Automated assessment of peri-implant disease severity by deep learning and image processing in periapical radiographs.

Authors

Yi-Cheng Mao, Chiung-An Chen, Yuan-Jin Lin, Yu-Jen Chang, Sung-Tsun Wei, Shih-Lun Chen, Tsung-Yi Chen, Kuo-Chen Li, Wei-Chen Tu, Patricia Angela R Abu

Published in

Journal of dental sciences. Volume 21. Issue 2. Pages 963-972. Epub Apr 01, 2026.

Abstract

Dental implant surgery had become a standard treatment option for oral rehabilitation. The severity of peri-implant bone loss was a critical clinical indicator for evaluating the success of dental implants. This study assessed an automated framework combining deep learning and image processing techniques for classifying the severity of peri-implant bone loss using periapical radiographs and providing diagnostic assistance.
A total of 780 periapical radiographs containing 1210 implants were analyzed. A YOLO-based object detection model was employed to localize peri-implant regions accurately. Subsequently, we applied our custom-developed peri-implant image processing pipeline and alveolar crest localization algorithm to categorize each implant into one of three severity levels. The clinical feasibility of the proposed framework was also evaluated.
On a test dataset of 120 periapical radiographs, the YOLOv8-S model achieved a detection precision of 98.1 %, with a sensitivity of 96.0 % and a specificity of 99.1 %. For the three-grade classification of peri-implant bone loss severity, the highest accuracy reached 96.61 %, with an overall classification accuracy of 95.8 %. In clinical feasibility testing, our framework demonstrated a 36-fold improvement in assessment speed and approximately a 7.5 % increase in diagnostic accuracy compared to manual evaluation by dental experts.
The proposed automated framework for assessing peri-implant bone loss severity in periapical radiographs shows strong potential to assist clinical dental practices. It is a reliable second opinion to support clinical decision-making and improve diagnostic efficiency.

PMID:
42689103
Bibliographic data and abstract were imported from PubMed on 03 Sep 2026.

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