切换至 "中华医学电子期刊资源库"

中华消化病与影像杂志(电子版) ›› 2022, Vol. 12 ›› Issue (01) : 11 -15. doi: 10.3877/cma.j.issn.2095-2015.2022.01.003

论著

基于深度学习实现磁控胶囊内镜操作质量控制的可行性研究
沈大伟1, 李真1, 陈飞雪1, 张皓2, 彭程1, 孔庆周1, 刘静1, 田宝苓1, 李延青1, 任洪波1,()   
  1. 1. 250012 济南,山东大学齐鲁医院消化内科
    2. 430000 安翰科技(武汉)股份有限公司
  • 收稿日期:2021-11-12 出版日期:2022-02-01
  • 通信作者: 任洪波
  • 基金资助:
    山东省重点研发计划(重大科技创新工程)(2019JZZY011007); 山东大学临床研究中心经费支持(2020SDUCRCC022)

A retrospective feasibility study of a quality-control system for real-time supervision of magnetically controlled capsule endoscopy based on deep learning

Dawei Shen1, Zhen Li1, Feixue Chen1, Hao Zhang2, Cheng Peng1, Qingzhou Kong1, Jing Liu1, Baoling Tian1, Yanqing Li1, Hongbo Ren1,()   

  1. 1. Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan 250012, China
    2. Ankon Technologies Co., Ltd, Wuhan 430000, China
  • Received:2021-11-12 Published:2022-02-01
  • Corresponding author: Hongbo Ren
引用本文:

沈大伟, 李真, 陈飞雪, 张皓, 彭程, 孔庆周, 刘静, 田宝苓, 李延青, 任洪波. 基于深度学习实现磁控胶囊内镜操作质量控制的可行性研究[J/OL]. 中华消化病与影像杂志(电子版), 2022, 12(01): 11-15.

Dawei Shen, Zhen Li, Feixue Chen, Hao Zhang, Cheng Peng, Qingzhou Kong, Jing Liu, Baoling Tian, Yanqing Li, Hongbo Ren. A retrospective feasibility study of a quality-control system for real-time supervision of magnetically controlled capsule endoscopy based on deep learning[J/OL]. Chinese Journal of Digestion and Medical Imageology(Electronic Edition), 2022, 12(01): 11-15.

目的

开发一套基于深度学习的人工智能辅助质量控制系统,实现磁控胶囊胃镜检查过程实时质量控制。

方法

回顾性纳入山东大学齐鲁医院、山东美兆健康科技、莒县美年大健康三家内镜中心2019年1月至2020年1月共320例患者磁控胶囊胃镜检查图像资料,经内镜医师标注,基于Resnet-50开发出一套磁控胶囊胃镜辅助质量控制系统,实时进行检查完整度,部位清洁度,检查时间等指标监控,同时向操作者反馈检查质量,规范内镜操作者检查过程,实现磁控胶囊胃镜检查的质量控制。

结果

该磁控胶囊胃镜辅助质量控制系统识别胃内6解剖部位准确度为92.30~98.28%,4类清洁度识别准确度为91.46~95.37%,实际外部磁场操作时间计时准确度可达100%。同时,该质控系统整合至磁控操作系统,可安全运行。

结论

磁控胶囊胃镜辅助质量控制系统可实现质量控制,实时向内镜操作员反馈检查质量,规范内镜操作过程。

Objective

To construct a real-time quality-control system during the stage of magnetically controlled capsule endoscopy for supervision based on deep learning.

Methods

A total of 320 patients with magnetically controlled capsule endoscopy image data were retrospectively included from January 2019 to January 2020 in endoscopy center of Qilu Hospital of Shandong University, Shandong Meizhao Healthy Technology and Juxian Meinian Onehealth Healthcare Holdings.After labeling by experienced endoscopists according to the guideline published in China, a real-time quality-control system including completeness with cleanliness and operation time report was developed based on the Resnet-50 model.With the help of real-time quality-control system, the operator can modulate the inspection process in terms of feedback information provided by quality-control system.

Results

The accuracy of a real-time quality-control system in identifying six anatomical parts of the gastric ranged from 92.30% to 98.28%, the accuracy of system in recognizing four types of mucosal cleanliness ranged from 91.46% to 95.37%, and the accuracy of timing of actual external magnetic field operation time was 100%.Meanwhile this system can be integrated into the magnetic control operating system safely and efficiently.

Conclusion

The real-time quality-control system of magnetically controlled capsule endoscopy can achieve the quality control function in the real-time inspection process, and provide real-time feedback to the endoscopic operator besides provide guidance and help for endoscopic operation.

