Deep Learning-Assisted Diagnostic System: Implant Brand Detection Using Improved IB-YOLOv10 in Periapical Radiographs.

Journal: Diagnostics (Basel, Switzerland)
Published Date: May 8, 2025

Abstract

: Implant brand identification is critical in modern dental clinical diagnostics. With the increasing variety of implant brands and the difficulty of accurate identification in periapical radiographs, there is a growing demand for automated solutions. This study aims to leverage deep learning techniques to assist in dental implant classification, providing dentists with an efficient and reliable tool for implant brand detection. : We proposed an innovative implant brand feature extraction method with multiple image enhancement techniques to improve implant visibility and classification accuracy. Additionally, we introduced a PA resolution enhancement technique that utilizes Dark Channel Prior and Lanczos interpolation for image resolution upscaling. : We evaluated the performance differences among various YOLO models for implant brand detection. Additionally, we analyzed the impact of implant brand feature extraction and PA resolution enhancement techniques on YOLO's detection accuracy. Our results show that IB-YOLOv10 achieves a 17.8% accuracy improvement when incorporating these enhancement techniques compared to IB-YOLOv10 without enhancements. In real-world clinical applications, IB-YOLOv10 can classify implant brands in just 6.47 ms per PA, significantly reducing diagnostic time. Compared to existing studies, our model improves implant detection accuracy by 2.3%, achieving an overall classification accuracy of 94.5%. : The findings of this study demonstrate that IB-YOLOv10 effectively reduces the diagnostic burden on dentists while providing a fast and reliable implant brand detection solution, improves clinical efficiency, and establishes a robust deep learning approach for automated implant detection in PA.

Authors

  • Yuan-Jin Lin
    Department of Program on Semiconductor Manufacturing Technology (PSMT), Academy of Innovative Semiconductor and Sustainable Manufacturing (AISSM), National Cheng Kung University, Tainan City 701401, Taiwan.
  • Shih-Lun Chen
    Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan 320314, Taiwan.
  • Ya-Cheng Lu
    Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320317, Taiwan.
  • Xu-Ming Lin
    Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320317, Taiwan.
  • Yi-Cheng Mao
    Department of General Dentistry, Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan.
  • Ming-Yi Chen
    Department of Family Dentistry, Taoyuan Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan.
  • Chao-Shun Yang
    Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan.
  • Tsung-Yi Chen
    Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan.
  • Kuo-Chen Li
    Department of Information Management, Chung Yuan Christian University, Taoyuan City 320317, Taiwan.
  • Wei-Chen Tu
    Department of Electrical Engineering, National Cheng Kung University, Tainan City 701401, Taiwan.
  • Patricia Angela R Abu
    Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon 1108, Philippines.
  • Chiung-An Chen
    Department of Electrical Engineering, Ming Chi University of Technology, New Taipei 243303, Taiwan.

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