Advances in Automatic Classification and Prediction Study of Neuropsychiatric Diseases
Chen Xiaoyi1,3,4, Zhou Jing1,3,4,5, Ke Pengfei1,3,4, Kong Lingyin1,3,4, Wu Fengchun2,3, Wu Kai1,2,3,4,5,6,7,8#*
1(Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China) 2(The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou 510370, China) 3(Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China) 4(Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou 510500, China) 5(National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China) 6(Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou 510006, China) 7(National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China) 8(Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai980-8575, Japan)
Abstract：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 neuropsychiatric diseases. In this paper, we reviewed recent research progress of automated classification and prediction of neuropsychiatric 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 neuropsychiatric diseases, including schizophrenia, depression, Alzheimer‘s disease and Parkinson's disease. It was shown that current studies 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.
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