Finger drawing on smartphone screens enables early Parkinson's disease detection through hybrid 1D-CNN and BiGRU deep learning architecture.

Journal: PloS one
Published Date: Jul 14, 2025

Abstract

BACKGROUND: Parkinson's disease (PD), a progressive neurodegenerative disorder prevalent in aging populations, manifests clinically through characteristic motor impairments including bradykinesia, rigidity, and resting tremor. Early detection and timely intervention may delay disease progression. Spiral drawing tasks have been established as effective auxiliary diagnostic tools. This study developed a hybrid deep learning model to analyze motion data from finger drawings of spiral and wave lines on smartphone screens, aiming to detect early Parkinson's disease.

Authors

  • Zhaohui Zhu
    Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China. zhuzhaohui316@163.com.
  • E Wu
    Rehabilitation and Nursing School, Hangzhou Vocational & Technical College, Hangzhou, China.
  • Pengfei Leng
    School of Public Health, Hangzhou Normal University, Hangzhou, 311121, Zhejiang, China.
  • Jiajun Sun
    Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
  • Mingming Ma
    Department of Diagnostic Ultrasound and Echocardiography, Sir Run Run Shaw Hospital, Zhejiang University College of Medicine, Hangzhou, People's Republic of China.
  • Zhigeng Pan
    School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing, 210044, China.

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