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Second Opinion for Non-Surgical Root Canal Treatment Prognosis Using Machine Learning Models.

Catalina Bennasar1, Irene García2, Yolanda Gonzalez-Cid2

  • 1ADEMA, School of Dentistry, University of the Balearic Islands, 07122 Palma de Mallorca, Spain.

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Summary
This summary is machine-generated.

Machine learning models can enhance non-surgical root canal treatment (NSRCT) prognosis accuracy. These models offer a valuable second opinion, improving upon traditional clinical experience for predicting NSRCT outcomes.

Keywords:
apical periodontitismachine learningnon-surgical root canal treatmentoutcome prediction

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Area of Science:

  • Dentistry
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Non-surgical root canal treatment (NSRCT) failure prediction relies heavily on clinical experience, which can be prone to errors.
  • Current methods for predicting NSRCT outcomes are underdeveloped, necessitating more objective approaches.
  • Understanding risk factors associated with NSRCT failure is crucial for improving treatment success rates.

Purpose of the Study:

  • To investigate the potential of machine learning (ML) models as a decision support tool for predicting non-surgical root canal treatment (NSRCT) outcomes.
  • To evaluate whether ML models can serve as a reliable second opinion to aid dentists in treatment prognosis.
  • To assess the accuracy and sensitivity of ML models in predicting the success or failure of NSRCT.

Main Methods:

  • A retrospective study involving 119 cases of untreated Apical Periodontitis undergoing NSRCT by a single specialist.
  • Data collection using a novel template, defining treatment success (lesion clearance) or failure as the binary outcome.
  • Four ML algorithms—Logistic Regression (LR), Random Forest (RF), Naive-Bayes (NB), and K Nearest Neighbors (KNN)—were trained and tested using selected variables.

Main Results:

  • The Random Forest (RF) and K Nearest Neighbors (KNN) algorithms demonstrated a statistically significant improvement (p < 0.05) in the sensitivity and accuracy of treatment prognosis.
  • Analysis identified key variables associated with NSRCT outcomes, serving as inputs for the ML models.
  • The study provides a proof of concept for the utility of ML in enhancing prognostic capabilities for NSRCT.

Conclusions:

  • Machine learning models show promise in improving the accuracy and sensitivity of non-surgical root canal treatment prognosis.
  • ML models can function as a valuable second opinion tool, supporting clinical decision-making for dentists.
  • Further research through randomized clinical trials is warranted to validate the clinical utility of ML in NSRCT prognosis.