REVIEW
CHEN Chongchong, SHEN Xiaoming, MA Yunzhi, XIE Yanming, HAO Shinan, LV Mengke, ZHOU Guangsheng, MENG Mingxian
Parkinson’s disease (PD) is a common neurodegenerative disease, and its early diagnosis and identification are challenging. With the rapid development of machine learning technology, its application in PD diagnosis has received increasing attention. Compared with traditional diagnostic methods, machine learning technology shows the advantages of multimodality, non-invasive, less variation and personalization in PD diagnosis, but at this stage, the non-specificity of early PD symptoms, the high quality of the PD labelled data and privacy issues still limit the application of machine learning techniques to some extent. This paper reviewed the research progress of machine learning technology in PD diagnosis in recent years. Future research needs to further improve the data quality, construct models with more generalisation ability, and explore biological explanations, etc., so that the close integration of machine learning technology and clinical medicine can provide more accurate diagnosis and better treatment options for PD patients.