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Integrating Machine Learning and Deep Learning for Predicting Non-Surgical Root Canal Treatment Outcomes Using

Catalina Bennasar1, Antonio Nadal-Martínez2, Sebastiana Arroyo1

  • 1Academia Dental de Mallorca (ADEMA), School of Dentistry, University of Balearic Islands, 07122 Palma de Mallorca, Spain.

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Summary
This summary is machine-generated.

Deep learning models using 2D radiographs significantly improve non-surgical root canal treatment (NSRCT) outcome prediction compared to traditional machine learning and clinician prognosis. Image-based AI offers superior accuracy for apical periodontitis treatment success.

Keywords:
apical periodontitisdeep learningmachine learningnon-surgical root canal treatmentoutcome prediction

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Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Radiology

Background:

  • Previous studies predicted non-surgical root canal treatment (NSRCT) success using categorical data and machine learning (ML).
  • Radiographic imaging is crucial for diagnosing apical periodontitis (AP) and assessing treatment outcomes.
  • This study explores deep learning (DL) for predicting NSRCT outcomes from 2D periapical radiographs.

Purpose of the Study:

  • To evaluate the efficacy of DL in predicting NSRCT outcomes using 2D radiographs.
  • To compare the performance of DL models against traditional ML models.
  • To assess if image-based AI prediction surpasses clinician prognosis.

Main Methods:

  • A DL model was trained and validated using leave-one-out cross-validation (LOOCV).
  • DL model output was integrated with categorical variables for ML analysis, reproduced using backward stepwise selection (BSS).
  • Statistical comparisons (chi-square, Fisher's exact test) were performed between DL, ML, and clinician prognosis.

Main Results:

  • 2D radiographs demonstrated significant predictive capability (p<0.000000127).
  • The best-performing ML model shifted from random forest (RF) to logistic regression (LR) when DL variables were included.
  • The DL-LR model and DL showed statistically significant superior performance in sensitivity, NPV, and accuracy compared to clinician prognosis (p<0.05).

Conclusions:

  • Image-based artificial intelligence models demonstrate superior predictive capability for NSRCT outcomes over models using only categorical data.
  • Deep learning models utilizing 2D radiographs outperform clinician prognosis in predicting treatment success.
  • AI-driven radiographic analysis represents a significant advancement in predicting endodontic treatment outcomes.