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Related Experiment Video

Updated: Jun 13, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Machine learning prediction of canal transportation using micro-CT data.

Ekta Chaudhari1, Arun Kumar Dagur2, Meetkumar Dedania3

  • 1Department of Conservative Dentistry and Endodontics, Siddhpur Dental College, Sabarkantha, Gujarat, India.

Bioinformation
|June 12, 2026
PubMed
Summary

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This summary is machine-generated.

Machine learning accurately predicts root canal transportation, a common endodontic complication. Using pre-operative micro-computed tomography (micro-CT) scans, this study identifies risks before instrumentation, improving treatment planning.

Area of Science:

  • Endodontics
  • Biomedical Engineering
  • Machine Learning in Dentistry

Background:

  • Root canal transportation is a significant complication in endodontic therapy.
  • Current methods lack pre-instrumentation risk prediction for canal transportation.
  • Predicting transportation is crucial for optimizing endodontic procedures.

Purpose of the Study:

  • To develop and validate machine learning models for predicting root canal transportation magnitude and direction.
  • To utilize pre-operative micro-computed tomography (micro-CT) derived morphometric features for prediction.
  • To enhance risk assessment and instrumentation strategy selection in endodontics.

Main Methods:

  • 120 mandibular molars with curved canals were scanned pre- and post-instrumentation.
Keywords:
Machine learning (ML)canal transportationendodonticsmicro-CTroot canal morphology

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Last Updated: Jun 13, 2026

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  • Seventeen morphometric variables were extracted from micro-CT scans.
  • Four machine learning algorithms were trained and evaluated using five-fold cross-validation.

    • Main Results:

    • The gradient boosting model achieved the highest accuracy in predicting apical transportation.
    • The best model demonstrated a coefficient of determination of 0.87.
    • Performance metrics included a mean absolute error of 0.031 mm and root mean square error of 0.042 mm.

    Conclusions:

    • Machine learning models utilizing pre-operative micro-CT data can accurately predict canal transportation.
    • This predictive capability can aid in endodontic risk assessment.
    • The findings support the selection of optimal instrumentation strategies to minimize transportation.