目的:在全景射线照片(PR)的解释中,牙齿的识别和编号是正确诊断的重要组成部分。本研究评估了YOLO-V5在自动检测中的有效性,分割,基于PR的混合牙列儿科患者的乳牙和恒牙编号。
方法:使用CranioCatch标记程序对3854名混合儿科患者进行乳牙和恒牙标记。数据集分为三个子集:训练(n=3093,占总数的80%),验证(n=387,占总数的10%)和测试(n=385,占总数的10%)。开发了一种使用YOLO-v5模型的人工智能(AI)算法。
结果:灵敏度,精度,F-1得分,和平均平均精度-0.5(mAP-0.5)值分别为0.99、0.99、0.99和0.98,牙齿检测。敏感性,精度,F-1得分,和mAP-0.5值分别为0.98、0.98、0.98和0.98,牙齿分割。
结论:基于YOLO-v5的模型有潜力检测并能够使用混合牙列患儿的PR准确分割乳牙和恒牙。
OBJECTIVE: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs.
METHODS: A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed.
RESULTS: The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation.
CONCLUSIONS: YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition.