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Related Concept Videos

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Tooth Anatomy

The human tooth enables us to eat a variety of foods, speak clearly, and even aid in shaping our faces. Teeth are composed of various elements that work together. Here's a detailed look at the anatomy of a human tooth.
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YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Gianmarco Scarano1, Simone Agostinelli2, Irene Amerini1

  • 1ALCOR Lab, Department of Computer, Control and Management Engineering, Faculty of Information Engineering, Informatics and Statistics, Sapienza University of Rome, 00185 Rome, Italy.

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|June 25, 2026
PubMed
Summary

This study introduces a deep learning framework for automatically detecting periapical lesions in dental radiographs. The system accurately segments teeth and identifies lesions, improving diagnostic efficiency.

Keywords:
computer visiondeep learningmedical image segmentationperiapical lesion detectionteeth segmentation

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Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Chronic periapical periodontitis involves progressive bone destruction around tooth apexes.
  • Manual radiographic detection of these lesions is subjective and time-consuming.
  • Automated diagnostic tools are needed for efficient screening.

Purpose of the Study:

  • To present a unified deep learning framework for joint tooth segmentation and periapical lesion detection.
  • To develop an efficient and practical tool for periapical lesion screening in panoramic radiographs.

Main Methods:

  • A deep learning model for tooth segmentation and a second model for periapical lesion detection were employed.
  • An advanced loss function (Powerful IoU v2) and a spatial association mechanism were utilized.
  • The framework was trained on open-source datasets.

Main Results:

  • Tooth segmentation achieved an mAP@50 of 97.7% and a mean Dice coefficient of 93.5%.
  • Periapical lesion detection reached an mAP@50 of 91.9%.
  • A 3.49× computational speedup was observed with the region-of-interest approach.

Conclusions:

  • The proposed framework provides accurate tooth segmentation and periapical lesion detection.
  • The system offers an efficient and reproducible tool for periapical lesion screening.
  • Explicit tooth-to-lesion mapping enhances diagnostic utility.