PIDGN: An explainable multimodal deep learning framework for early prediction of Parkinson's disease.

Journal: Journal of neuroscience methods
PMID: 39832626

Abstract

BACKGROUND: Parkinson's disease (PD), the second most common neurodegenerative disease in the world, is usually not diagnosed until the later stages of the disease, when patients might have already missed the best treatment period. Therefore, more effective prediction methods based on artificial intelligence (AI) are needed to assist physicians in timely diagnosis.

Authors

  • Wenjia Li
    School of Mathematics and Statistics, Ludong University, Yantai 264025, China.
  • Quanrui Rao
    School of Information and Electrical Engineering, Ludong University, Yantai 264025, China.
  • Shuying Dong
    School of Mathematics and Statistics, Ludong University, Yantai 264025, China.
  • Mengyuan Zhu
    Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China.
  • Zhen Yang
    CAS Max-Planck Partner Institute for Computational Biology, Shanghai Institute of Biological Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
  • Xianggeng Huang
    School of Mathematics and Statistics, Ludong University, Yantai 264025, China.
  • Guangchen Liu
    School of Mathematics and Statistics, Ludong University, Yantai 264025, China. Electronic address: liuguangchen@ldu.edu.cn.

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