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Segmentation of Pulp and Pulp Stones with Automatic Deep Learning in Panoramic Radiographs: An Artificial

Mujgan Firincioglulari1, Mehmet Boztuna2, Omid Mirzaei3

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Artificial intelligence (AI) algorithms can accurately detect pulp stones and pulp calcifications on dental radiographs. This AI tool shows promise in assisting dentists with radiographic diagnoses.

Keywords:
artificial intelligencedeep learningpanoramic radiographpulp stone

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Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Pulp stones are calcified masses in dental pulp that can complicate dental procedures.
  • Accurate detection of pulp stones on radiographs is crucial for dental treatment planning.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of artificial intelligence (AI) algorithms for identifying pulp and pulp stones on panoramic radiographs.
  • To assess the potential of AI as an adjunct tool for radiographic diagnosis in dentistry.

Main Methods:

  • Utilized 713 panoramic radiographs with identified pulp stones.
  • Employed CVAT v1.7.0 software for manual annotation of 4675 pulp stones and 5085 pulps.
  • Developed and tested AI models for pulp and pulp stone segmentation.

Main Results:

  • The AI model achieved high performance metrics for pulp segmentation (Dice: 0.84, IoU: 0.758) and pulp stone segmentation (Dice: 0.759, IoU: 0.686).
  • Precision and recall values for pulp segmentation were 0.858 and 0.827, respectively.
  • Precision and recall values for pulp stone segmentation were 0.792 and 0.773, respectively.

Conclusions:

  • AI algorithms can successfully identify pulp and pulp stones in radiographic images.
  • AI software demonstrates potential as a valuable tool to support clinicians in radiographic diagnosis.
  • Further research with larger datasets is recommended to improve AI model capabilities.