Author:
Tian Jianqiao,Smith Glenn,Guo Han,Liu Boya,Pan Zehua,Wang Zijie,Xiong Shuangyu,Fang Ruogu
Abstract
AbstractAlzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get early treatment by having routine screenings. However, current clinical diagnostic imaging tools do not meet the specific requirements for screening procedures due to high cost and limited availability. In this work, we took the initiative to evaluate the retina, especially the retinal vasculature, as an alternative for conducting screenings for dementia patients caused by Alzheimer's disease. Highly modular machine learning techniques were employed throughout the whole pipeline. Utilizing data from the UK Biobank, the pipeline achieved an average classification accuracy of 82.44%. Besides the high classification accuracy, we also added a saliency analysis to strengthen this pipeline's interpretability. The saliency analysis indicated that within retinal images, small vessels carry more information for diagnosing Alzheimer's diseases, which aligns with related studies.
Publisher
Springer Science and Business Media LLC
Reference50 articles.
1. Fosnacht, A. M. et al. From brain disease to brain health: primary prevention of Alzheimer’s disease and related disorders in a health system using an electronic medical record-based approach. J. Prev. Alzheimer’s Dis. 4(3), 157–164 (2017).
2. Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 7(3), 280–292 (2011).
3. McKhann, G. M. et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 7(3), 263–269 (2011).
4. Hyman, B. T. et al. National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease. Alzheimer’s Dement. 8(1), 1–13 (2012).
5. Ding, Y. et al. A deep learning model to predict a diagnosis of Alzheimer disease by using 18 F-FDG PET of the brain. Radiology 290(3), 456–464 (2019).
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