Modular machine learning for Alzheimer's disease classification from retinal vasculature.

Journal: Scientific reports
Published Date: Jan 8, 2021

Abstract

Alzheimer'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.

Authors

  • Jianqiao Tian
    J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, 32611, USA.
  • Glenn Smith
    Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, 32611, USA.
  • Han Guo
    College of Electrical Engineering, Zhejiang University, Hangzhou, 310000, China.
  • Boya Liu
    School of Transportation Engineering, Tongji University, 4800 Cao'an Highway, Shanghai 201804, China. Electronic address: hatakeya@sohu.com.
  • Zehua Pan
    School of Electrical and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China.
  • Zijie Wang
    Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA.
  • Shuangyu Xiong
    Department of Data Science, East China Normal University, Shanghai, 200062, China.
  • Ruogu Fang
    J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL.

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