A Case-Control Clinical Trial on a Deep Learning-Based Classification System for Diagnosis of Amyloid-Positive Alzheimer's Disease

Objective A deep learning-based classification system (DLCS) which uses structural brain magnetic resonance imaging (MRI) to diagnose Alzheimer's disease (AD) was developed in a previous recent study. Here, we evaluate its performance by conducting a single-center, case-control clinical trial. Methods We retrospectively collected T1-weighted brain MRI scans of subjects who had an accompanying measure of amyloid-beta (A ss) positivity based on a 18F-florbetaben positron emission tomography scan. The dataset included 188 A ss-positive patients with mild cognitive impairment or dementia due to AD, and 162 A ss-negative controls with normal cognition. We calculated the sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) of the DLCS in the classification of A ss-positive AD patients from A ss-negative controls. Results The DLCS showed excellent performance, with sensitivity, specificity, positive predictive value, negative predictive value, and AUC of 85.6% (95% confidence interval [CI], 79.8-90.0), 90.1% (95% CI, 84.5-94.2), 91.0% (95% CI, 86.3-94.1), 84.4% (95% CI, 79.288.5), and 0.937 (95% CI, 0.911-0.963), respectively. Conclusion The DLCS shows promise in clinical settings where it could be routinely applied to MRI scans regardless of original scan purpose to improve the early detection of AD.