深度学习在口腔种植影像学中的应用：研究进展与挑战

doi:10.11871/jfdc.issn.2096-742X.2024.03.004

数据与计算发展前沿 ›› 2024, Vol. 6 ›› Issue (3): 41-49.

CSTR: 32002.14.jfdc.CN10-1649/TP.2024.03.004

doi: 10.11871/jfdc.issn.2096-742X.2024.03.004

• 专刊：智慧医疗前沿与进展（下） • 上一篇下一篇

深度学习在口腔种植影像学中的应用：研究进展与挑战

郑懿诺³(),孙沐毅²,张虹云⁴,张婧⁴,邓天政¹,刘倩^1,^*()

1.空军军医大学，空军特色医学中心口腔科，北京 100142
2.北京邮电大学，人工智能学院，北京 100876
3.空军军医大学，基础医学院，陕西西安 710032
4.空军军医大学第三附属医院，口腔解剖生理学教研室，陕西西安 710032

收稿日期:2023-10-29 出版日期:2024-06-20 发布日期:2024-06-21
通讯作者: *刘倩（E-mail: 13259776870@163.com）
作者简介:郑懿诺，空军军医大学基础医学院，本科生，获得全国大学生英语竞赛三等奖、“互联网＋”大学生创新创业大赛校赛三等奖、中国病理生理学会“人类技能与生命健康”科普作品大赛一等奖、第五届康复科普创新大赛优胜奖等。研究兴趣为口腔种植学等。
本文中负责文献收集及初稿撰写。
ZHENG Yinuo is an undergraduate student at the Basic Medical Science Academy, the Fourth Military Medical University. She won the third prize in the National English Competition for College Students, the third prize in the "Internet+" College Student Innovation and Entrepreneurship Competition, the first prize in the "Human Technology and Life Health" Science Popularization Competition of the Chinese Society of Pathology and Physiology, and the winning prize in the 5th Rehabilitation Science Popularization Innovation Competition. Her research interests include oral implantology.
In this paper, she is responsible for literature collection and draft writing.
E-mail: zhengyinuo002X@qq.com|刘倩，空军军医大学空军特色医学中心口腔科，主治医师，博士，以第一作者或通讯作者（含共同）发表论文11篇，主要研究领域为数字化技术在口腔领域的应用。
本文中负责论文修改及定稿。
LIU Qian, Ph.D., is the attending physician of the Department of Stomatology, Air Force Medical Center, the Fourth Military Medical University. She has published 11 papers as the first author or corresponding author (including co-corresponding author). Her research interests include the application of digital technology in the field of dentistry.
In this paper, she is responsible for the revision and finalization of the paper.
E-mail: 13259776870@163.com
基金资助:
国家自然科学基金青年项目(82301111);国家自然科学基金青年项目(82201089);陕西省自然科学基础研究项目(2022JQ-888);陕西省重点研发计划项目资助(2022SF-441);空军军医大学临床研究项目(2022LC2204)

Application of Deep Learning in Dental Implant Imaging: Research Progress and Challenges

ZHENG Yinuo³(),SUN Muyi²,ZHANG Hongyun⁴,ZHANG Jing⁴,DENG Tianzheng¹,LIU Qian^1,^*()

1. Department of Stomatology, Air Force Medical Center, PLA, The Fourth Military Medical University, Beijing 100142, China
2. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
3. School of Basic Medical Sciences, The Fourth Military Medical University, Xi'an, Shaanxi 710032, China
4. Department of Oral Anatomy and Physiology, Third Affiliated Hospital of The Fourth Military Medical University, Xi'an, Shaanxi 710032, China

Received:2023-10-29 Online:2024-06-20 Published:2024-06-21

摘要/Abstract

摘要：

【目的】系统性地回顾和总结深度学习在口腔种植领域的研究进展，包括口腔影像处理、种植体系统检测以及口腔种植预后的应用。【方法】基于深度学习在口腔种植领域的研究，按照研究方向进行分类总结，阐述相关研究的主要研究内容及结论。【结果】深度学习技术在口腔种植领域已取得显著成就。口腔影像中的智能分割和识别技术提高了口腔医生的诊断准确性和工作效率，而口腔种植体系统的自动化检测有助于更快速地了解患者的口腔情况。此外，深度学习还在口腔种植预后的预测中发挥关键作用，帮助医生提前干预并改善治疗结果。【结论】深度学习在口腔种植领域具有巨大潜力，有助于推动口腔种植更加精准高效，为口腔医生赋能。

关键词: 口腔种植, 深度学习, 神经网络, CBCT

Abstract:

