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Related Experiment Video

Updated: Oct 7, 2025

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Machine learning models for prognosis prediction in endodontic microsurgery.

Yang Qu1, Zhenzhe Lin2, Zhaojing Yang2

  • 1Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China; Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China.

Journal of Dentistry
|January 12, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning models, including gradient boosting machine (GBM), can predict endodontic microsurgery prognosis. The GBM model showed strong predictive accuracy, aiding clinical decisions and reducing treatment failure.

Keywords:
Endodontic microsurgeryMachine learningPrognosis prediction

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Area of Science:

  • Endodontics
  • Machine Learning
  • Prognosis Prediction

Background:

  • Endodontic microsurgery outcomes can be challenging to predict.
  • Accurate prognosis prediction is crucial for successful treatment and clinical decision-making.

Purpose of the Study:

  • To establish and validate machine learning models for predicting endodontic microsurgery prognosis.
  • To identify key predictors of treatment success or failure.

Main Methods:

  • Developed and tested Gradient Boosting Machine (GBM) and Random Forest (RF) models.
  • Utilized a dataset of 234 teeth from 178 patients with 5-fold cross-validation.
  • Evaluated model performance using accuracy, sensitivity, specificity, PPV, NPV, F1 score, and AUC.

Main Results:

  • Identified eight key predictors: tooth type, lesion size, bone defect type, root filling density/length, post apical extension, age, and sex.
  • The GBM model achieved 0.80 accuracy, 0.92 sensitivity, 0.71 specificity, and 0.88 AUC.
  • The RF model achieved 0.80 accuracy, 0.85 sensitivity, 0.76 specificity, and 0.83 AUC.

Conclusions:

  • Machine learning models, particularly GBM, can effectively predict endodontic microsurgery prognosis.
  • These models, based on common variables, can support clinical decision-making and improve patient communication.