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Machine learning for classification of periodontal defects (vertical versus horizontal) using CBCT datasets.

Sourav Panda1, Vishwannath Hiremath2, J Sophia Jeba Priya3

  • 1Department of Periodontics, Institute of Dental Sciences, Siksha O Anusandhan University, Bhubaneswar, India.

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Summary

Machine learning accurately classifies periodontal bone defects using cone-beam computed tomography (CBCT) scans. Deep learning models show high accuracy, improving diagnostic consistency and treatment planning in periodontology.

Keywords:
Machine learningartificial intelligenceclassificationcone-beam computed tomographydeep learningperiodontal defects

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Area of Science:

  • Dentistry
  • Radiology
  • Artificial Intelligence

Background:

  • Accurate classification of periodontal bone defects is crucial for effective treatment planning.
  • Conventional radiographic methods face limitations in complex anatomical areas.

Purpose of the Study:

  • To develop and validate machine learning models for automatic classification of periodontal bone defects.
  • To assess the performance of different machine learning algorithms using CBCT data.

Main Methods:

  • Trained convolutional neural networks, random forest, support vector machines, and gradient boosting classifiers.
  • Utilized radiomic features and raw image data from 1,847 teeth in 312 patients.
  • Evaluated performance using five-fold cross-validation against expert consensus.

Main Results:

  • The convolutional neural network achieved the highest accuracy (91.4%).
  • Achieved 89.8% sensitivity for vertical defects and 92.6% specificity for horizontal defects.
  • Demonstrated an area under the ROC curve of 0.946.

Conclusions:

  • Machine learning, especially deep learning, reliably classifies periodontal defect morphology on CBCT images.
  • These models can enhance diagnostic consistency and clinical decision-making in periodontology.