Using interpretable deep learning radiomics model to diagnose and predict progression of early AD disease spectrum: a preliminary [F]FDG PET study.

Journal: European radiology
PMID: 39477837

Abstract

OBJECTIVES: In this study, we propose an interpretable deep learning radiomics (IDLR) model based on [F]FDG PET images to diagnose the clinical spectrum of Alzheimer's disease (AD) and predict the progression from mild cognitive impairment (MCI) to AD.

Authors

  • Jiehui Jiang
    Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.
  • Chenyang Li
  • Jiaying Lu
    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
  • Jie Sun
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China.
  • Xiaoming Sun
    Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China.
  • Jiacheng Yang
    School of Computing, University of Leeds, UK.
  • Luyao Wang
    Department of Genetics, School of Life Sciences, Bengbu Medical University, Bengbu, China.
  • Chuantao Zuo
  • Kuangyu Shi
    Universitätsklinik für Nuklearmedizin, Inselspital University Hospital Bern, University of Bern, Bern, Switzerland.

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