Abstract

Medial temporal atrophy is one of the diagnostic biomarkers for Alzheimer’s disease (AD), but because of its limited specificity at this region alone, structural changes throughout the brain need to be investigated. We developed an artificial intelligence (AI) algorithm integrating voxel-based morphometry and support vector machine to extract features from the entire brain, used the AD Neuroimaging Initiative database for training, and evaluated its utility in several cohorts. This AI outperformed expert radiologists for AD diagnosis-the mean accuracy of two radiologists was 63.8%, whereas that of the AI was 90.5%. The accuracy for AD diagnosis in several test datasets ranged from 88.0%-94.2%, and increased to 92.5%-100% when the Mini-Mental State Examination score was included. The prediction accuracy for mild cognitive impairment (MCI) progression was 83.2%, which was equal to the highest value reported in previous studies. In the AI-positive subjects, 97.6% of the AD and 91.9% of progressive MCI patients had AD pathology, defined as cerebrospinal fluid positive for amyloid beta (Aβ) and phosphorylated tau, indicating the usefulness of the algorithm for predicting AD pathology. The hazard ratio for MCI progression was 2.1 for Aβ-positive patients and 3.6 for AI-positive subjects. Since the results were based on a database specific to AD, they do not directly reflect actual clinical performance. But the AI could help clinicians use brain MRI as a biomarker in the clinical setting.

Keywords: Alzheimer’s disease; Artificial intelligence; Cognitive impairment; Machine learning; Magnetic resonance imaging