ArticleComputational intelligence and neuroscience2016
Retreatment Predictions in Odontology by means of CBR Systems.
Article in Computational intelligence and neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Predicting outcomes of nonsurgical retreatment with artificial intelligence: A multicenter retrospective study using logistic regression, random forest, and extreme gradient boosting.Journal of conservative dentistry and endodontics · 2026Article
- Artificial intelligence in endodontics: A comprehensive review of applications and advancement.Bioinformation · 2026Article
- Automated design prediction for definitive obturator prostheses: A case-based reasoning study.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2025Article
- A Three-Stage Fusion Neural Network for Predicting the Risk of Root Fracture-A Pilot Study.Bioengineering (Basel, Switzerland) · 2025Article
- Integrating Machine Learning and Deep Learning for Predicting Non-Surgical Root Canal Treatment Outcomes Using Two-Dimensional Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence and Dentistry: The Future.Journal of pharmacy & bioallied sciences · 2024Review
- Artificial intelligence application in endodontics: A narrative review.Imaging science in dentistry · 2024Review
- Artificial Intelligence in Endodontics: A Scoping Review.Iranian endodontic journal · 2024Article
- A brief exploration of artificial intelligence in dental healthcare: a narrative review.F1000Research · 2024Review
- ORIENTATE: automated machine learning classifiers for oral health prediction and research.BMC oral health · 2023Article
- Evaluation of the Diagnostic and Prognostic Accuracy of Artificial Intelligence in Endodontic Dentistry: A Comprehensive Review of Literature.Computational and mathematical methods in medicine · 2023Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The field of odontology requires an appropriate adjustment of treatments according to the circumstances of each patient. A follow-up treatment for a patient experiencing problems from a previous procedure such as endodontic therapy, for example, may not necessarily preclude the possibility of extraction. It is therefore necessary to investigate new solutions aimed at analyzing data and, with regard to the given values, determine whether dental retreatment is required. In this work, we present a decision support system which applies the case-based reasoning (CBR) paradigm, specifically designed to predict the practicality of performing or not performing a retreatment. Thus, the system uses previous experiences to provide new predictions, which is completely innovative in the field of odontology. The proposed prediction technique includes an innovative combination of methods that minimizes false negatives to the greatest possible extent. False negatives refer to a prediction favoring a retreatment when in fact it would be ineffective. The combination of methods is performed by applying an optimization problem to reduce incorrect classifications and takes into account different parameters, such as precision, recall, and statistical probabilities. The proposed system was tested in a real environment and the results obtained are promising.
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Registered trials
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