Advances in Automatic Classification and Prediction Study of Neuropsychiatrie Diseases

作者：Xiaoyi Chen; Jing Zhou; Pengfei Ke; Lingyin Kong; Fengchun Wu; Kai Wu^*

来源：Chinese Journal of Biomedical Engineering, 2021, 40(6): 752-763.

DOI：10.3969/j.issn.0258-8021.2021.06.12

• classification and prediction
• machine learning
• neuroimaging
• neuropsychiatrie diseases

There are still many unknown neuropathological mechanisms of neuropsychiatric diseases, and objective clinical diagnostic criteria are lacking, which brings great challenges to the diagnosis and prognosis of neuropsychiatric diseases. With the rapid development of neuroimaging technology, neuroimaging data have been widely used to explore the neuropathological mechanism and potential biomarkers of neuropsychiatric diseases. Compared with traditional univariate analysis methods, that can only perform population-level analyses, neuroimaging-data-driven machine learning models can realize individualized and automated prediction of neuropsychiatrie diseases. In this paper, we reviewed recent research progress of automated classification and prediction of neuropsychiatrie diseases based on machine learning technology, and summarized and analyzed the basic principles of machine learning technology and the latest research achievements of four typical neuropsychiatrie diseases, including schizophrenia, depression, Alzheimer's disease and Parkinson's disease. It was shown that current stuthes still face the challenge of small sample size and low reproducibility. Nonetheless, the sample size can be increased through collaborative analysis of multi-site data in the future. Meanwhile, deep learning and cross-disease diagnosis and prediction are also important directions of future research. ? 2021 Chinese Academy of Medical Sciences.

• 单位
  广州医科大学; 华南理工大学