切换至 "中华医学电子期刊资源库"

中华肺部疾病杂志(电子版) ›› 2019, Vol. 12 ›› Issue (06) : 757 -759. doi: 10.3877/cma.j.issn.1674-6902.2019.06.019

短篇论著

人工智能肺结节辅助诊断软件在肺CT检查结节分析中的临床应用
王爽1, 雷盛1, 谷涛1, 张启川1,()   
  1. 1. 400037 重庆,陆军军医大学(第三军医大学)第二附属医院放射诊断科
  • 收稿日期:2019-03-19 出版日期:2019-12-20
  • 通信作者: 张启川

Clinical application of artificial intelligence pulmonary nodules auxiliary diagnosis software in the analysis of pulmonary ct nodules

Shuang Wang1, Sheng Lei1, Tao Gu1   

  • Received:2019-03-19 Published:2019-12-20
引用本文:

王爽, 雷盛, 谷涛, 张启川. 人工智能肺结节辅助诊断软件在肺CT检查结节分析中的临床应用[J]. 中华肺部疾病杂志(电子版), 2019, 12(06): 757-759.

Shuang Wang, Sheng Lei, Tao Gu. Clinical application of artificial intelligence pulmonary nodules auxiliary diagnosis software in the analysis of pulmonary ct nodules[J]. Chinese Journal of Lung Diseases(Electronic Edition), 2019, 12(06): 757-759.

表1 病变检出及分析所占用时间情况对比
图1 右肺上叶后段中等密度小结节,大小4.69 mm×3.28 mm,分析软件及医师均能诊断出
图2 右肺上叶前段稍低密度影小结节,大小4.77 mm×3.34 mm,分析软件能够准确检查出,但医师出现漏诊
图3 右肺下叶背段,分析软件误诊为结节,大小17.99 mm×6.12 mm,实际为肺静脉血管
1
Economopoulou P, Mountzios G. The emerging treatment landscape of advanced non-small cell lung cancer[J]. Ann Transl Med, 2018, 6: 138.
2
Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015[J]. CA Cancer J Clin, 2016, 66: 115-132.
3
Cardiothoracic Group, Radiology Branch. Chinese medical association,Low dose spiral CT lung cancer screening expert consensus[J]. Zhonghua Fang She Xue Za Zhi, 2015(5): 328-335.
4
Suzuki K, Koike T, Asakawa T, et al. A prospective radiological study of thin-section computed tomography to predict pathological noninvasiveness in peripheral clinical IA lung cancer (Japan Clinical Oncology Group 0201)[J]. J Thorac Oncol, 2011, 6: 751-756.
5
Zhang GZ, Bai CX. Chest low-dose CT screening for lung cancer is and is not[J]. Zhonghua Jie He He Hu Xi Za Zhi, 2015, 38(4): 242-245.
6
Tang W, Wu N, Huang Y, et al. Results of low-dose computed tomography (LDCT) screening for early lung cancer: prevalence in 4,690 asymptomatic participants[J]. Zhonghua Zhong Liu Za Zhi, 2014, 36(7): 549-554.
7
Zhang Y, Hong QY, Shi WB, et al. Value of low-dose spiral computed tomography in lung cancer screening[J]. Zhonghua Yi Xue Za Zhi, 2013, 93(38): 3011-3014.
8
Ferreira JJ, Oliveira MC, de Azevedo-Marques PM. Cloud-based NoSQL open database of pulmonary nodules for computer-aided lung cancer diagnosis and reproducible research[J]. J Digit Imaging, 2016, 29(6): 716-729.
9
Freedman MT. Comment on "Maximum-Intensity-Projection and Computer-Aided-Detection Alogorithms as Stand-Alone Reader Devices in Lung Cancer Screening Using Different Dose levels and Reconstruction Kernels" [J]. AJR Am J Roentgenol, 2017, 208(3): W132.
10
Kobayashi H, Ohkubo M, Narita A, et al. A method for evaluating the performance sf computer-aided detection of pulmonary nodules in lung cancer CT screening: detection limit for nodule size and density[J]. Br J Radiol, 2017, 90(1070): 20160313.
11
Ebner L, Roos JE, Christe A. Reply to "Comment on Maximum-Intensity-Projection and Computer-Aided-Detection Algorithms as Stand-Alone Reader Devices in Lung Cancer Screening Using Different Dose Levels and Reconstruction Kernels" [J]. AJR Am Roentgenol, 2017, 208(3): W133.
12
Huang P, Park S, Yan R, et al. Added value of computer-aided CT image features for early lung cancer diagnosis with small pulmonary nodules: A matched case-control study[J]. Radiology, 2018, 286(1): 286-295.
13
Al MB, Brennan PC, Mello-Thoms C. A review of lung cancer screening and the role of computer-aided detection[J]. Clin Radiol, 2017, 72(6): 433-442.
14
LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436.
15
Silver D, Huang A, Maddison CJ, et al. Mastering the game of Go with deep neural networks and tree search[J]. Nature, 2016, 529(7587): 484-489.
16
Moravcík M, Schmid M, Burch N, et al. DeepStack: Expert-level artificial intelligence in heads-up no-limit poker[J]. Science, 2017, 356(6337): 508-513.
17
Nishio M, Nagashima C. Computer-aided diagnosis for lung cancer:usefulness of nodule heterogeneity[J]. Acad Radiol, 2017, 24(3): 328-336.
18
Ebner L, Roos JE, Christensen JD, et al. Maximum-intensity-projection and computer-aided-detection algorithms as stand-alone reader devices in lung cancer screening using different dose levels and reconstruction kernels[J]. AJR Am J Roentgenol, 2016, 207(2): 282-288.
19
Young S, Lo P, Kim G, et al. The effect of radiation dose reduction on computer-aided-detection(CAD) perfoemance in a low-dose lung cancer screening population[J]. Med phys, 2017, 44(4): 1337-1346.
20
Kobayashi H, Ohkubo M, Narita A, et al. A method for evaluating the performance sf computer-aided detection of pulmonary nodules in lung cancer CT screening:detection limit for nodule size and density[J]. Br J Radiol, 2017, 90(1070): 20160313.
21
Ohkubo M, Narita A, Wada S, et al. Technical Note: Image filtering to make computer-aided detection robust to image reconstruction kernel choice in lung cancer CT screening[J]. Med Phys, 2016, 43(7): 4098.
22
Song QZ, Zhao L, Luo XK, et al. Using deep learning for classification of lung nodules on computed tomography images[J]. J Healthc Eng, 2017: 8314740.
23
Li W, Cao P, Zhao DZ, et al. Pulmonary nodule classification with deep convolutional neural networks on computed tomography images[J]. Comput Math Method Med, 2016: 6215085.
24
Ginneken VB. Fifty years of computer analysis in chest imaging:rule-based, machine learning, deep learning[J]. Radiol Phys Technol, 2017, 10(1): 23-32.
25
Gruetzemacher R, Gupta A. Using deep learning for pulmonary nodule detection & diagnosis. Twenty-second Americas conference on information systems, San Diego 2016.

