机器学习在口腔种植学中的应用研究进展

doi:10.7518/gjkq.2023069

摘要/Abstract

摘要：

近年来，人工智能技术的发展正在逐渐改变传统的医疗行业，机器学习是实现人工智能的主流方法。在口腔种植领域，机器学习技术可以辅助医生的诊疗过程，包括影像学资料智能识别、种植方案优化、自动化机器人等方面，是未来口腔种植学的发展趋势。本文就机器学习在口腔种植领域的研究进展做一总结，并对口腔种植的数字化、智能化未来进行展望。

关键词: 机器学习, 人工智能, 口腔种植

Abstract:

In recent years, artificial intelligence has been gradually changing the traditional medical industry, and machine learning is the main approach to realize it. In aspect of dental implantation, it can assist doctors in their daily diagnosis and treatment, including intelligent recognition of imaging data, implant scheme optimization, automatic robot and so on. It is the future of dental implantation. This paper summarizes the research progress of machine learning in dental implantation and looks forward to its intellectualization in the future.

Key words: machine learning, artificial intelligence, dental implant

中图分类号:

R 782

朱可石,廖安琪,余优成. 机器学习在口腔种植学中的应用研究进展[J]. 国际口腔医学杂志, 2023, 50(4): 491-498.

Zhu Keshi,Liao Anqi,Yu Youcheng.. Research progress on the application of machine learning in dental implantology[J]. Int J Stomatol, 2023, 50(4): 491-498.

图/表 1

表 1

不同CNN模型智能识别/分类口腔种植系统的内容及比较"

文献	CNN模型	影像学资料	模型评价指标	结果	结论
[9]	SqueezeNet；GoogLeNet；Res-Net-18；MobileNet-v2；ResNet-50	801张来源于根尖X线片的单种植体图片	准确率、平均精度（average precision，AP）、召回率、F1评分	5种模型的测试AP均超过90%；MobileNet-v2和Squee-zeNet的准确率分别约达96%和97%	使用参数不到400万的小型网络和少量的图像，CNN也能达较高的精度
[10]	CNN（Keras＆Tensorflow）	1 800张根尖X线片	准确率、敏感性、特异性、阳性/阴性预测值（positive predictive value，PPV/ negative predictive value，NPV）、受试者操作特征（receiver operating characteristic，ROC）曲线	训练数据组、测试数据组和验证数据的系统准确率分别为99.78%、99.36%和85.29%	CNN识别较少种植系统（3个）时可以达到很高的准确率
[11]	微调版You Only Look Once(YOLO) v3-	1 282张曲面体层片	真阳性率（true positive，TP）、AP、平均AP、平均交并比	识别每个种植体系统的TP和AP分别为0.50~0.82和0.51~0.85	图片质量和样本数量影响了模型对同品牌不同种植系统的识别准确率
[12]	CNN-transfer-Inception v3	1 206张来源于根尖X线片的种植体图片	准确率、敏感性、特异性、PPV、NPV、ROC曲线和ROC曲线下面积（area under curve，AUC）	诊断准确率93.8%；敏感性为93.5%；特异性为94.2%；PPV为92%	深度CNN模型在从X线片识别牙种植体方面有良好的表现。但影像资料数据库规模太小
[13]	基础CNN；VGG16-transfer；微调版VGG16 VGG19-transfer；微调版VGG19	8 859张来源于曲面体层片的种植体图像	准确率、精密度、召回率、ROC、F1评分	微调版VGG16和微调版VGG19的准确率分别达0.94和0.93	对深度CNN模型的特定卷积块进行微调，深度CNN可以更专门地用于特定分类任务
[14]	多任务CNN：ResNet18、34、50、101和152	9 767张来源于曲面体层片的种植体图像	准确率、AP、召回率、特异性、F1评分、ROC曲线、AUC	多任务模式下，CNN识别准确率均超98%；多任务模式下，CNN模型的各评价指标都显著高于单任务模式	多任务模式下的迭代学习显著提高了CNN模型的准确性
[15]	自动化深层CNN（automate deep CNN， ADCNN）：Neuro-T version 2.0.1	7 146张曲面体层片、4 384张根尖X线片	ROC曲线、AUC、Youden指数、敏感性、特异性	ADCNN的AUC、Youden指数、敏感性和特异性值分别为0.95、0.81、0.96和0.85	ADCNN的表现显著优于大多数参与对照实验的口腔医生

表 1

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