Multimodal deep learning for Alzheimer's disease dementia assessment.

Autor: Qiu S; Department of Medicine, Boston University School of Medicine, Boston, MA, USA.; Department of Physics, College of Arts & Sciences, Boston University, Boston, MA, USA., Miller MI; Department of Medicine, Boston University School of Medicine, Boston, MA, USA., Joshi PS; Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA.; Department of General Dentistry, Boston University School of Dental Medicine, Boston, MA, USA.; The Framingham Heart Study, Boston University School of Medicine, Boston, MA, USA., Lee JC; Department of Medicine, Boston University School of Medicine, Boston, MA, USA., Xue C; Department of Medicine, Boston University School of Medicine, Boston, MA, USA.; Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA., Ni Y; Department of Medicine, Boston University School of Medicine, Boston, MA, USA., Wang Y; Department of Medicine, Boston University School of Medicine, Boston, MA, USA., De Anda-Duran I; School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA., Hwang PH; Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA., Cramer JA; Department of Radiology, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA., Dwyer BC; Department of Neurology, Boston University School of Medicine, Boston, MA, USA., Hao H; Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China., Kaku MC; Department of Neurology, Boston University School of Medicine, Boston, MA, USA., Kedar S; Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA.; Department Neurology, Emory University School of Medicine, Atlanta, GA, USA.; Department Ophthalmology, Emory University School of Medicine, Atlanta, GA, USA., Lee PH; Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA., Mian AZ; Department of Radiology, Boston University School of Medicine, Boston, MA, USA., Murman DL; Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA., O'Shea S; Department of Neurology, Boston University School of Medicine, Boston, MA, USA., Paul AB; Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA., Saint-Hilaire MH; Department of Neurology, Boston University School of Medicine, Boston, MA, USA., Alton Sartor E; Department of Neurology, Boston University School of Medicine, Boston, MA, USA., Saxena AR; Department of Neurology, Boston University School of Medicine, Boston, MA, USA., Shih LC; Department of Neurology, Boston University School of Medicine, Boston, MA, USA., Small JE; Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA., Smith MJ; Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA., Swaminathan A; Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA., Takahashi CE; Department of Neurology, Boston University School of Medicine, Boston, MA, USA., Taraschenko O; Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA., You H; Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China., Yuan J; Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China., Zhou Y; Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China., Zhu S; Department of Neurology, Boston University School of Medicine, Boston, MA, USA., Alosco ML; Department of Neurology, Boston University School of Medicine, Boston, MA, USA.; Boston University Alzheimer's Disease Research Center, Boston, MA, USA., Mez J; The Framingham Heart Study, Boston University School of Medicine, Boston, MA, USA.; Department of Neurology, Boston University School of Medicine, Boston, MA, USA.; Boston University Alzheimer's Disease Research Center, Boston, MA, USA., Stein TD; Boston University Alzheimer's Disease Research Center, Boston, MA, USA.; Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA, USA.; Boston VA Healthcare System, Boston, MA, USA.; Bedford VA Healthcare System, Bedford, MA, USA., Poston KL; Department of Neurology, Stanford University, Palo Alto, CA, USA., Au R; Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA.; The Framingham Heart Study, Boston University School of Medicine, Boston, MA, USA.; Department of Neurology, Boston University School of Medicine, Boston, MA, USA.; Boston University Alzheimer's Disease Research Center, Boston, MA, USA.; Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA., Kolachalama VB; Department of Medicine, Boston University School of Medicine, Boston, MA, USA. vkola@bu.edu.; Boston University Alzheimer's Disease Research Center, Boston, MA, USA. vkola@bu.edu.; Department of Computer Science, Boston University, Boston, MA, USA. vkola@bu.edu.; Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA. vkola@bu.edu.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2022 Jun 20; Vol. 13 (1), pp. 3404. Date of Electronic Publication: 2022 Jun 20.
DOI: 10.1038/s41467-022-31037-5
Abstrakt: Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these frameworks compare favorably with the diagnostic accuracy of practicing neurologists and neuroradiologists. Lastly, we apply interpretability methods in computer vision to show that disease-specific patterns detected by our models track distinct patterns of degenerative changes throughout the brain and correspond closely with the presence of neuropathological lesions on autopsy. Our work demonstrates methodologies for validating computational predictions with established standards of medical diagnosis.
(© 2022. The Author(s).)
Databáze: MEDLINE
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