人工智能在锥形束计算机断层扫描影像中识别慢性根尖周炎根尖区病变的应用

doi:10.7518/hxkq.2022.05.011

华西口腔医学杂志 ›› 2022, Vol. 40 ›› Issue (5): 576-581.doi: 10.7518/hxkq.2022.05.011

人工智能在锥形束计算机断层扫描影像中识别慢性根尖周炎根尖区病变的应用

钱军¹(), 马芮¹, 曲妍², 邓少纯¹, 段瑶¹, 左飞飞³, 王亚杰³, 毋育伟¹()

^1.北京大学口腔医学院·口腔医院第二门诊部国家口腔疾病临床医学研究中心口腔数字化医疗技术和材料国家工程实验室，北京 100020
^2.首都医科大学附属北京康复医院口腔科，北京 100144
^3.北京朗视仪器股份有限公司，北京 100084

收稿日期:2022-04-13 修回日期:2022-07-05 出版日期:2022-10-01 发布日期:2022-10-17
通讯作者: 毋育伟 E-mail:atlasatlas@163.com;yuweiwu@bjmu.edu.cn
作者简介:钱军，副主任医师，硕士，E-mail：atlasatlas@163.com
基金资助:
国家自然科学青年基金(81300851);首都健康保障培育研究(Z181100001618018)

Use and performance of artificial intelligence applications in the diagnosis of chronic apical periodontitis based on cone beam computed tomography imaging

Qian Jun¹(), Ma Rui¹, Qu Yan², Deng Shaochun¹, Duan Yao¹, Zuo Feifei³, Wang Yajie³, Wu Yuwei¹()

^1.Second Clinical Division, Peking University School and Hospital of Stomatology; National Clinical Research Center for Oral Diseases; National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing 100020, China
^2.Dept. of Stomatology, Beijing Rehabilitation Hospital of Capital Medical University, Beijing 100144, China
^3.LargeV Instrument Corp. Ltd, Beijing 100084, China

Received:2022-04-13 Revised:2022-07-05 Online:2022-10-01 Published:2022-10-17
Contact: Wu Yuwei E-mail:atlasatlas@163.com;yuweiwu@bjmu.edu.cn
Supported by:
National Natural Science Foundation of China(81300851);Research of Capital Health Security and Cultivation(Z181100001618018);Correspondence: Wu Yuwei, E-mail: yuweiwu@bjmu.edu.cn

摘要/Abstract

摘要：

目的探讨基于卷积神经网络算法的人工智能（AI）计算机辅助诊断系统在锥形束CT（CBCT）影像上识别慢性根尖周炎根尖区病变的应用。方法收集北京大学口腔医院第二门诊部2017年1月—2021年12月累及单牙根的慢性根尖周炎的CBCT影像，总计49例患者55个牙位。由5位中级职称的临床医生通过Materialize Mimics Medical软件对慢性根尖周炎病变区域识别并进行手动逐层分割，然后通过AI 3D U-Net网络对病损特征进行深度学习，网络分割结果与手动分割数据的一致性，本研究通过交联比（IOU）、Dice系数、像素精确度（PA）在测试集上进行评价。结果神经网络在测试集的IOU为92.18%，Dice系数为95.93%，PA为99.27%。结论 AI和临床医师的慢性根尖周炎病变检出和分割一致性较高，基于本研究深度学习方法的AI系统为下一步检测CBCT图像中的慢性根尖周炎奠定了基础。

关键词: 人工智能, 锥形束计算机断层扫描, 深度学习, 慢性根尖周炎

Abstract:

Objective This study aims to investigate the diagnostic application of an artificial intelligence (AI) computer-aided diagnostic system based on a convolutional neural network algorithm in detecting chronic apical periodontitis in cone beam computed tomography (CBCT) images. Methods CBCT raw data of 55 single root chronic apical pe-riodontitis taken in 2nd Dental Center of Peking University School and Hospital from 49 patients from January 2017 to December 2021 were collected, and the chronic apical periodontitis areas were identified by experienced clinicians ma-nually and segmented layer by layer in Materialise Mimics Medical Software. Deep learning of lesion characterization was conducted via AI 3D U-Net, and the network segmentation results were compared manually with the test sets in terms of intersection over union (IOU), Dice coefficient, and pixel accuracy (PA). Results In our deep learning algorithm, the IOU for all actual true lesions in test set samples was 92.18%, and the Dice coefficient and the PA index were 95.93% and 99.27%, respectively. Lesion segmentation and volume measurements performed by humans and AI systems showed excellent agreement. Conclusion AI systems based on deep learning methods can be applied for detecting chronic apical periodontitis on CBCT images in clinical applications.

Key words: artificial intelligence, cone-beam computed tomography, deep learning, chronic apical periodontitis

中图分类号:

R 445

钱军, 马芮, 曲妍, 邓少纯, 段瑶, 左飞飞, 王亚杰, 毋育伟. 人工智能在锥形束计算机断层扫描影像中识别慢性根尖周炎根尖区病变的应用[J]. 华西口腔医学杂志, 2022, 40(5): 576-581.

Qian Jun, Ma Rui, Qu Yan, Deng Shaochun, Duan Yao, Zuo Feifei, Wang Yajie, Wu Yuwei. Use and performance of artificial intelligence applications in the diagnosis of chronic apical periodontitis based on cone beam computed tomography imaging[J]. West China Journal of Stomatology, 2022, 40(5): 576-581.

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参考文献 17

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人工智能在锥形束计算机断层扫描影像中识别慢性根尖周炎根尖区病变的应用

Use and performance of artificial intelligence applications in the diagnosis of chronic apical periodontitis based on cone beam computed tomography imaging

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