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Peri-Implant Bone Loss Measurement Using a Region-Based Convolutional Neural Network on Dental Periapical

Jun-Young Cha1, Hyung-In Yoon1, In-Sung Yeo1

  • 1Department of Prosthodontics, School of Dentistry and Dental Research Institute, Seoul National University, Daehak-ro 101, Jongro-gu, Seoul 03080, Korea.

Journal of Clinical Medicine
|April 3, 2021
PubMed
Summary

A deep convolutional neural network (CNN) accurately detects peri-implant bone levels on dental radiographs, aiding in assessing peri-implantitis severity. This AI tool matches clinician performance in identifying key landmarks for bone loss measurement.

Keywords:
artificial intelligenceconvolutional neural networkdeep learningkeypoint detectionmachine learningperi-implant bone levelperi-implantitisradiographs

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Area of Science:

  • Artificial Intelligence in Dentistry
  • Radiographic Image Analysis
  • Periodontology

Background:

  • Assessing peri-implant marginal bone level on radiographs is difficult due to unclear bone boundaries and differing buccal/lingual bone heights.
  • Accurate measurement of bone loss is crucial for diagnosing and managing peri-implantitis.

Purpose of the Study:

  • To evaluate a deep convolutional neural network (CNN) for detecting implant landmarks and marginal bone levels on dental periapical radiographs.
  • To develop an automated system for calculating bone loss percentage and classifying resorption severity.
  • To compare the AI model's performance against a dental clinician.

Main Methods:

  • A modified region-based CNN (R-CNN) was trained using transfer learning on 708 periapical radiographs.
  • Data augmentation was employed to enrich the training dataset.
  • Performance was evaluated using average precision, average recall, and mean object keypoint similarity (OKS), comparing the model to a dental clinician.

Main Results:

  • The modified R-CNN model demonstrated no statistically significant difference compared to a dental clinician in detecting landmarks around dental implants.
  • The AI system successfully measured radiographic bone loss and classified resorption severity using detected keypoints.
  • High accuracy in landmark detection suggests reliability for clinical application.

Conclusions:

  • The modified R-CNN model is a viable tool for accurately measuring radiographic peri-implant bone loss.
  • This automated system can assist in assessing the severity of peri-implantitis.
  • AI-powered analysis offers a reliable alternative for quantitative evaluation of bone levels around dental implants.