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/OL]. 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/OL]. 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/OL]. 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/OL]. 中华医学超声杂志(电子版), 2024, 21(05): 522-526.
[3] 明昊, 肖迎聪, 巨艳, 宋宏萍. 乳腺癌风险预测模型的研究现状[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(05): 287-291.
[4] 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.
[5] 熊鹰, 林敬莱, 白奇, 郭剑明, 王烁. 肾癌自动化病理诊断:AI离临床还有多远?[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 535-540.
[6] 李伟, 宋子健, 赖衍成, 周睿, 吴涵, 邓龙昕, 陈锐. 人工智能应用于前列腺癌患者预后预测的研究现状及展望[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 541-546.
[7] 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.
[8] 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/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(05): 497-502.
[9] 李义亮, 苏拉依曼·牙库甫, 麦麦提艾力·麦麦提明, 克力木·阿不都热依木. 机器人与腹腔镜食管裂孔疝修补术联合Nissen 胃底折叠术短期疗效分析[J/OL]. 中华疝和腹壁外科杂志(电子版), 2024, 18(05): 512-517.
[10] 犹成亿, 尤恒, 叶东樊, 张雯, 刘禹, 王仁宇, 苏琳茜, 甘慧, 徐智. 基于3D Res U-Net-Faster RCNN 技术和CT 影像学特征的肺结节性质预测模型的建立[J/OL]. 中华肺部疾病杂志(电子版), 2024, 17(05): 673-679.
[11] 钱春蕊, 周燕, 张晶, 蔡笃财, 门慧, 王松海, 黎莉, 邢龙. 高分辨率CT 与多层螺旋CT 在肺结节及早期肺癌中的应用[J/OL]. 中华肺部疾病杂志(电子版), 2024, 17(05): 827-830.
[12] 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/OL]. 中华肝脏外科手术学电子杂志, 2024, 13(04): 557-561.
[13] 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.
[14] 杨松林, 黄仕豪, 王丽珠, 李禧萌, 邹飞翔, 李坤炜, 梁明柱, 陈炳辉. 良性肺结节生长变化的影像学评价[J/OL]. 中华介入放射学电子杂志, 2024, 12(04): 344-350.
[15] 王嘉巍, 张广健, 杜昊楠, 余宸傲, 张佳. 混合现实技术在肺结节手术中的应用进展[J/OL]. 中华胸部外科电子杂志, 2024, 11(04): 254-259.
阅读次数
全文


摘要