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Preoperative risk assessment does not allow to predict root filling length using machine learning: A longitudinal

S R Herbst1, C S Herbst1, F Schwendicke1

  • 1Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, Berlin 14197, Germany.

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Summary

Identifying preoperative risk factors is crucial for successful root canal treatments (RCT). While machine learning models showed limited predictive power for optimal root filling length (RFL), understanding these factors aids treatment planning.

Keywords:
Machine learningObturationOrthograde root canal treatmentRetrospective studyRisk assessment

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Area of Science:

  • Endodontics
  • Dental Materials Science
  • Biomedical Engineering

Background:

  • Achieving optimal root filling length (RFL) is critical for the success of orthograde root canal treatments (RCT).
  • Preoperative risk factors can influence the ability to achieve RFL, impacting treatment outcomes.
  • The application of machine learning (ML) in predicting RCT outcomes is an emerging area of research.

Purpose of the Study:

  • To identify significant associations between preoperative risk factors and optimal RFL in RCT.
  • To evaluate the predictive capability of various machine learning algorithms for successful RFL.

Main Methods:

  • A retrospective analysis of 555 completed RCTs from a university clinic (2016-2020) was conducted.
  • Logistic regression (logR) was used for association analyses, and logR along with Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGB) were employed for predictive modeling.
  • Successful RFL was defined as 0-2mm from the apex; suboptimal RFL was >2mm or beyond the apex.

Main Results:

  • Unsuccessful RFL was significantly associated with undergraduate student operators, indistinct canal paths, reduced canal size, and retreatment cases (p < 0.01).
  • Dentists demonstrated higher success rates in mitigating risks compared to undergraduate students.
  • Machine learning models exhibited insufficient predictive performance for RFL on a separate test set.

Conclusions:

  • Operator experience and specific preoperative risk factors significantly influence the achievement of optimal RFL in RCT.
  • Current machine learning algorithms demonstrate limited efficacy in predicting the technical outcome of RFL in endodontic treatments.
  • Preoperative risk assessment, particularly focusing on single radiographic factors, offers valuable insights for endodontic treatment planning.