Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease

• Published in: Translational research : the journal of laboratory and clinical medicine Vol. 194; pp. 56 - 67
• Main Authors: Liu, Xiaonan, Chen, Kewei, Wu, Teresa, Weidman, David, Lure, Fleming, Li, Jing
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-04-2018
• Subjects: Alzheimer Disease - diagnostic imaging; Artificial Intelligence; Cognitive Dysfunction - diagnostic imaging; Fluorodeoxyglucose F18; Humans; Machine Learning; Magnetic Resonance Imaging; Multimodal Imaging - methods; Positron-Emission Tomography; Prognosis; ADNI; FDA; CNN; MRI; ADAS-Cog; FDG; DL; SVM; AUC; CDR; MMSE; PLS; CSF; IWG; APOE; TBM; ML; AA; VBM; AD; ICA; NIA; AI; GP; SNAP; ROI; MKL; NPSEs; MCI; NC; SVDs; SUVR; PET
• ISSN: 1931-5244; 1878-1810
• DOI: 10.1016/j.trsl.2018.01.001

Alzheimer's disease (AD) is a major neurodegenerative disease and the most common cause of dementia. Currently, no treatment exists to slow down or stop the progression of AD. There is converging belief that disease-modifying treatments should focus on early stages of the disease, that is, the mild cognitive impairment (MCI) and preclinical stages. Making a diagnosis of AD and offering a prognosis (likelihood of converting to AD) at these early stages are challenging tasks but possible with the help of multimodality imaging, such as magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG)-positron emission topography (PET), amyloid-PET, and recently introduced tau-PET, which provides different but complementary information. This article is a focused review of existing research in the recent decade that used statistical machine learning and artificial intelligence methods to perform quantitative analysis of multimodality image data for diagnosis and prognosis of AD at the MCI or preclinical stages. We review the existing work in 3 subareas: diagnosis, prognosis, and methods for handling modality-wise missing data—a commonly encountered problem when using multimodality imaging for prediction or classification. Factors contributing to missing data include lack of imaging equipment, cost, difficulty of obtaining patient consent, and patient drop-off (in longitudinal studies). Finally, we summarize our major findings and provide some recommendations for potential future research directions.