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Abstract：
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.
CONCLUSIONS: We found that the indications for cracked tooth extraction can be predicted to a certain extent through a deep learning model 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.
CONCLUSIONS: We found that the indications for cracked tooth extraction can be predicted to a certain extent through a deep learning model using panoramic radiography.
摘要：
背景：我们的目的是确定利用基于深度学习的全景X线照相术预测裂牙拔牙适应症的可行性。
方法：评估了418颗牙齿（第1组：209颗正常牙齿；第2组：209颗裂纹牙齿）的全景X射线照片，以训练和测试深度学习模型。我们使用InceptionV3，ResNet50和EfficientNetB0评估了单个牙齿的裂纹诊断模型的性能。对裂牙诊断模型进行了五次交叉验证，将418个数据实例分为训练，验证,和测试集的比例为3:1:1。
结果：为了评估可行性，灵敏度,特异性，准确度,并计算了深度学习模型的F1得分，值为90.43-94.26%，52.63-60.77%，72.01-75.84%，和76.36-79.00%，分别。
结论：我们发现，通过使用全景射线照相术的深度学习模型，可以在一定程度上预测出现裂痕的拔牙适应症。
方法：评估了418颗牙齿（第1组：209颗正常牙齿；第2组：209颗裂纹牙齿）的全景X射线照片，以训练和测试深度学习模型。我们使用InceptionV3，ResNet50和EfficientNetB0评估了单个牙齿的裂纹诊断模型的性能。对裂牙诊断模型进行了五次交叉验证，将418个数据实例分为训练，验证,和测试集的比例为3:1:1。
结果：为了评估可行性，灵敏度,特异性，准确度,并计算了深度学习模型的F1得分，值为90.43-94.26%，52.63-60.77%，72.01-75.84%，和76.36-79.00%，分别。
结论：我们发现，通过使用全景射线照相术的深度学习模型，可以在一定程度上预测出现裂痕的拔牙适应症。
