Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Kutahya Health Science University, Kutahya, Turkiye.
Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Suleyman Demirel University, Isparta, Turkiye.
J Coll Physicians Surg Pak. 2024 Aug;34(8):922-926. doi: 10.29271/jcpsp.2024.08.922.
To investigate the effectiveness of using YOLO-v5x in detecting fixed prosthetic restoration in panoramic radiographs.
Descriptive study. Place and Duration of the Study: Department of Oral and Maxillofacial Radiology, Eskisehir Osmangazi University, Eskisehir, Turkiye from November 2022 to April 2023.
For 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.
The 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.
The 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.
Artificial intelligence, Dentistry, Dental prosthesis, Panoramic radiography.
研究 YOLO-v5x 在全景放射片中检测固定修复体的效果。
描述性研究。地点和时间：土耳其埃斯基谢希尔奥斯曼加齐大学口腔颌面放射科，2022 年 11 月至 2023 年 4 月。
为了对固定修复体进行标注，使用 YOLO-v5x 架构对 8000 张全景片进行评估。在为这项研究创建数据集时，将固定修复体在口腔全景片上分为种植牙、桥体、牙冠和种植牙支持的牙冠。然后，将标注的图像随机分为三组：80%用于训练，10%用于验证，10%用于测试。标注的全景图像构成了模型的训练数据集，利用在这个学习阶段获得的知识，模型在测试阶段生成预测结果。
大多数标注数据都是针对牙冠修复体的。YOLOv5x 的精确度和敏感度值分别为：牙冠 0.99 和 0.98，种植牙 0.98 和 0.99，桥体 0.99 和 0.99，种植牙支持的牙冠 0.99 和 0.99。
这项研究的结果表明，YOLO-v5x 在检测牙科修复体方面具有较高的成功率。该模型的高精度和高敏感度表明，它具有增强临床专业表现和促进更高效的口腔健康服务发展的巨大潜力。
人工智能、牙科学、牙修复体、全景放射摄影术。
