基于CBCT图像分析下颌第三磨牙阻力来源的人工智能口腔外科医师  

Artificial intelligence oral surgeon analyzing the source of resistance of mandibular third molars based on CBCT images

作　　者：何禹龙 包崇云[1] HE Yu-long;BAO Chong-yun(Department of Oral and Maxillofacial Surgery,West China Stomatology Hospital,Sichuan University,Chengdu 610041,Sichuan Province,China)

机构地区：[1]四川大学华西口腔医院口腔颌面外科,四川成都610041

出　　处：《中国口腔颌面外科杂志》2024年第4期339-345,共7页China Journal of Oral and Maxillofacial Surgery

基　　金：国家重点研发计划(2020YFC2009005);国家自然科学基金(82071166)。

摘　　要：目的:将牙槽外科与人工智能深度融合,改变现有的诊疗模式,从而更好指导临床工作,尤其是为基层口腔外科医师提供帮助。方法:随机选择2022年9月—2022年12月于四川大学华西口腔医院就诊的214例阻生下颌第三磨牙患者的CBCT数据,分为训练集和测试集。根据专业临床经验提出六型阻力来源分类,并对患者进行人工分类。通过搭建的“人工智能口腔外科医师(artificial intelligence oral surgeon,AIOS)模型”对分类特征进行深度学习及测试,最终由混淆矩阵图和Accuracy-Loss-Epoch曲线分析学习过程及结果。结果:所有模型训练集均达到99.07%~100%的准确率;在测试集中,所有模型的准确率达到80%以上,部分模型达到100%的准确率。结论:AIOS显示出预测CBCT图像上阻生下颌第三磨牙阻力来源并协助口腔外科医师进行口腔图像分析的前景,为未来开发出可用于口腔临床的具备阻力分析、方案制定、模拟手术的全套AIOS奠定了基础。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% accuracy.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 

分 类 号：R782.1[医药卫生—口腔医学]