国际口腔医学杂志

国际口腔医学杂志 ›› 2024, Vol. 51 ›› Issue (4): 483-488.doi: 10.7518/gjkq.2024063

• 综述 • 上一篇    

卷积神经网络在牙体牙髓病学中的应用进展

陈新月1(),潘晓予2,杨燕1,贾媛媛2,陈亮1()   

  1. 1.重庆医科大学附属口腔医院牙体牙髓科 重庆 400000
    2.重庆医科大学医学信息学院 重庆 400000
  • 收稿日期:2023-07-26 修回日期:2024-02-26 出版日期:2024-07-01 发布日期:2024-06-24
  • 通讯作者: 陈亮
  • 作者简介:陈新月，医师，硕士，Email：2021120627@stu.cqmu.edu.cn
  • 基金资助:
    重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX0128);重庆市博士后研究项目(2021XM3062);重庆医科大学未来医学青年创新团队发展支持计划(W0034)

Application progress of convolution neural network in endodontics

Xinyue Chen1(),Xiaoyu Pan2,Yan Yang1,Yuanyuan Jia2,Liang Chen1()   

  1. 1.Dept. of Endodontics, Stomatological Hospital of Chongqing Medical University, Chongqing 400000, China
    2.School of Medical Information, Chongqing Medical University, Chongqing 400000, China
  • Received:2023-07-26 Revised:2024-02-26 Online:2024-07-01 Published:2024-06-24
  • Contact: Liang Chen
  • Supported by:
    General Program of Natural Science Foundation of Chongqing Municipality(CSTB2022NSCQ-MSX0128);Chongqing Postdoctoral Research Program(2021XM3062);Chongqing Medical University Future Medical Youth Innovation Team Support Program (W0034)

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摘要：

在计算机算法迅速发展的信息时代，深度学习的应用在各个领域均受到了广泛关注，而卷积神经网络则是深度学习中最为典型的网络结构之一，具有突出的学习能力及适应能力，在图像的识别处理上表现尤为出色。与此同时，在牙体牙髓病学的发展过程中，卷积神经网络的应用也越发常见，例如协助医生进行龋病、根尖周病的分析、诊断、治疗、预后评估等，有利于缓解医疗资源紧缺、推动牙体牙髓病学的发展。本文就卷积神经网络在牙体牙髓病学中的应用进展进行总结，并对其未来发展可能进行初步展望。

关键词: 深度学习, 卷积神经网络, 牙体牙髓病学

Abstract:

In the information age with the rapid development of computer algorithms, the application of deep learning has received extensive attention in various fields. As one of the most typical network structures in deep learning, a convolutional neural network has outstanding learning ability and great adaptability and plays an important role especially in image recognition and processing. Meanwhile, in the development of endodontics, the application of convolutional neural networks has become increasingly common, such as in assisting doctors in the analysis, diagnosis, treatment, and prognosis evaluation of caries and periapical diseases. Such networks have contributed to alleviating the shortage of medical resources and promoting the development of endodontics. This paper mainly summarizes the application of convolutional neural networks in endodontics and looks forward to its future.

Key words: deep learning, convolution neural network, endodontics

中图分类号: 

  • R781.1

图1

ResNet模型完成图像分类任务的具体结构"

图2

CNN模型在龋病诊断中的应用示意"

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