Medical Image Analysis, Volume 67, January 2021,
The enormous social and economic cost of Alzheimer's disease (AD) has driven a number of neuroimaging investigations for early detection and diagnosis. Towards this end, various computational approaches have been applied to longitudinal imaging data in subjects with Mild Cognitive Impairment (MCI), as serial brain imaging could increase sensitivity for detecting changes from baseline, and potentially serve as a diagnostic biomarker for AD. However, current state-of-the-art brain imaging diagnostic methods have limited utility in clinical practice due to the lack of robust predictive power. To address this limitation, we propose a flexible spatial-temporal solution to predict the risk of MCI conversion to AD prior to the onset of clinical symptoms by sequentially recognizing abnormal structural changes from longitudinal magnetic resonance (MR) image sequences. Firstly, our model is trained to sequentially recognize different length partial MR image sequences from different stages of AD. Secondly, our method is leveraged by the inexorably progressive nature of AD. To that end, a Temporally Structured Support Vector Machine (TS-SVM) model is proposed to constrain the partial MR image sequence's detection score to increase monotonically with AD progression. Furthermore, in order to select the best morphological features for enabling classifiers, we propose a joint feature selection and classification framework. We demonstrate that our early diagnosis method using only two follow-up MR scans is able to predict conversion to AD 12 months ahead of an AD clinical diagnosis with 81.75% accuracy.
Aged; Alzheimer Disease; Alzheimer's Disease; Anterior Commissure; Article; Binary Classification; Brain; Brain Mapping; Cerebrospinal Fluid; Classification (of Information); Clinical Practice; Cognitive Defect; Cognitive Dysfunction; Computational Approach; Controlled Study; Cross Validation; Detection And Diagnosis; Diagnosis; Diagnostic Biomarkers; Diagnostic Imaging; Diagnostic Test Accuracy Study; Digital Imaging And Communications In Medicine; Disease Exacerbation; Early AD Diagnosis; Early Diagnosis; Feature Extraction; Feature Selection; Feature Selection And Classification; Female; Follow Up; Human; Humans; Image Processing; Image Registration; Image Segmentation; Longitudinal Imaging; Longitudinal MRI; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Major Clinical Study; Male; Mild Cognitive Impairment; Mild Cognitive Impairments (MCI); Morphological Features; Neurodegenerative Diseases; Neuroimaging; Nuclear Magnetic Resonance Imaging; Phenotype; Priority Journal; Support Vector Machine; Support Vector Machines; Temporal Structured Support Vector Machine; White Matter; Global