Deep Learning Analysis of Retinal Structures and Risk Factors of Alzheimer's Disease.

Journal: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
PMID: 40040194

Abstract

The importance of early Alzheimer's Disease screening is becoming more apparent, given the fact that there is no way to revert the patient's status after the onset. However, the diagnostic procedure of Alzheimer's Disease involves a comprehensive analysis of cognitive tests, blood sampling, and imaging, which limits the screening of a large population in a short period. Preliminary works show that rich neurological and cardiovascular information is encoded in the patient's eye. Due to the relatively fast and easy procedure acquisition, early-stage screening of Alzheimer's Disease patients with eye images holds great promise. In this study, we employed a deep neural network as a framework to investigate the relationship between risk factors of Alzheimer's Disease and retinal structures. Our result shows that the model not only can predict several risk factors above the baseline but also can discover the relationship between the retinal structures and risk factors to provide insights into the retinal imaging biomarkers of Alzheimer's disease.

Authors

  • Seowung Leem
  • Yunchao Yang
  • Adam J Woods
    Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, USA; Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, USA. Electronic address: ajwoods@phhp.ufl.edu.
  • Ruogu Fang
    J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL.

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