A Practical Alzheimer Disease Classifier via Brain Imaging-Based Deep Learning on 85,721 Samples

COEX 

We developed a robust brain MRI-based Alzheimer’s disease (AD) diagnostic classifier using deep learning/transfer learning on an extensive dataset from over 217 sites/scanners (85,721 scans, 50,876 participants, January 2017 to August 2021). Employing the Inception-ResNet-V2 network as a base model for sex classification yielded 94.9% accuracy. Transfer learning for AD diagnosis achieved 90.9% accuracy in cross-validation on ADNI (6,857 samples) and 94.5%/93.6%/91.1% accuracy on three independent datasets (AIBL, MIRIAD, OASIS). Testing on unseen mild cognitive impairment (MCI) patients revealed 65.2% accuracy in predicting MCI to AD conversion, outperforming non-converters (20.6%). Predicted scores correlated significantly with illness severity. Our AD classifier, exhibiting high accuracy and potential for clinical integration, represents a significant advancement in neuroimaging diagnostics.