Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis

• Published in: Neurobiology of aging Vol. 121; pp. 139 - 156
• Main Authors: Mirabnahrazam, Ghazal, Ma, Da, Beaulac, Cédric, Lee, Sieun, Popuri, Karteek, Lee, Hyunwoo, Cao, Jiguo, Galvin, James E, Wang, Lei, Beg, Mirza Faisal
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2023
• Subjects: Alzheimer Disease - diagnostic imaging; Alzheimer Disease - genetics; Alzheimer's disease; Cognitive Dysfunction - diagnosis; Cognitive Dysfunction - genetics; Deep learning; Disease Progression; Early detection; Humans; Magnetic Resonance Imaging - methods; Multi-modal data; Neuroimage genomics; Neuroimaging - methods; Survival Analysis; Deep learning; Survival analysis; Multi-modal data; Neuroimage genomics; Alzheimer's disease; Early detection

Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, and it is difficult to predict individual progression trajectory from normal or mildly impaired cognition to DAT. An in-depth examination of multiple modalities of data may yield an accurate estimate of tim...