Detection of Dental Restorations in Digital Panoramic Radiographs Using YOLOv11

Year 2026, Volume: 53 Issue: 1, 35 - 42, 25.03.2026

Fulya Aydın , Şükran Ayran , Muzaffer Kaan Yüce

https://doi.org/10.52037/eads.2026.0007

https://izlik.org/JA49XY23BN

Abstract

Abstract
Purpose
The aim of this study was to evaluate the performance of the YOLOv11 deep learning–based object detection model in detecting dental restorations on digital panoramic radiographs.
Materials and Methods
A total of 320 panoramic radiographs obtained from adult patients were included in this study. Four types of dental restorations—amalgam, composite, crown, and bridge—were manually annotated and used for model training and evaluation. The dataset was divided into training and testing subsets at a ratio of 75% and 25%, respectively. A validation subset was derived from the training data. Model performance was assessed using precision, recall, F1-score, and mean Average Precision (mAP) metrics.
Results
The YOLOv11 model achieved an overall precision of 0.663, recall of 0.665, and F1-score of 0.663 across all restoration categories. The mAP@50 value was 0.671, while the mAP@50–95 value was 0.432, indicating variations in detection performance across different Intersection over Union thresholds. Differences in detection performance were observed among the various types of dental restorations.
Conclusion
The findings indicate that YOLOv11 demonstrates measurable potential as an automated support tool for detecting dental restorations on panoramic radiographs; however, the achieved performance remains lower than that reported in several previous studies. Successful clinical integration of such AI-assisted systems requires further optimization and validation using larger and more heterogeneous datasets to enhance generalizability and clinical applicability.
Keywords: deep learning; dental restorations; panoramic radiography; YOLOv11

Keywords

deep learning , dental restorations , panoramic radiography , YOLOv11

Ethical Statement

The study protocol received approval from the Institutional Review Board of the Gülhane Scientific Research Ethics Committee at the University of Health Sciences (Approval No: 11.03.2025/148).

