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Diagnostics
Volume 15
Issue 20
10.3390/diagnostics15202598
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Automated Implant Placement Pathway from Dental Panoramic Radiographs Using Deep Learning for Preliminary Clinical Assistance

by
Pei-Yi Wu
1,
Shih-Lun Chen
2,*,
Yi-Cheng Mao
3,
Yuan-Jin Lin
4,
Pin-Yu Lu
5,
Kai-Hsun Yu
5,
Kuo-Chen Li
5,
Tsun-Kuang Chi
6,*,
Tsung-Yi Chen
7 and
Patricia Angela R. Abu
8
1
Department of Periodontics, Division of Dentistry, Taoyuan Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan
2
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320317, Taiwan
3
Department of Operative Dentistry, Taoyuan Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan
4
Program on Semiconductor Manufacturing Technology, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan City 701401, Taiwan
5
Department of Information Management, Chung Yuan Christian University, Taoyuan City 320317, Taiwan
6
Department of Electrical Engineering, Ming Chi University of Technology, 84 Gungjuan Rd., New Taipei City 243303, Taiwan
7
Department of Electronic Engineering, Feng Chia University, Taichung City 40724, Taiwan
8
Ateneo Laboratory for Intelligent Visual Environments, Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, Philippines
*
Authors to whom correspondence should be addressed.
Diagnostics 2025, 15(20), 2598; https://doi.org/10.3390/diagnostics15202598
Submission received: 3 September 2025 / Revised: 7 October 2025 / Accepted: 14 October 2025 / Published: 15 October 2025
(This article belongs to the Special Issue 3rd Edition: AI/ML-Based Medical Image Processing and Analysis)
Versions Notes

Abstract

Background/Objective: Dental implant therapy requires clinicians to identify edentulous regions and adjacent teeth accurately to ensure precise and efficient implant placement. However, this process is time-consuming and subject to operator bias. To address this challenge, this study proposes an AI-assisted detection framework that integrates deep learning and image processing techniques to predict implant placement pathways on dental panoramic radiographs, supporting clinical decision-making. Methods: The proposed framework is first applied to YOLO models to detect edentulous regions and employs image enhancement techniques to improve image quality. Subsequently, YOLO-OBB is utilized to extract pixel-level positional information about neighboring healthy teeth. An implant pathway orientation visualization algorithm is applied to derive clinically relevant implant placement recommendations. Results: Experimental evaluation using YOLOv9m and YOLOv8n-OBB demonstrated stable performance in both recognition and accuracy. The models achieved Precision values of 88.86% and 89.82%, respectively, with an average angular error of only 1.537° compared to clinical implant pathways annotated by dentists. Conclusions: This study presents the first AI-assisted diagnostic framework for DPR-based implant pathway prediction. The results indicate strong consistency with clinical planning, confirming its potential to enhance diagnostic accuracy and provide reliable decision support in implant dentistry.
Keywords: AI-assisted diagnostic; image enhancement; implant placement pathway; You only Look Once AI-assisted diagnostic; image enhancement; implant placement pathway; You only Look Once

Share and Cite

MDPI and ACS Style

Wu, P.-Y.; Chen, S.-L.; Mao, Y.-C.; Lin, Y.-J.; Lu, P.-Y.; Yu, K.-H.; Li, K.-C.; Chi, T.-K.; Chen, T.-Y.; Abu, P.A.R. Automated Implant Placement Pathway from Dental Panoramic Radiographs Using Deep Learning for Preliminary Clinical Assistance. Diagnostics 2025, 15, 2598. https://doi.org/10.3390/diagnostics15202598

AMA Style

Wu P-Y, Chen S-L, Mao Y-C, Lin Y-J, Lu P-Y, Yu K-H, Li K-C, Chi T-K, Chen T-Y, Abu PAR. Automated Implant Placement Pathway from Dental Panoramic Radiographs Using Deep Learning for Preliminary Clinical Assistance. Diagnostics. 2025; 15(20):2598. https://doi.org/10.3390/diagnostics15202598

Chicago/Turabian Style

Wu, Pei-Yi, Shih-Lun Chen, Yi-Cheng Mao, Yuan-Jin Lin, Pin-Yu Lu, Kai-Hsun Yu, Kuo-Chen Li, Tsun-Kuang Chi, Tsung-Yi Chen, and Patricia Angela R. Abu. 2025. "Automated Implant Placement Pathway from Dental Panoramic Radiographs Using Deep Learning for Preliminary Clinical Assistance" Diagnostics 15, no. 20: 2598. https://doi.org/10.3390/diagnostics15202598

APA Style

Wu, P.-Y., Chen, S.-L., Mao, Y.-C., Lin, Y.-J., Lu, P.-Y., Yu, K.-H., Li, K.-C., Chi, T.-K., Chen, T.-Y., & Abu, P. A. R. (2025). Automated Implant Placement Pathway from Dental Panoramic Radiographs Using Deep Learning for Preliminary Clinical Assistance. Diagnostics, 15(20), 2598. https://doi.org/10.3390/diagnostics15202598

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