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Related Concept Videos

Tooth Anatomy01:21

Tooth Anatomy

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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.
The Crown, Neck, and Root
The visible part of the tooth is referred to as the crown. It's covered by enamel, the hardest substance in the human body. The crown is uniquely shaped for each type of tooth, allowing for different functions such as cutting, tearing, or...
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Induction of Periodontitis via a Combination of Ligature and Lipopolysaccharide Injection in a Rat Model
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Periodontitis bone loss detection in panoramic radiographs using modified YOLOv7.

Mohammed Gamal Ragab1, Said Jadid Abdulkadir1, Nadhem Qaid2

  • 1Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia.

Peerj. Computer Science
|September 24, 2025
PubMed
Summary

This study introduces YOLOv7-M, a deep learning model for automated periodontitis bone loss diagnosis from dental radiographs. It offers faster and more accurate detection than existing methods, improving patient care.

Keywords:
Automated periodontitisBone loss diagnosisFeature extractionPanoramic radiographsYOLOv7

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Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Periodontitis is a prevalent dental disease leading to tooth loss.
  • Diagnosing periodontitis-related bone loss from radiographs is challenging, requiring expertise and time.
  • Automated diagnosis systems are needed to improve efficiency and accuracy.

Purpose of the Study:

  • To develop and evaluate a deep learning model for automated diagnosis of bone loss in periodontitis.
  • To address the limitations of manual radiographic analysis.

Main Methods:

  • A modified You Only Look Once (YOLO)v2 model, termed YOLOv7-M, was developed.
  • YOLOv7-M incorporates a focus module and feature fusion for enhanced performance.
  • The model was trained and evaluated on a dental radiograph dataset.

Main Results:

  • YOLOv7-M achieved high performance metrics: F1-score (92.5%), precision (91.7%), recall (87.1%), and mAP (91.0%).
  • The model demonstrated superior accuracy and speed compared to YOLOv5 and YOLOv7.
  • YOLOv7-M outperformed other state-of-the-art object detectors in statistical evaluations.

Conclusions:

  • The proposed YOLOv7-M model shows significant potential for automated periodontitis diagnosis.
  • This technology can aid clinicians in early detection and treatment, improving patient outcomes.
  • Automated analysis of dental radiographs can enhance the management of periodontitis.