根管形态与根管治疗失败的预测分析:一项回顾性研究。

IF 1.8 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Frontiers in dental medicine Pub Date : 2025-04-24 eCollection Date: 2025-01-01 DOI:10.3389/fdmed.2025.1540038
Mohmed Isaqali Karobari, Vishnu Priya Veeraraghavan, P J Nagarathna, Sudhir Rama Varma, Jayaraj Kodangattil Narayanan, Santosh R Patil
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引用次数: 0

摘要

背景:根管治疗失败(RCT)显著影响患者预后和牙科实践。了解根管形态与RCT结果之间的关系有助于预测治疗成功。本研究旨在分析根管形态在RCT失败中的预测作用。方法:采用随机对照试验对224例患者进行回顾性研究。人口统计数据、牙齿类型和根管形态也被记录下来。进行单因素和多因素逻辑回归分析以确定RCT失败的预测因素。此外,采用机器学习算法开发预测模型,并使用受试者工作特征(ROC)曲线进行评估。结果:224项随机对照试验中,成功112项(50%),失败112项(50%)。严重的椎管弯曲(p = 0.002)是失败的重要预测因素。最终的预测模型显示ROC曲线下面积(AUC)为0.83,表明在区分成功和失败的rct方面具有良好的准确性。结论:这些发现强调了根管形态学在预测RCT结果中的重要性。机器学习方法可以增强临床决策,为RCT失败风险较高的患者提供更好的治疗计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predictive analysis of root canal morphology in relation to root canal treatment failures: a retrospective study.

Predictive analysis of root canal morphology in relation to root canal treatment failures: a retrospective study.

Predictive analysis of root canal morphology in relation to root canal treatment failures: a retrospective study.

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 (p < 0.001) and presence of accessory canals (p = 0.002) were significant predictors of failure. The final predictive model demonstrated an area under the ROC curve (AUC) of 0.83, indicating good accuracy in distinguishing between successful and failed RCTs.

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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