AUTHOR=Aresta Simona , Nemni Raffaello , Zanardo Moreno , Sirabian Graziella , Capelli Dario , Alì Marco , Vitali Paolo , Bertoldo Enrico Giuseppe , Fiolo Valentina , Bonanno Lilla , Maresca Giuseppa , Battista Petronilla , Sardanelli Francesco , Pizzini Francesca Benedetta , Castiglioni Isabella , Salvatore Christian 

TITLE=AI-based staging, causal hypothesis and progression of subjects at risk of Alzheimer’s disease: a multicenter study

JOURNAL=Frontiers in Neurology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1568086

DOI=10.3389/fneur.2025.1568086

ISSN=1664-2295

ABSTRACT=IntroductionIn 2024, 11 European scientific societies/organizations and one patient advocacy association have defined a patient-centered biomarker-based diagnostic workflow for memory clinics evaluating neurocognitive disorders.MethodsWe tested the performance of an artificial intelligence (AI) tool applied to neuropsychological and magnetic resonance imaging (MRI) assessment for staging and causal hypothesis, which are the two recommended workflow steps guiding the next one recommending optimal biomarkers to be used for a biological diagnosis of neurocognitive disorders, according to intersocietal recommendations. Moreover, we assessed the AI performance in predicting the progression to Alzheimer’s disease (AD)-dementia.ResultsFor the three-class classification of staging (n patients = 426), the inter-rater AI-humans agreement was substantial for both healthy subjects/subjective cognitive impairment/worried-well vs. all the remaining groups (rest) (Cohen’s κ = 0.81) and mild cognitive impairment/mild dementia vs. rest κ = 0.70) classification, almost perfect for moderate/severe dementia vs. rest κ =0.90) classification. For the three-class classification of causal hypotheses (n = 112), the AI performance vs. biomarker-based diagnosis was: positive predictive value 91% [95% CI: 84–96%]; negative predictive value 100%, and accuracy 91% [84–96%]. For the binary classification of progression or not progression to AD-dementia at 24-month, with clinical conversion as a reference standard (n = 341), the AI performance was: sensitivity 89% [84–94%], specificity 82% [77–87%]; accuracy 85% [81–89%]; and area under the receiver operating characteristic curve 83% [79–87%].DiscussionThe AI tool showed high agreement with human assessment for staging, high accuracy with biomarkers for causal hypotheses of neurocognitive disorders and predicted progression to AD at 24-month with 89% sensitivity and 82% specificity.