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Predicting postoperative pain following root canal treatment by using artificial neural network evaluation
This study aimed to evaluate the accuracy of back propagation (BP) artificial neural network model for predicting postoperative pain following root canal treatment (RCT). The BP neural network model was developed using MATLAB 7.0 neural network toolbox, and the functional projective relationship was...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8390654/ https://www.ncbi.nlm.nih.gov/pubmed/34446767 http://dx.doi.org/10.1038/s41598-021-96777-8 |
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author | Gao, Xin Xin, Xing Li, Zhi Zhang, Wei |
author_facet | Gao, Xin Xin, Xing Li, Zhi Zhang, Wei |
author_sort | Gao, Xin |
collection | PubMed |
description | This study aimed to evaluate the accuracy of back propagation (BP) artificial neural network model for predicting postoperative pain following root canal treatment (RCT). The BP neural network model was developed using MATLAB 7.0 neural network toolbox, and the functional projective relationship was established between the 13 parameters (including the personal, inflammatory reaction, operative procedure factors) and postoperative pain of the patient after RCT. This neural network model was trained and tested based on data from 300 patients who underwent RCT. Among these cases, 210, 45 and 45 were allocated as the training, data validation and test samples, respectively, to assess the accuracy of prediction. In this present study, the accuracy of this BP neural network model was 95.60% for the prediction of postoperative pain following RCT. To conclude, the BP network model could be used to predict postoperative pain following RCT and showed clinical feasibility and application value. |
format | Online Article Text |
id | pubmed-8390654 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83906542021-09-01 Predicting postoperative pain following root canal treatment by using artificial neural network evaluation Gao, Xin Xin, Xing Li, Zhi Zhang, Wei Sci Rep Article This study aimed to evaluate the accuracy of back propagation (BP) artificial neural network model for predicting postoperative pain following root canal treatment (RCT). The BP neural network model was developed using MATLAB 7.0 neural network toolbox, and the functional projective relationship was established between the 13 parameters (including the personal, inflammatory reaction, operative procedure factors) and postoperative pain of the patient after RCT. This neural network model was trained and tested based on data from 300 patients who underwent RCT. Among these cases, 210, 45 and 45 were allocated as the training, data validation and test samples, respectively, to assess the accuracy of prediction. In this present study, the accuracy of this BP neural network model was 95.60% for the prediction of postoperative pain following RCT. To conclude, the BP network model could be used to predict postoperative pain following RCT and showed clinical feasibility and application value. Nature Publishing Group UK 2021-08-26 /pmc/articles/PMC8390654/ /pubmed/34446767 http://dx.doi.org/10.1038/s41598-021-96777-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Gao, Xin Xin, Xing Li, Zhi Zhang, Wei Predicting postoperative pain following root canal treatment by using artificial neural network evaluation |
title | Predicting postoperative pain following root canal treatment by using artificial neural network evaluation |
title_full | Predicting postoperative pain following root canal treatment by using artificial neural network evaluation |
title_fullStr | Predicting postoperative pain following root canal treatment by using artificial neural network evaluation |
title_full_unstemmed | Predicting postoperative pain following root canal treatment by using artificial neural network evaluation |
title_short | Predicting postoperative pain following root canal treatment by using artificial neural network evaluation |
title_sort | predicting postoperative pain following root canal treatment by using artificial neural network evaluation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8390654/ https://www.ncbi.nlm.nih.gov/pubmed/34446767 http://dx.doi.org/10.1038/s41598-021-96777-8 |
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