表1 部位识别模型数据集分布(张)
图1 人工智能辅助质量控制软件界面
图2 部位识别模型混淆矩阵 注:纵坐标为真实标签,横坐标为预测标签
图3 清洁度模型判断混淆矩阵 注:纵坐标为真实标签,横坐标为预测标签
表2 部位识别模型测试结果
表3 清洁度识别模型测试结果
1
Jin G,,Lv J,,Yang M,et al.Genetic risk,incident gastric cancer,and healthy lifestyle:a meta-analysis of genome-wide association studies and prospective cohort study[J].Lancet Oncol202021(10):1378-1386.
2
Necula L,,Matei L,,Dragu D,et al.Recent advances in gastric cancer early diagnosis[J].World J Gastroenterol201925(17):2029-2044.
3
Yao K,,Uedo N,,Kamada T,et al.Guidelines for endoscopic diagnosis of early gastric cancer[J].Dig Endosc202032(5):663-698.
4
廖专,李兆申.磁控胶囊胃镜:开启消化内镜新时代[J].中华消化杂志2019(06):363-366.
5
Hu J,,Wang X,,Sun S.Comparison between the widely used magnetically controlled capsule gastroscopy and conventional gastroscopy:a meta-analysis[J].Minim Invasive Ther Allied Technol2021,1-14.
6
Geropoulos G,,Aquilina J,,Kakos C,et al.Magnetically Controlled Capsule Endoscopy Versus Conventional Gastroscopy:A Systematic Review and Meta-Analysis [J].J Clin Gastroenterol202155(7):577-585.
7
上海国家消化系统疾病临床医学研究中心,国家消化内镜质控中心,中华医学会消化内镜学分会胶囊内镜协作组,等.中国磁控胶囊胃镜临床应用指南(精简版,2021年,上海)[J].中华消化杂志202141(9):582-587.
8
Min J K,,Kwak M S,,Cha J M.Overview of Deep Learning in Gastrointestinal Endoscopy[J].Gut Liver201913(4):388-393.
9
Horie Y,,Yoshio T,,Aoyama K,et al.Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks[J].Gastrointest Endosc201989(1):25-32.
10
Ding Z,,Shi H,,Zhang H,et al.Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model[J].Gastroenterology2019157(4):1044-1054.
11
Wu L,,Zhang J,,Zhou W,et al.Randomised controlled trial of WISENSE,a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy[J].Gut201968(12):2161-2169.
12
徐铭,姚理文,胡珊,等.基于深度学习的消化内镜检查辅助质量控制系统研究(含视频)[J].中华消化内镜杂志202138(2):107-114.
13
Zhou J,,Wu L,,Wan X,et al.A novel artificial intelligence system for the assessment of bowel preparation(with video)[J].Gastrointestinal Endoscopy202091(2):428-435.
14
Xia J,,Xia T,,Pan J,et al.Use of artificial intelligence for detection of gastric lesions by magnetically controlled capsule endoscopy[J].Gastrointest Endosc202193(1):133-139.
15
廖专,王贵齐,陈刚,等.中国磁控胶囊胃镜临床应用专家共识(2017,上海)[J].中华消化内镜杂志201734(10):685-694.
16
廖专,王贵齐,陈刚,等.中国磁控胶囊胃镜临床应用专家共识精简版(2017年,上海)[J].中华消化杂志201737(12):793-795.
17
Zhu S,,Qian Y,,Tang X,et al.Gastric preparation for magnetically controlled capsule endoscopy:A prospective,randomized single-blinded controlled trial[J].Dig Liver Dis201850(1):42-47.
18
He K,,Zhang X,,Ren S,et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.Nevada:USA,2016:770-778.
19
Ye S,,Lu S,,Bai X,et al.ResNet-Locust-BN Network-Based Automatic Identification of East Asian Migratory Locust Species and Instars from RGB Images[J].Insects202011(8).
20
Beg S,,Card T,,Warburton S,et al.Diagnosis of Barrett′s esophagus and esophageal varices using a magnetically assisted capsule endoscopy system[J].Gastrointestinal endoscopy202091(4):773-781.
21
Ching H,,Hale M F,,Sidhu R,et al.Magnetically assisted capsule endoscopy in suspected acute upper GI bleeding versus esophagogastroduodenoscopy in detecting focal lesions[J].Gastrointestinal Endoscopy201990(3):430-439.
