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

Abstract

Introduction: In 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. Methods: We 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. Results: = 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%]. Discussion: The 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.

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