title: Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis.
abstract: OBJECTIVE
  Mobile healthcare applications are becoming a growing trend. Also, the prevalence of dementia in modern society is showing a steady growing trend. Among degenerative brain diseases that cause dementia, Alzheimer disease (AD) is the most common. The purpose of this study was to identify AD patients using magnetic resonance imaging in the mobile environment.
  
  
  METHODS
  We propose an incremental classification for mobile healthcare systems. Our classification method is based on incremental learning for AD diagnosis and AD prediction using the cortical thickness data and hippocampus shape. We constructed a classifier based on principal component analysis and linear discriminant analysis. We performed initial learning and mobile subject classification. Initial learning is the group learning part in our server. Our smartphone agent implements the mobile classification and shows various results.
  
  
  RESULTS
  With use of cortical thickness data analysis alone, the discrimination accuracy was 87.33% (sensitivity 96.49% and specificity 64.33%). When cortical thickness data and hippocampal shape were analyzed together, the achieved accuracy was 87.52% (sensitivity 96.79% and specificity 63.24%).
  
  
  CONCLUSIONS
  In this paper, we presented a classification method based on online learning for AD diagnosis by employing both cortical thickness data and hippocampal shape analysis data. Our method was implemented on smartphone devices and discriminated AD patients for normal group.
authors.affiliation: Department of Biomedical Engineering, Korea University, Seoul, Korea.
authors.forename: Ga-Young
authors.initials: GY
authors.lastname: Lee
authors.affiliation: Department of Computer and Radio Communications Engineering, Korea University, Seoul, Korea.
authors.forename: Jeonghun
authors.initials: J
authors.lastname: Kim
authors.affiliation: Seoul National University Biomedical informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Korea. ; Systems Biomedical Informatics Research Center, Seoul National University, Seoul, Korea.
authors.forename: Ju Han
authors.initials: JH
authors.lastname: Kim
authors.affiliation: Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
authors.forename: Kiwoong
authors.initials: K
authors.lastname: Kim
authors.affiliation: Department of Biomedical Engineering, Korea University, Seoul, Korea.
authors.forename: Joon-Kyung
authors.initials: JK
authors.lastname: Seong
keywords: Alzheimer Disease; Artificial Intelligence; Classification; Delivery of Health Care; Mobile Health Units