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Related Experiment Video

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Morphotype stratification of radix entomolaris in mandibular molars.

Siva Shankar Dev1, Ramya Ramadoss1, K Nitya1

  • 1Department of Oral Biology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 600077, India.

Journal of Oral Biology and Craniofacial Research
|October 27, 2025
PubMed
Summary

Radix entomolaris (RE) in molars presents endodontic challenges. Cone-beam CT and machine learning effectively classify root canal anatomy, improving treatment planning for complex cases.

Keywords:
CBCTEndodontic diagnosisFusion statusMachine learningRadix entomolarisRoot canal morphologyRoundness index

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Area of Science:

  • Endodontics
  • Dental Anatomy
  • Radiology
  • Machine Learning in Dentistry

Background:

  • Radix entomolaris (RE), a supernumerary root in mandibular molars, poses significant endodontic treatment challenges due to complex anatomy.
  • Conventional radiography often fails to detect RE, increasing the risk of missed canals and treatment failure.

Purpose of the Study:

  • To characterize the morphometric complexity of RE using cone-beam computed tomography (CBCT).
  • To develop machine learning (ML) models for classifying root canal morphotypes and predicting anatomical bifurcation in RE.

Main Methods:

  • One hundred mandibular first molars with RE underwent high-resolution CBCT scanning.
  • Morphometric parameters (canal curvature, area, roundness, volume, root fusion) were extracted and used to train ML models.
  • A decision tree classifier predicted bifurcation, and K-means clustering stratified morphotypes.

Main Results:

  • The ML model achieved high accuracy (F1-score 0.87) in predicting bifurcation.
  • Volumetric canal size was the strongest predictor of bifurcation (AUC=0.81).
  • Two morphotypes were identified: simple (round) and complex (irregular/bifurcated/C-shaped); 27% showed mid-root bifurcation.

Conclusions:

  • CBCT-derived features, especially canal volume and curvature, effectively predict RE complexity.
  • ML models integrating these features offer a framework for personalized endodontic planning.
  • AI-assisted diagnostics can improve management of complex root canal anatomy.