22
刘蓉,胡奉环,宋雷,等.磁控胶囊胃镜用于心血管疾病患者消化道检查的有效性及安全性[J].中国循环杂志202035(12):1241-1244.
23
Kaan H L,,Phan P T,,Tiong A M H,et al.First-in-man feasibility study of a novel ingestible magnetically inflated balloon capsule for treatment of obesity[J].Endoscopy International Open20208(5):E607-E610.
24
Xiao Y,,Wu Z,,He S,et al.Fully automated magnetically controlled capsule endoscopy for examination of the stomach and small bowel:a prospective,feasibility,two-centre study[J].Lancet Gastroenterol Hepatol20216(11):914-921.
25
Rey J,,Ogata H,,Hosoe N,et al.Blinded nonrandomized comparative study of gastric examination with a magnetically guided capsule endoscope and standard videoendoscope[J].Gastrointest Endosc201275(2):373-381.
26
Zou W,,Hou X,,Xin L,et al.Magnetic-controlled capsule endoscopy vs.gastroscopy for gastric diseases:a two-center self-controlled comparative trial[J].Endoscopy201547(6):525.
27
Denzer U W,,Rosch T,,Hoytat B,et al.Magnetically guided capsule versus conventional gastroscopy for upper abdominal complaints:a prospective blinded study[J].J Clin Gastroenterol201549(2):101-107.
28
Liao Z,,Hou X,,Lin-Hu E,et al.Accuracy of Magnetically Controlled Capsule Endoscopy,Compared With Conventional Gastroscopy,in Detection of Gastric Diseases[J].Clin Gastroenterol Hepatol201614(9):1266-1273.
29
Hirasawa T,,Aoyama K,,Tanimoto T,et al.Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images[J].Gastric Cancer201821(4):653-660.
[1] 罗辉, 方晔. 品管圈在提高甲状腺结节细针穿刺检出率中的应用[J/OL]. 中华医学超声杂志(电子版), 2024, 21(10): 972-977.
[2] 王益佳, 周青, 曹省, 袁芳洁, 周妍, 张梅. 中国经胸超声心动图检查存图及报告质控现状分析[J/OL]. 中华医学超声杂志(电子版), 2024, 21(07): 657-663.
[3] 周易, 张红梅, 尹立雪, 杨浩, 付培. 四川省超声医学质量控制指标动态变化趋势分析[J/OL]. 中华医学超声杂志(电子版), 2024, 21(07): 664-670.
[4] 顾莉莉, 姜凡. 安徽省超声产前筛查切面图像质量现状调查情况及分析[J/OL]. 中华医学超声杂志(电子版), 2024, 21(07): 671-674.
[5] 王晓娜, 张宁, 宋伟, 杨明, 李丽, 薛红元. 河北省超声医学质量管理与控制现状分析[J/OL]. 中华医学超声杂志(电子版), 2024, 21(07): 675-680.
[6] 张亚庆, 黄旴宁, 许珊珊, 刘小蓝. 海南省二级与三级医院超声医学质量控制指标分析[J/OL]. 中华医学超声杂志(电子版), 2024, 21(07): 681-685.
[7] 刘畅, 蒋洁, 胥雪冬, 崔立刚, 王淑敏, 陈文. 北京市海淀区医疗机构甲状腺超声检查及TIRADS分类基线调查[J/OL]. 中华医学超声杂志(电子版), 2024, 21(07): 693-697.
[8] 吴禾禾, 马春亮, 常青, 陈宇, 牛丽娟, 王勇. 超声医学质量控制与住院医师规范化培训相结合的实践探讨[J/OL]. 中华医学超声杂志(电子版), 2024, 21(07): 698-701.
[9] 李洋, 蔡金玉, 党晓智, 常婉英, 巨艳, 高毅, 宋宏萍. 基于深度学习的乳腺超声应变弹性图像生成模型的应用研究[J/OL]. 中华医学超声杂志(电子版), 2024, 21(06): 563-570.
[10] 张嘉炜, 王瑞, 张克诚, 易磊, 周增丁. 烧烫伤创面深度智能检测模型P-YOLO的建立及测试效果[J/OL]. 中华损伤与修复杂志(电子版), 2024, 19(05): 379-385.
[11] 叶莉, 杜宇. 深度学习在牙髓根尖周病临床诊疗中的应用[J/OL]. 中华口腔医学研究杂志(电子版), 2024, 18(06): 351-356.
[12] 黄俊龙, 李文双, 李晓阳, 刘柏隆, 陈逸龙, 丘惠平, 周祥福. 基于盆底彩超的人工智能模型在女性压力性尿失禁分度诊断中的应用[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 597-605.
[13] 嵇振岭, 陈杰, 唐健雄. 重视复杂腹壁疝手术并发症的预防和处理[J/OL]. 中华疝和腹壁外科杂志(电子版), 2024, 18(06): 601-606.
[14] 赵毅, 李昶田, 唐文博, 白雪婷, 刘荣. 腹腔镜术中超声主胰管自动识别模型的临床应用[J/OL]. 中华腔镜外科杂志(电子版), 2024, 17(05): 290-294.
[15] 孙铭远, 褚恒, 徐海滨, 张哲. 人工智能应用于多发性肺结节诊断的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 785-790.
阅读次数
全文


摘要