PURPOSE:To deeply integrate alveolar surgery with artificial intelligence and change the existing diagnosis and treatment mode to guide clinical work better,especially to provide help for primary oral surgeons.METHODS:CBCT data of 214 patients with impacted mandibular third molar admitted to West China Stomatology Hospital of Sichuan Uni-versity from September 2022 to December 2022 were randomly selected and divided into training dataset and test dataset.According to the clinical experience of human experts,six-type classification of resistance sources were proposed,and patients'CBCT data were artificially classified.Then,the classification features were deeply learned and tested through the artificial intelligence oral surgeon(AIOS)model.Finally,the confusion matrix graph and Accuracy-Loss-Epoch curve were used to analyze the learning process and results.RESULTS:All model training datasets achieved 99.07％-100％ac-curacy.In the test dataset,the accuracy of all models reach more than 80％,and some models can reach 100％accuracy.CONCLUSIONS:AIOS has shown promising prospects for predicting the source of resistance of impacted mandibular third molar on CBCT images and assisting clinical oral surgeons in oral image analysis,laying a good foundation for the development of a full set of AIOS with resistance analysis,protocol formulation,and simulated surgery that can be applied in the clinic of oral surgery in the future.
关键词
机器学习/CBCT/卷积神经网络/图像分类/口腔外科
Key words
Machine learning/CBCT/Convolutional neural network/Image classification/Oral surgery
维普
万方数据