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Updated: Feb 24, 2026

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Clinical-imaging Model for Predicting Prognosis in Contemporary Endodontic Microsurgery: A Retrospective Machine

Sergio I Tobón-Arroyave1, Felipe A Restrepo-Restrepo2, Nathaly Marín-Cardona2

  • 1Laboratory of Immunodetection and Bioanalysis, Faculty of Dentistry, University of Antioquia, Medellín, Colombia.

Journal of Endodontics
|February 22, 2026
PubMed
Summary

Machine learning accurately predicts endodontic microsurgery (EMS) outcomes. Key factors for poor prognosis include poor root-end filling quality and lack of guided tissue regeneration, highlighting areas for improved surgical success.

Keywords:
Artificial intelligenceendodonticsmachine learningmicrosurgeryprognosisretrospective studies

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Area of Science:

  • Dentistry
  • Biomedical Engineering

Background:

  • Predictive models for endodontic microsurgery (EMS) outcomes are limited.
  • This study addresses the need for improved prognostic tools in EMS.

Purpose of the Study:

  • To evaluate machine learning (ML) algorithms for predicting EMS prognosis.
  • To identify key patient, tooth, and procedure-related predictors of EMS outcomes.

Main Methods:

  • Retrospective analysis of 213 teeth using ML algorithms.
  • Data preprocessing included SMOTE for class imbalance and SelectKbest for feature selection.
  • Classifiers like random forest were trained and validated; SHapley Additive exPlanations (SHAP) were used for interpretation.

Main Results:

  • The random forest classifier achieved high predictive performance (accuracy: 91%, AUC: 0.97).
  • Identified predictors of poor prognosis include lack of guided tissue regeneration, poor root-end filling quality, and lesion size.

Conclusions:

  • The random forest model shows strong predictive potential for EMS outcomes.
  • SHAP-derived predictors offer clinical insights but require external validation for definitive conclusions.