), with an AUC of 0.98 (95% CI: 0.97-0.99). Across neuroimaging modalities (sMRI, fMRI, PET, and multimodal), AUC values remained consistently robust, ranging from 0.94 to 0.98. Notably, a temporal analysis indicated a slight declining trend in reported AUC values from 2009 to 2025, though most models maintained an AUC above 0.90.
Conclusions:
Neuroimaging-based AI models show promising diagnostic performance for FTD, especially for the svPPA subtype, supporting their potential as a clinical adjunct. However, the evidence certainty is limited by frequent high risk of bias and study heterogeneity. Prospective, multicenter validation is required to confirm generalizability and clinical utility.
Registration Number:
CRD420251270006.
Key Points:
Question Can neuroimaging-based artificial intelligence accurately distinguish frontotemporal dementia and its major clinical subtypes from cognitively normal controls in published diagnostic studies? Findings Across 23 studies, pooled accuracy was high, but estimates were limited by heterogeneity, small subgroup samples, and frequent validation bias. Clinical relevance AI models may support FTD diagnostic workflows, but current evidence favors cautious adjunctive use rather than stand-alone clinical implementation until prospective external validation improves.
