Multimodal deep learning for Alzheimer's disease dementia assessment.
Nat Commun
; 13(1): 3404, 2022 06 20.
Article
in En
| MEDLINE
| ID: mdl-35725739
ABSTRACT
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Alzheimer Disease
/
Cognitive Dysfunction
/
Deep Learning
Type of study:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Nat Commun
Year:
2022
Document type:
Article