Multimodal deep learning for Alzheimer’s disease dementia assessment

Qiu, Shangran; I. Miller, Matthew; Joshi, Prajakta; C. Lee, Joyce; Xue, Chonghua; Ni, Yunruo; Wang, Yuwei; De Anda‐Duran, Ileana; H Hwang, Phillip; Cramer, Justin; Dwyer, Brigid; Hao, Honglin; Kaku, Michelle; Kedar, Sachin; H. Lee, Peter; Mian, Asim; L. Murman, Daniel; A. O’Shea, Sarah; B. Paul, Aaron; Saint‐Hilaire, Marie; Alton Sartor, E.; Saxena, Aneeta; C. Shih, Ludy; E. Small, Juan; J. Smith, Maximilian; Swaminathan, Arun; Takahashi, Courtney; Taraschenko, Olga; You, Hui; Yuan, Jing; Zhou, Yan; Zhu, Shuhan; L. Alosco, Michael; Mez, Jesse; D. Stein, Thor; L. Poston, Kathleen; Au, Rhoda; B. Kolachalama, Vijaya

doi:10.1038/s41467-022-31037-5

Public

Multimodal deep learning for Alzheimer’s disease dementia assessment

Published in Nature Communications • Jun 20, 2022

Authors:

Shangran Qiu

Matthew I. Miller

Prajakta Joshi

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 accomplishe...

Subject

Dementia

Neuroimaging

Interpretability

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