Chinese Journal of Tissue Engineering Research ›› 2021, Vol. 25 ›› Issue (24): 3796-3803.doi: 10.12307/2021.082

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Computer aided diagnosis of lumbar disc degeneration based on metabolomics

Jiang Lihong1, Wu Xiaofeng1, Ouyang Lin2, 3, Luo Aifang2, 3, Huang Li2, 3   

  1. 1School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000, Fujian Province, China; 2Institute of Medical Imaging, School of Medicine, Xiamen University, Zhangzhou 363000, Fujian Province, China; 3Department of Medical imaging, PLA 909th Hospital, Zhangzhou 363000, Fujian Province, China
  • Received:2020-07-27 Revised:2020-07-29 Accepted:2020-08-25 Online:2021-08-28 Published:2021-03-05
  • Contact: Wu Xiaofeng, Professor, Doctoral supervisor, School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000, Fujian Province, China Ouyang Lin, MD, Chief physician, Institute of Medical Imaging, School of Medicine, Xiamen University, Zhangzhou 363000, Fujian Province, China; Department of Medical imaging, PLA 909th Hospital, Zhangzhou 363000, Fujian Province, China
  • About author:Jiang Lihong, Master, School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000, Fujian Province, China
  • Supported by:
    the Science and Technology Project of Fujian Province, No. 2019Y31010067 (to OL); the Fund of the 909th Hospital Youth Nursery, No. 18Y021 (to LAF) 

Abstract: BACKGROUND: The diagnosis of lumbar disc degeneration is of great significance for the prevention of lumbar disease, and the diagnosis of lumbar disc degeneration mainly relies on the subjective evaluation of the imaging physician, which is likely to misjudge because of insufficient experience. 
OBJECTIVE: To propose a computer-aided diagnosis technique for classification on the lumbar disc degeneration, and to provide reference for imaging doctors. 
METHODS: Spearman correlation analysis is used to verify the correlation between magnetic resonance imaging metabolic indices of lumbar intervertebral disc and the Pfirrmann grades of lumbar disc degeneration. Several classifiers for the intelligent diagnosis of lumbar disc degeneration are developed by means of machine learning strategies such as the Softmax regression, the neural network and the support vector machine. 
RESULTS AND CONCLUSSION: The result of correlation analysis showed that three metabolic indices such as fat fraction (FF) of adjacent upper and lower vertebral bodies of degenerative disc, T2* values were significantly correlated with lumbar disc degeneration. The classification accuracy of the softmax regression, the neural network and the support vector machine respectively was 0.477, 0.515 and 0.523, and kappa’s coefficient of these three diagnostic models was 0.311, 0.300 and 0.330, respectively. The actual analysis indicates that it is feasible to establish a computer-aided intelligent diagnosis of lumbar disc degeneration by using the MRI metabolic indices, showing a promising approach for the diagnosis of lumbar disc degeneration.

Key words: bone, lumbar disc degeneration, MRI, Pfirrmann level, Spearman correlation analysis, softmax regression, neural network, support vector machine, metabolic indices

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