Early diagnosis and clinical score prediction of Parkinson's disease based on longitudinal neuroimaging data

Lei，Haijun1; Lei，Yukang1; Chen，Zihao1; Li，Shiqi1; Huang，Zhongwei1; Zhou，Feng2; Tan，Ee Leng3; Xiao，Xiaohua4; Lei，Yi4; Hu，Huoyou4; Huang，Yaohui4; Liu，Chien Hung5; Lei，Baiying6,7

2023

10.1007/s00521-023-08508-x

Neural Computing and Applications   影响因子和分区

0941-0643

1433-3058

Parkinson's disease (PD) is an irreversible neurodegenerative disease that has serious impacts on patients' lives. To provide timely accurate treatment and delay the deterioration of the disease, the accurate early diagnosis and clinical score prediction of PD are extremely important. Differing from previous studies on PD, we propose a network combining feature selection method with feature learning to obtain the most discriminative feature representation for longitudinal early diagnosis and clinical score prediction. Specifically, we first preprocess the multi-modal neuroimaging data at multiple time points to extract original longitudinal multi-modal features. Then, the feature selection method is performed to preliminary reduce the feature dimensions. Finally, the stacked sparse nonnegative autoencoder (SSNAE) is employed to obtain more discriminative longitudinal multi-modal features to improve the accuracy of early diagnosis and clinical score prediction at multiple time points. To verify our proposed network, diverse and extensive experiments are performed on the Parkinson's Progression Markers Initiative dataset, which aims to identify biological markers of PD risk, onset and progression. The results demonstrate that our proposed method is more efficient and achieves promising performance on both longitudinal early diagnosis and clinical scores prediction compared to state-of-the-art methods.

Classification Early diagnosis Longitudinal data Parkinson’s disease Prediction

SCI ; EI

英语

通讯

National Natural Science Foundation of China["62276172","61871274","61801305","U22A2024"] ; National Natural Science Foundation of Guangdong Province["2019A1515111205","2020A1515010649"] ; Shenzhen Science and Technology Program["JCYJ20220818095809021","JCYJ20190808165209410","KCXFZ20201221173213036"] ; (Key) Project of Department of Education of Guangdong Province[2019KZDZX1015] ; NTUTSZU Joint Research Program["2023006","2021002"] ; Special Project in Key fields of General Universities of Guangdong Province[2019KZDZX1015]

Computer Science

Computer Science, Artificial Intelligence

WOS:000985438700002

SPRINGER LONDON LTD

20232014082064

Classification (of information) ; Computer aided diagnosis ; Deterioration ; Feature Selection ; Neurodegenerative diseases ; Neuroimaging

Biomedical Engineering:461.1 ; Medicine and Pharmacology:461.6 ; Information Theory and Signal Processing:716.1 ; Computer Applications:723.5 ; Imaging Techniques:746 ; Information Sources and Analysis:903.1 ; Materials Science:951

ENGINEERING

2-s2.0-85159026303

Scopus

1.Key Laboratory of Service Computing and Applications,Guangdong Province Key Laboratory of Popular High Performance Computers,College of Computer Science and Software Engineering,Shenzhen University,Shenzhen,China
2.Department of Industrial and Manufacturing,Systems Engineering,The University of Michigan,Dearborn,United States
3.School of Electrical & Electronic Engineering,Nanyang Technological University,Singapore,Singapore
4.First Affiliated Hospital of Shenzhen University,Health Science Center,Shenzhen University,Shenzhen,China
5.Computer Science and Information Engineering,National Taipei University of Technology,Taipei,Taiwan
6.National-Regional Key Technology Engineering Laboratory for Medical Ultrasound,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging,School of Biomedical Engineering,Health Science Center,Shenzhen University,Shenzhen,China
7.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Southern University of Science and Technology,Shenzhen,518055,China

Lei，Haijun,Lei，Yukang,Chen，Zihao,et al. Early diagnosis and clinical score prediction of Parkinson's disease based on longitudinal neuroimaging data[J]. Neural Computing and Applications,2023,35(22):16429-16455.

Lei，Haijun.,Lei，Yukang.,Chen，Zihao.,Li，Shiqi.,Huang，Zhongwei.,...&Lei，Baiying.(2023).Early diagnosis and clinical score prediction of Parkinson's disease based on longitudinal neuroimaging data.Neural Computing and Applications,35(22),16429-16455.

Lei，Haijun,et al."Early diagnosis and clinical score prediction of Parkinson's disease based on longitudinal neuroimaging data".Neural Computing and Applications 35.22(2023):16429-16455.