A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease

Mild cognitive impairment (MCI) has been described as the intermediary stage before Alzheimer's Disease - many people however remain stable or even demonstrate improvement in cognition. Early detection of progressive MCI (pMCI) therefore can be utilised in identifying at-risk individuals and directing additional medical treatment in order to revert conversion to AD as well as provide psychosocial support for the person and their family. This paper presents a novel solution in the early detection of pMCI people and classification of AD risk within MCI people. We proposed a model, MudNet, to utilise deep learning in the simultaneous prediction of progressive/stable MCI classes and time-to-AD conversion where high-risk pMCI people see conversion to AD within 24 months and low-risk people greater than 24 months. MudNet is trained and validated using baseline clinical and volumetric MRI data (n = 559 scans) from participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI). The model utilises T1-weighted structural MRIs alongside clinical data which also contains neuropsychological (RAVLT, ADAS-11, ADAS-13, ADASQ4, MMSE) tests as inputs. The averaged results of our model indicate a binary accuracy of 69.8% for conversion predictions and a categorical accuracy of 66.9% for risk classifications.