ArticleFrontiers in dental medicine2025
Predictive analysis of root canal morphology in relation to root canal treatment failures: a retrospective study.
Article in Frontiers in dental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Evaluation of the Effect of Coronal Flaring on SWEEPS Laser-Activated Irrigation in Root Canals with Different Curvatures: A Confocal Laser Scanning Microscopy Study.Medicina (Kaunas, Lithuania) · 2026Article
- Comparison between dual-angle periapical technique and cone-beam computed tomography in the identification of a second canal in mandibular incisors in the presence of a zirconia implant: An in vitro study.Imaging science in dentistry · 2026Article
- Article
- Single-Visit Endodontics Under Dental Operating Microscopy: A Retrospective Observational Study.Cureus · 2026Article
- 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
- Effect of simulated root canal morphology and post space depth on fracture performance of glass fiber-reinforced post-core buildups.BMC oral health · 2025Article
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Authors and funding
6 authors.
Funding
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Abstract
Background: Failure of root canal treatment (RCT) significantly affects patient outcomes and dental practice. Understanding the association between root canal morphology and RCT outcomes can help predict treatment success. This study aimed to analyze the predictive role of root canal morphology in RCT failure. Methods: This retrospective study included 224 patients who underwent RCT. Demographic data, tooth type, and root canal morphology were also recorded. Univariate and multivariate logistic regression analyses were performed to identify predictors of RCT failure. Additionally, machine learning algorithms were employed to develop a predictive model that was evaluated using receiver operating characteristic (ROC) curves. Results: Of the 224 RCTs, 112 (50%) were classified as successful and 112 (50%) as failure. Severe canal curvature ( Conclusion: These findings underscore the importance of root canal morphology in predicting RCT outcomes. Machine learning approaches can enhance clinical decision making, enabling better treatment planning for patients at a higher risk of RCT failure.
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