[Objective] This study systematically reviews and summarizes the research progress of deep learning in the field of implantology, including its applications in image processing, implant system detection, and dental implant prognosis. [Methods] The research in the field of dental implantology based on deep learning is classified and summarized according to research directions, elucidating the main research topics and conclusions. [Results] Deep learning technology has made significant achievements in the field of oral implantology. Intelligent segmentation and recognition techniques in oral imaging have improved the diagnostic accuracy and efficiency of oral healthcare professionals. Additionally, automated detection of implant systems in oral surgery aids in rapid assessing of patients'oral conditions. Furthermore, deep learning plays a crucial role in predicting oral implant outcomes, enabling healthcare providers to intervene early and enhance treatment results. [Conclusions] Deep learning holds immense potential in the field of oral implantology, facilitating more precise and efficient procedures, thereby empowering oral healthcare professionals.

Key words: oral implantology, deep learning, neural networks, cone-beam computed tomography (CBCT)

郑懿诺, 孙沐毅, 张虹云, 张婧, 邓天政, 刘倩. 深度学习在口腔种植影像学中的应用：研究进展与挑战[J]. 数据与计算发展前沿, 2024, 6(3): 41-49.

ZHENG Yinuo, SUN Muyi, ZHANG Hongyun, ZHANG Jing, DENG Tianzheng, LIU Qian. Application of Deep Learning in Dental Implant Imaging: Research Progress and Challenges[J]. Frontiers of Data and Computing, 2024, 6(3): 41-49, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2024.03.004.

图/表 1

表1

文献基本信息"

文献编号	发表年份	所用算法	研究对象	研究内容
[1]	2020	U-Net及RNN	CBCT	分割牙根影像
[2]	2022	EfficientDet-D3	曲面断层片	识别牙齿及补料等
[3]	2023	YOLOv7	曲面断层片	识别牙齿及修复体等
[4]	2022	Mask R-CNN及Faster R-CNN	曲面断层片	分割识别牙齿
[5]	2023	CNN transformer UNet	CBCT	分割识别牙齿
[6]	2022	DenseNet169	CBCT	识别缺失牙位
[7]	2022	Faster R-CNN	根尖片	识别种植体及其周围组织
[8]	2023	3D-UNet	CBCT	分割识别种植体
[9]	2022	UNet	CBCT	分割上颌窦
[10]	2023	YOLOv5	CBCT	识别并分类异常上颌窦
[11]	2020	UNet	CBCT	分割下颌神经管
[12]	2022	3D U-Net	CBCT	分割下颌神经管
[13]	2022	Multi-Scale input Residual UNet	CBCT	分割下颌神经管
[14]	2023	类UNet	CBCT	分割下颌神经管
[15]	2023	STSNet	数字化口内扫描	分割牙齿
[16]	2020	Densenet	CBCT及数字化口内扫描	配准方法
[17]	2020	SqueezeNet、GoogLeNet、ResNet-18、MobileNet-v2及ResNet-50	根尖片	识别种植体系统
[18]	2020	GoogLeNet Inception CNN network	曲面断层片	诊断多种牙齿问题
[19]	2023	EfficientNet及Res2Next	曲面断层片	识别种植体系统
[20]	2023	YOLO	曲面断层片	种植体系统分类
[21]	2020	automated deep convolutional neural network	曲面断层片及根尖片	识别种植体系统
[22]	2022	automated deep convolutional neural network	曲面断层片	识别种植体系统
[23]	2023	automatic DL	曲面断层片及根尖片	识别种植体系统
[24]	2023	automatic DL	曲面断层片及根尖片	识别种植体系统
[25]	2021	ResNet	曲面断层片	识别种植体系统及治疗阶段
[26]	2022	Faster R-CNN	根尖片	检测种植体边缘骨丧失
[27]	2023	MobileNet-V3	曲面断层片及根尖片	检测种植体骨结合情况
[28]	2021	R-CNN	根尖片	检测种植体边缘骨丧失
[29]	2023	YOLOv3	根尖片及咬合翼片	检测种植体边缘骨丧失
[30]	2023	YOLOv2	根尖片	检测种植体周围炎
[31]	2022	VGG16、Inception V3及ResNet50	CBCT	预测种植体脱落风险
[32]	2023	ResNet-50	曲面断层片及根尖片	预测种植体预后
[33]	2021	RCNN	CBCT	评估种植体稳定性

表1

参考文献 36

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深度学习在口腔种植影像学中的应用：研究进展与挑战

Application of Deep Learning in Dental Implant Imaging: Research Progress and Challenges

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