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

Journal: NeuroImage. Clinical
PMID: 39983552

Abstract

BACKGROUND AND OBJECTIVES: 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.

Authors

  • Qiong Wu
    Key Laboratory of Flexible Electronics (KLOFE) and Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing 211816, P. R. China.
  • Dimitra Kiakou
    Hellenic Open University, Patra, Greece. kiakoud@cbs.mpg.de.
  • Karsten Mueller
    Max Planck Institute for Human Cognitive and Brain Sciences & Clinic for Cognitive Neurology, University Hospital Leipzig, Germany.
  • Wolfgang Köhler
    Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Clinic for Neurology, University of Leipzig Medical Center, Leipzig, Germany.
  • Matthias L Schroeter
    Max Planck Institute for Human Cognitive and Brain Sciences & Clinic for Cognitive Neurology, University of Leipzig, and German Consortium for Frontotemporal Lobar Degeneration, Ulm, Germany.

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