Predicting postoperative pain following root canal treatment by using artificial neural network evaluation

Abstract 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 relatio...

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Autores principales:	Xin Gao, Xing Xin, Zhi Li, Wei Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7aedb08ad5e64a58a856af2f8d9f453a
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spelling	oai:doaj.org-article:7aedb08ad5e64a58a856af2f8d9f453a2021-12-02T15:09:15ZPredicting postoperative pain following root canal treatment by using artificial neural network evaluation10.1038/s41598-021-96777-82045-2322https://doaj.org/article/7aedb08ad5e64a58a856af2f8d9f453a2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96777-8https://doaj.org/toc/2045-2322Abstract 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.Xin GaoXing XinZhi LiWei ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xin Gao Xing Xin Zhi Li Wei Zhang Predicting postoperative pain following root canal treatment by using artificial neural network evaluation
description	Abstract 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	article
author	Xin Gao Xing Xin Zhi Li Wei Zhang
author_facet	Xin Gao Xing Xin Zhi Li Wei Zhang
author_sort	Xin Gao
title	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_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_sort	predicting postoperative pain following root canal treatment by using artificial neural network evaluation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7aedb08ad5e64a58a856af2f8d9f453a
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_version_	1718387882382589952

Predicting postoperative pain following root canal treatment by using artificial neural network evaluation

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