ArticleScientific reports2021
Predicting postoperative pain following root canal treatment by using artificial neural network evaluation.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Application, performance and limitations of artificial intelligence for the diagnosis and prediction of oro-facial pain: a systematic review.Frontiers in oral health · 2026Pooled it
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- From the Pain Matrix to Functional Networks: A Narrative Review of Chronic Pain Mechanisms Across Adult and Pediatric Populations with Emerging AI Perspectives.Brain sciences · 2026Review
- AI in Root Canal Morphology: A Detailed Bibliometric Analysis of Research Trends and Global Contributions.Clinical and experimental dental research · 2025Review
- Artificial Intelligence in The Diagnosis, Treatment, and Prognostication in Endodontics: A Comprehensive Literature Review.European endodontic journal · 2025Review
- Prediction of ultrafiltration volume in maintenance hemodialysis patients using an artificial neural network model based on body composition information.BMC medical informatics and decision making · 2025Article
- Artificial intelligence (AI) in restorative dentistry: current trends and future prospects.BMC oral health · 2025Review
- The Use of Artificial Intelligence in Endodontics.Journal of dental research · 2024Review
- Empowering Modern Dentistry: The Impact of Artificial Intelligence on Patient Care and Clinical Decision Making.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review.Bioengineering (Basel, Switzerland) · 2024Review
- Using artificial intelligence to improve pain assessment and pain management: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Developments and Performance of Artificial Intelligence Models Designed for Application in Endodontics: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models.ACS chemical neuroscience · 2023Article
- Ultrasound-Triggered on Demand Lidocaine Release Relieves Postoperative Pain.Frontiers in bioengineering and biotechnology · 2022Article
- Awareness of endodontists regarding the determination of root canal morphology and configuration using artificial intelligence.Journal of oral biology and craniofacial researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.