Dataset Information

Multimodal deep learning for Alzheimer's disease dementia assessment.

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.

SUBMITTER: Qiu S

PROVIDER: S-EPMC9209452 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

Multimodal deep learning for Alzheimer's disease dementia assessment.

Qiu Shangran S Miller Matthew I MI Joshi Prajakta S PS Lee Joyce C JC Xue Chonghua C Ni Yunruo Y Wang Yuwei Y De Anda-Duran Ileana I Hwang Phillip H PH Cramer Justin A JA Dwyer Brigid C BC Hao Honglin H Kaku Michelle C MC Kedar Sachin S Lee Peter H PH Mian Asim Z AZ Murman Daniel L DL O'Shea Sarah S Paul Aaron B AB Saint-Hilaire Marie-Helene MH Alton Sartor E E Saxena Aneeta R AR Shih Ludy C LC Small Juan E JE Smith Maximilian J MJ Swaminathan Arun A Takahashi Courtney E CE Taraschenko Olga O You Hui H Yuan Jing J Zhou Yan Y Zhu Shuhan S Alosco Michael L ML Mez Jesse J Stein Thor D TD Poston Kathleen L KL Au Rhoda R Kolachalama Vijaya B VB

Nature communications 20220620 1

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 cap ...[more]

PMID: 35725739

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Dataset Information

Multimodal deep learning for Alzheimer's disease dementia assessment.

Publications

Multimodal deep learning for Alzheimer's disease dementia assessment.

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