• Melike Yurttas, Ozlem Yarbasi, Ridvan Karakurt, Halil Ayyildiz, Elif Bilgir, and Ibrahim Sevki Bayrakdar.
• Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Kutahya Health Science University, Kutahya, Turkiye.
• J Coll Physicians Surg Pak. 2024 Aug 1; 34 (8): 922926922-926.

ObjectiveTo investigate the effectiveness of using YOLO-v5x in detecting fixed prosthetic restoration in panoramic radiographs.Study DesignDescriptive study. Place and Duration of the Study: Department of Oral and Maxillofacial Radiology, Eskisehir Osmangazi University, Eskisehir, Turkiye from November 2022 to April 2023.MethodologyFor the labelling of fixed prosthetic restorations, 8,000 panoramic radiographs were evaluated using the YOLO-v5x architecture. In creating the dataset for this study, fixed prosthetic restorations were categorised as dental implant, pontic, crown, and implant-supported crown on dental panoramic radiographs. The labelled images were then randomly split into three groups: 80% for training, 10% for validation, and 10% for testing. The labelled panoramic images constituted the model's training dataset, and leveraging the knowledge acquired during this learning stage, the model generated predictions in the testing phase.ResultsThe majority of labelling data were dedicated to crown restorations. The precision and sensitivity values of YOLOv5x were 0.99 and 0.98 for crowns, 0.98 and 0.99 for implants, 0.99 and 0.99 for pontics, and 0.99 and 0.99 for implant-supported crowns, respectively.ConclusionThe results obtained in this study demonstrate a satisfactory success rate of YOLO-v5x in detecting dental prosthetic restorations. The high precision and sensitivity of the model indicate its strong potential to enhance clinical professional performance and contribute to the development of more efficient dental health services.Key WordsArtificial intelligence, Dentistry, Dental prosthesis, Panoramic radiography.

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