References

• Wang CW, Huang CT, Lee JH, Li CH, Chang SW, Siao MJ, et al. A benchmark for comparison of dental radiography analysis algorithms. Med Image Anal. 2016;31:63–76. doi:10.1016/j.media.2016.02.004.
• Ahmed N, Abbasi MS, Zuberi F, Qamar W, Halim MSB, Maqsood A, et al. Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry-A Systematic Review. Biomed Res Int. 2021;2021:9751564. doi:10.1155/2021/9751564.
• Issa J, Olszewski R, Dyszkiewicz-Konwińska M. The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review. Int J Environ Res Public Health. 2022;19(1). doi:10.3390/ijerph19010560.
• Mohammad N, Ahmad R, Kurniawan A, Mohd Yusof MYP. Applications of contemporary artificial intelligence technology in forensic odontology as primary forensic identifier: A scoping review. Front Artif Intell. 2022;5:1049584. doi:10.3389/frai.2022.1049584.
• Orhan K, Shamshiev M, Ezhov M, Plaksin A, Kurbanova A, Ünsal G, et al. AI-based automatic segmentation of craniomaxillofacial anatomy from CBCT scans for automatic detection of pharyngeal airway evaluations in OSA patients. Sci Rep. 2022;12(1):11863. doi:10.1038/s41598-022-15920-1.
• Schwendicke F, Chaurasia A, Wiegand T, Uribe SE, Fontana M, Akota I, et al. Artificial intelligence for oral and dental healthcare: Core education curriculum. J Dent. 2023;128:104363. doi:10.1016/j.jdent.2022.104363.
• Ariji Y, Gotoh M, Fukuda M, Watanabe S, Nagao T, Katsumata A, et al. A preliminary deep learning study on automatic segmentation of contrast-enhanced bolus in videofluorography of swallowing. Sci Rep. 2022;12(1):18754. doi:10.1038/s41598-022-21530-8.
• Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 779–788.
• Saber S, Abou El Nasr H, Torky AA, Saif N. Automated assessment of periapical health based on the radiographic periapical index using YOLOv8, YOLOv11, and YOLOv12 one-stage object detection algorithms. Scientific Reports. 2025;15(1):36487. doi:10.1038/s41598-025-21761-5.
• Şevik U, Mutlu O. Detection of Dental Anomalies in Digital Panoramic Images Using YOLO: A Next Generation Approach Based on Single Stage Detection Models. Diagnostics. 2025;15(15):1961. doi:10.3390/diagnostics15151961.
• Ayhan M, Kayadibi I, Aykanat B. RCFLA-YOLO: A deep learning-driven framework for the automated assessment of root canal filling quality in periapical radiographs. BMC Medical Education. 2025;25(1):894. doi:10.1186/s12909-025-07483-2.
• Khanam R, Hussain M. Yolov11: An overview of the key architectural enhancements. arXiv. 2024. doi:10.48550/arXiv.2410.17725.
• LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015;521(7553):436–444. doi:10.1038/nature14539.
• Zhiqiang W, Jun L. A review of object detection based on convolutional neural network. In: 2017 36th Chinese control conference (CCC). IEEE; 2017. p. 11104–11109.
• Hossin M, Sulaiman MN. A review on evaluation metrics for data classification evaluations. International journal of data mining & knowledge management process. 2015;5(2):1. doi:10.5121/ijdkp.2015.5201.
• Chen T, Kim S, Zhou J, Metaxas D, Rajagopal G, Yue N. 3D meshless prostate segmentation and registration in image guided radiotherapy. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2009. p. 43–50.
• Yazdanian M, Karami S, Tahmasebi E, Alam M, Abbasi K, Rahbar M, et al. Dental radiographic/digital radiography technology along with biological agents in human identification. Scanning. 2022;2022(1):5265912. doi:10.1155/2022/5265912.
• Terlemez A, Tassoker M, Kizilcakaya M, Gulec M. Comparison of cone-beam computed tomography and panoramic radiography in the evaluation of maxillary sinus pathology related to maxillary posterior teeth: Do apical lesions increase the risk of maxillary sinus pathology? Imaging Sci Dent. 2019;49(2):115. doi:10.5624/isd.2019.49.2.115.
• McDavid W, Langlais R, Welander U, Morris C. Real, double, and ghost images in rotational panoramic radiography. Dentomaxillofacial Radiology. 1983;12(2):122–128. doi:10.1259/dmfr.1983.0020.
• Casalegno F, Newton T, Daher R, Abdelaziz M, Lodi-Rizzini A, Schürmann F, et al. Caries detection with near-infrared transillumination using deep learning. J Dent Res. 2019;98(11):1227–1233. doi:10.1177/0022034519871884.
• Sivari E, Senirkentli GB, Bostanci E, Guzel MS, Acici K, Asuroglu T. Deep learning in diagnosis of dental anomalies and diseases: A systematic review. Diagnostics. 2023;13(15):2512. doi:10.3390/diagnostics13152512.
• Kaya E, Gunec HG, Aydin KC, Urkmez ES, Duranay R, Ates HF. A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs. Imaging science in dentistry. 2022;52(3):275. doi:10.5624/isd.20220050.
• Kılıc MC, Bayrakdar IS, Çelik O, Bilgir E, Orhan K, Aydın OB, et al. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofac Radiol. 2021;50(6):20200172. doi:10.1259/dmfr.20200172.
• Bumann EE, Al-Qarni S, Chandrashekar G, Sabzian R, Bohaty B, Lee Y. A novel collaborative learning model for mixed dentition and fillings segmentation in panoramic radiographs. Journal of dentistry. 2024;140:104779. doi:10.1016/j.jdent.2023.104779.
• Vinayahalingam S, Goey Rs, Kempers S, Schoep J, Cherici T, Moin DA, et al. Automated chart filing on panoramic radiographs using deep learning. J Dent. 2021;115:103864. doi:10.1016/j.jdent.2021.103864.
• Aliaga I, Vera V, Vera M, García E, Pedrera M, Pajares G. Automatic computation of mandibular indices in dental panoramic radiographs for early osteoporosis detection. Artificial intelligence in medicine. 2020;103:101816. doi:10.1016/j.artmed.2020.101816.
• Akdoğan S, Öziç MU, Tassoker M. Development of an AI-supported clinical tool for assessing mandibular third molar tooth extraction difficulty using panoramic radiographs and YOLO11 sub-models. Diagnostics. 2025;15(4):462. doi:10.3390/diagnostics15040462.
• He Z, Wang K, Fang T, Su L, Chen R, Fei X. Comprehensive performance evaluation of YOLOv11, YOLOv10, YOLOv9, YOLOv8 and YOLOv5 on object detection of power equipment. In: 2025 37th Chinese Control and Decision Conference (CCDC). IEEE; 2025. p. 1281–1286.
• Vempati RT. Real-time detection and recognition of license plate using YOLO11 object detection model [Thesis]. Kansas State University; 2024.
• Abdalla-Aslan R, Yeshua T, Kabla D, Leichter I, Nadler C. An artificial intelligence system using machine-learning for automatic detection and classification of dental restorations in panoramic radiography. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130(5):593–602. doi:10.1016/j.oooo.2020.05.012.