Attention-enhanced dilated convolution for Parkinson's disease detection using transcranial sonography.

Journal: Biomedical engineering online
Published Date: Jul 31, 2024

Abstract

BACKGROUND: Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis.

Authors

  • Shuang Chen
    The Beijing Genomics Institute (BGI), Shenzhen 518083, China. chenss@connect.hku.hk.
  • Yuting Shi
    National Clinical Research Center of Oral Diseases, Shanghai 200011, China.
  • Linlin Wan
    Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410083, China.
  • Jing Liu
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Yongyan Wan
    School of Computer Science and Engineering, Central South University, No.932 South Lushan Road, Changsha, 410083, Hunan, China.
  • Hong Jiang
    Department of Neurosurgery, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Rong Qiu
    Department of Respiratory and Critical Care Medicine, Suining Central Hospital, Suining, Sichuan, China.

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