An FDG-PET–Based Machine Learning Framework to Support Neurologic Decision-Making in Alzheimer Disease and Related Disorders

作者：Leland Barnard, Hugo Botha, Nick Corriveau‐Lecavalier, Jonathan Graff‐Radford, Ellen Dicks, Venkatsampath Gogineni, Gemeng Zhang, Brian J. Burkett, Derek Richard Johnson, Sean J. Huls, Aditya Khurana, John L. Stricker, Hoon-Ki Paul Min, Matthew L. Senjem, Winnie Z. Fan, Heather J. Wiste, Mary M. Machulda, Melissa Erin Murray, Dennis W. Dickson, Aivi T. Nguyen, Robert Ross Reichard, Jeffrey L. Gunter, Christopher G. Schwarz, Kejal Kantarci, Jennifer L. Whitwell, Keith A. Josephs, David S. Knopman, Bradley F. Boeve, Ronald Carl Petersen, Clifford R. Jack, Val J. Lowe, David Thomas Jones, for the Alzheimer's Disease Neuroimaging Initiative · 发表于：Neurology · 年份：2025 · DOI：10.1212/wnl.0000000000213831 · 被引用次数：21 · 研究领域：Dementia and Cognitive Impairment Research、Artificial Intelligence in Healthcare and Education、Machine Learning in Healthcare

BACKGROUND AND OBJECTIVES: Distinguishing neurodegenerative diseases is a challenging task requiring neurologic expertise. Clinical decision support systems (CDSSs) powered by machine learning (ML) and artificial intelligence can assist with complex diagnostic tasks by augmenting user capabilities, but workflow integration poses many challenges. We propose that a modeling framework based on fluorodeoxyglucose PET (FDG-PET) imaging can address these challenges and form the basis of an effective CDSS for neurodegenerative disease. METHODS: This retrospective study focused on FDG-PET images in a discovery cohort drawn from 3 research studies plus routine clinical patients. When selecting research study participants, the inclusion criterion was the availability of an FDG-PET image from within 2.5 years of diagnosis with 1 of 9 specific neurodegenerative syndromes or designation as unimpaired. Participants from disease groups were recruited from the clinical patient population while unimpaired participants came primarily from a population study. The discovery cohort was used to develop a clinical decision support framework we call StateViewer, which applies a neighbor matching algorithm to detect the presence of 9 different neurodegenerative phenotypes. The ML performance of this framework was evaluated in the discovery cohort by nested cross-validation and externally validated in the Alzheimer's Disease Neuroimaging Initiative. Potential for clinical integration was demonstrated ...