URL    
26
Nibali A, He Z, Wollersheim D. Pulmonary nodule classification with deep residual networks[J]. Int J Comput Ass Rad, 2017, 12(10):1799-1808.
27
Hussein S, Gillies R, Cao K, et al. TumorNet: lung nodule characterization using multi-view convolutional neural network with gaussian process. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, 2017: 1007-1010. doi:10.1109/ISBI.2017.7950686.

URL    
[1] 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]. 中华医学超声杂志(电子版), 2024, 21(06): 563-570.
[2] 杨敬武, 周美君, 陈雨凡, 李素淑, 何燕妮, 崔楠, 刘红梅. 人工智能超声结合品管圈活动对低年资超声医师甲状腺结节风险评估能力的作用[J]. 中华医学超声杂志(电子版), 2024, 21(05): 522-526.
[3] 罗刚, 泮思林, 孙玲玉, 李志新, 陈涛涛, 乔思波, 庞善臣. 一种新型语义网络分析模型对室间隔完整型肺动脉闭锁和危重肺动脉瓣狭窄胎儿右心发育不良程度的评价作用[J]. 中华医学超声杂志(电子版), 2024, 21(04): 377-383.
[4] 伯小皖, 郭乐杭, 余松远, 李明宙, 孙丽萍. 甲状腺结节人工智能自动分割和分类系统的建立和验证[J]. 中华医学超声杂志(电子版), 2024, 21(03): 304-309.
[5] 孔德铭, 刘铮, 李睿, 钱文伟, 王飞, 蔡道章, 柴伟. 人工智能辅助全髋关节置换三维术前规划准确性评价[J]. 中华关节外科杂志(电子版), 2024, 18(04): 431-438.
[6] 何淳诺, 田志敏, 李焕玺, 吴昊越, 庄凯鹏, 周胜虎, 张浩强. 小儿发育性髋关节发育不良诊治的研究进展[J]. 中华关节外科杂志(电子版), 2024, 18(04): 497-504.
[7] 莫淇舟, 苏劲, 黄健, 李健维, 李思宁, 柳建军. 智能控压输尿管软镜碎石吸引取石术在直径10~25 mm上尿路结石中的应用[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(05): 497-502.
[8] 苏博兴, 肖博, 李建兴. 2024年美国泌尿外科学会年会结石领域手术治疗相关热点研究及解读[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(04): 303-308.
[9] 莫林键, 杨舒博, 农卫赟, 程继文. 人工智能虚拟数字医师在钬激光前列腺剜除日间手术患教管理中的应用[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(04): 318-322.
[10] 阮星星, 黄智渊, 刘芙香, 狄金明. 从临床医师诊治患者的思路出发撰写临床研究论文[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(04): 397-401.
[11] 刘锦程, 王斌, 张雯, 张明周, 刘禹, 叶东樊, 黄赞胜, 邱凌霄, 卿斌, 王创业, 王南博, 王苹, 郭宇航, 周培花, 程秋霞, 徐智. 肺泡灌洗液RASSF1A及SHOX2甲基化联合径向超声特征对肺结节性质鉴别诊断的意义[J]. 中华肺部疾病杂志(电子版), 2024, 17(04): 505-511.
[12] 李梦阳, 张恩犁, 吴俊杰, 赵之明. 人工智能视觉图像分割在腔镜外科教学中的应用[J]. 中华腔镜外科杂志(电子版), 2024, 17(03): 129-134.
[13] 欧阳佳裕, 李刚, 贺露瑶, 罗娜. 双层探测器光谱CT在早期原发性肝癌中的诊断价值[J]. 中华肝脏外科手术学电子杂志, 2024, 13(04): 557-561.
[14] 杨旌旗, 魏雨荷, 宿翀, 沈燕, 张亚男. 针灸技术传承工具研发相关思路、方法及现状分析[J]. 中华针灸电子杂志, 2024, 13(03): 112-116.
[15] 张玮玮, 霍晓川. 人工智能时代医学生批判性思维培养的重要性[J]. 中华脑血管病杂志(电子版), 2024, 18(04): 357-359.
阅读次数
全文


摘要