Development of a Deep Learning Model for Early Alzheime's Disease Detection from Structural MRIs and External Validation on an Independent Cohort

Early diagnosis of Alzheimers disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimers disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression. We validate both models on an internal held-out cohort from The Alzheimers Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimers Coordinating Center (NACC). The deep-learning model is more accurate and significantly faster than the volume/thickness model. The model can also be used to forecast progression: subjects with mild cognitive impairment misclassified as having mild Alzheimers disease dementia by the model were faster to progress to dementia over time. An analysis of the features learned by the proposed model shows that it relies on a wide range of regions associated with Alzheimers disease. These findings suggest that deep neural networks can automatically learn to identify imaging biomarkers that are predictive of Alzheimers disease, and leverage them to achieve accurate early detection of the disease.