Forecast of pain degree of lumbar disc herniation based on back propagation neural network

Abstract: To further explore the pathogenic mechanism of lumbar disc herniation (LDH) pain, this study screens important imaging features that are significantly correlated with the pain score of LDH. The features with significant correlation imaging were included into a back propagation (BP) neural network model for training, including Pfirrmann classification, Michigan State University (MSU) regional localization (MSU protrusion size classification and MSU protrusion location classification), sagittal diameter index, sagittal diameter/transverse diameter index, transverse diameter index, and AN angle (angle between nerve root and protrusion). The BP neural network training model results showed that the specificity was 95 ± 2%, sensitivity was 91 ± 2%, and accuracy was 91 ± 2% of the model. The results show that the degree of intraspinal occupation of the intervertebral disc herniation and the degree of intervertebral disc degeneration are related to LDH pain. The innovation of this study is that the BP neural network model constructed in this study shows good performance in the accuracy experiment and receiver operating characteristic experiment, which completes the prediction task of lumbar Magnetic Resonance Imaging features for the pain degree of LDH for the first time, and provides a basis for subsequent clinical diagnosis.

Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch

Bibliographic citation
Forecast of pain degree of lumbar disc herniation based on back propagation neural network ; volume:18 ; number:1 ; year:2023 ; extent:18
Open life sciences ; 18, Heft 1 (2023) (gesamt 18)

Creator
Ren, Xinying
Liu, Huanwen
Hui, Shiji
Wang, Xi
Zhang, Honglai

DOI
10.1515/biol-2022-0673
URN
urn:nbn:de:101:1-2023090914075388885741
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
14.08.2025, 10:53 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Associated

  • Ren, Xinying
  • Liu, Huanwen
  • Hui, Shiji
  • Wang, Xi
  • Zhang, Honglai

Other Objects (12)