基于深度学习的曲面体层片预测牙隐裂拔牙适应证。
Deep learning-based prediction of indication for cracked tooth extraction using panoramic radiography.
机构信息
Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon, 21999, Republic of Korea.
Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon, 35233, Republic of Korea.
出版信息
BMC Oral Health. 2024 Aug 16;24(1):952. doi: 10.1186/s12903-024-04721-9.
BACKGROUND
We aimed to determine the feasibility of utilizing deep learning-based predictions of the indications for cracked tooth extraction using panoramic radiography.
METHODS
Panoramic radiographs of 418 teeth (group 1: 209 normal teeth; group 2: 209 cracked teeth) were evaluated for the training and testing of a deep learning model. We evaluated the performance of the cracked diagnosis model for individual teeth using InceptionV3, ResNet50, and EfficientNetB0. The cracked tooth diagnosis model underwent fivefold cross-validation with 418 data instances divided into training, validation, and test sets at a ratio of 3:1:1.
RESULTS
To evaluate the feasibility, the sensitivity, specificity, accuracy, and F1 score of the deep learning models were calculated, with values of 90.43-94.26%, 52.63-60.77%, 72.01-75.84%, and 76.36-79.00%, respectively.
CONCLUSION
We found that the indications for cracked tooth extraction can be predicted to a certain extent through a deep learning model using panoramic radiography.
背景
本研究旨在探讨利用基于深度学习的全景片预测牙隐裂拔牙适应证的可行性。
方法
评估了 418 颗牙齿的全景片（组 1：209 颗正常牙；组 2：209 颗牙隐裂牙），以训练和测试深度学习模型。我们使用 InceptionV3、ResNet50 和 EfficientNetB0 评估了单个牙齿的牙隐裂诊断模型的性能。牙隐裂诊断模型在 418 个数据实例上进行了五重交叉验证，将数据分为训练集、验证集和测试集，比例为 3:1:1。
结果
为了评估可行性，计算了深度学习模型的灵敏度、特异性、准确性和 F1 评分，分别为 90.43%-94.26%、52.63%-60.77%、72.01%-75.84%和 76.36%-79.00%。
结论
我们发现，通过使用全景片的深度学习模型，可以在一定程度上预测牙隐裂拔牙的适应证。
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