The most widely used clinical Parkinson's Disease Rating Scale, which consists of four sections, including Non-motor Experience of Daily Life; II: Motor Experience of Daily Life; III: Motor Examination; IV: Motor Complications;
Published in Chapter:
Automatic Diagnosis of Parkinson's Disease Based on Deep Learning Models and Multimodal Data
Ling Li (Zhoushan Hospital, Wenzhou Medical University, China),
Fangyu Dai (Zhoushan Hospital, Wenzhou Medical University, China),
Songbin He (Zhoushan Hospital, Wenzhou Medical University, China), Hao Yu (The Second Affiliated Hospital, Zhejiang University School of Medicine, China), and
Haipeng Liu (Coventry University, UK)
Copyright: © 2024
|Pages: 22
DOI: 10.4018/979-8-3693-1281-0.ch009
Abstract
Parkinson's disease (PD) is a common age-related neurodegenerative disorder in the aging society. Early diagnosis of PD is particularly important for efficient intervention. Currently, the diagnosis of PD is mainly made by neurologists who assess the abnormalities of the patient's motor system and evaluate the severity according to established criteria, which is highly dependent on the neurologists' expertise and often unsatisfactory. Artificial intelligence provides new potential for automatic and reliable diagnosis of PD based on multimodal data analysis. Some deep learning models have been developed for automatic detection of PD based on diverse biomarkers such as brain imaging images, electroencephalograms, walking postures, speech, handwriting, etc., with promising accuracy. This chapter summarizes the state-of-the-art, technical advancements, unmet research gaps, and future directions of deep learning models for PD detection. It provides a reference for biomedical engineers, data scientists, and health professionals.