Current situation and prospect of artificial intelligence in the diagnosis and treatment of gastrointestinal tumors using image deep learning_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

LI Shaodong , HONG Sen , LI Shiquan , DONG Ruizhi , LIANG Bin ,  KANG Zhenhua

Department of Colorectal and Anal Surgery, The First Hospital of Jilin University, Changchun 130021, P. R. China;

Corresponding author：

KANG Zhenhua, Email: k_z_h75@163.com

Keywords：

artificial intelligence; deep learning; gastrointestinal tumor; endoscopic image

DOI：

10.7507/1007-9424.202012127

Video：

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Objective To summarize the application status of artificial intelligence (AI) in the diagnosis and treatment of gastrointestinal tumors using image deep learning, as well as its application prospect. Method Literatures on AI in the field of gastrointestinal tumors in recent years were reviewed and summarized.Results AI had developed rapidly in the medical field. The gastrointestinal endoscopy, imaging examination, and pathological diagnosis assisted by AI technology could assist doctors to make more accurate diagnosis opinions, and make the diagnosis and treatment of gastrointestinal tumors develop towards a more accurate and efficient direction. However, the application of AI in the medical field had just begun, and it still needed to be popularized for a long time.Conclusion The gastrointestinal endoscopy system, imaging examination system, and pathological diagnosis assisted by AI technology all show high specificity and sensitivity, which obviously reflects its high efficiency and accuracy.

Citation： LI Shaodong, HONG Sen, LI Shiquan, DONG Ruizhi, LIANG Bin, KANG Zhenhua. Current situation and prospect of artificial intelligence in the diagnosis and treatment of gastrointestinal tumors using image deep learning. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2021, 28(11): 1524-1529. doi: 10.7507/1007-9424.202012127 Copy

1.	Wei WQ, Chen ZF, He YT, et al. Long-term follow-up of a community assignment, one-time endoscopic screening study of esophageal cancer in China. J Clin Oncol, 2015, 33(17): 1951-1957.
2.	蔡世伦, 阿依木克地斯·亚力孔, 李染, 等. 基于深度学习的人工智能辅助诊断在食管早癌中的应用. 中华消化内镜杂志, 2019, 36(4): 246-250.
3.	Horie Y, Yoshio T, Aoyama K, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc, 2019, 89(1): 25-32.
4.	腾讯发布一个 AI 神器有望攻克食管癌早筛难题. 信息与电脑(理论版), 2017, 15: 5.
5.	石善江, 王宏光, 刘时助. 应用卷积神经网络的人工智能技术在早期食管癌诊断中的临床分析. 中外医疗, 2019, 38(18): 7-9, 16.
6.	郑荣寿, 孙可欣, 张思维, 等. 2015 年中国恶性肿瘤流行情况分析. 中华肿瘤杂志, 2019, 49(1): 19-28.
7.	Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
8.	Hirasawa T, Aoyama K, Tanimoto T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer, 2018, 21(4): 653-660.
9.	李夏, 吴练练, 于红刚. 人工智能胃镜在盲区监测和自主图像采集中的应用研究. 中华消化内镜杂志, 2019, 36(4): 240-245.
10.	Ishioka M, Hirasawa T, Tada T. Detecting gastric cancer from video images using convolutional neural networks. Dig Endosc, 2019, 31(2): e34-e35.
11.	Itoh T, Kawahira H, Nakashima H, et al. Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images. Endosc Int Open, 2018, 6(2): E139-E144 .
12.	Winawer SJ, Zauber AG, Ho MN, et al. Prevention of colorectal cancer by colonoscopic polypectomy. The National Polyp Study Workgroup. N Engl J Med, 1993, 329(27): 1977-1981.
13.	Zauber AG, Winawer SJ, O’Brien MJ, et al. Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths. N Engl J Med, 2012, 366(8): 687-696.
14.	Yamada M, Saito Y, Imaoka H, et al. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Sci Rep, 2019, 9(1): 14465-14473.
15.	Leufkens AM, DeMarco DC, Rastogi A, et al. Effect of a retrograde-viewing device on adenoma detection rate during colonoscopy: the TERRACE study. Gastrointest Endosc, 2011, 73(3): 480-489.
16.	DeMarco DC, Odstrcil E, Lara LF, et al. Impact of experience with a retrograde-viewing device on adenoma detection rates and withdrawal times during colonoscopy: the Third Eye Retroscope study group. Gastrointest Endosc, 2010, 71(3): 542-550.
17.	Waye JD, Heigh RI, Fleischer DE, et al. A retrograde-viewing device improves detection of adenomas in the colon: a prospective efficacy evaluation (with videos). Gastrointest Endosc, 2010, 71(3): 551-556.
18.	Gralnek IM, Siersema PD, Halpern Z, et al. Standard forward-viewing colonoscopy versus full-spectrum endoscopy: an international, multicentre, randomised, tandem colonoscopy trial. Lancet Oncol, 2014, 15(3): 353-360.
19.	Wang P, Xiao X, Glissen Brown JR, et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng, 2018, 2(10): 741-748.
20.	Wang P, Berzin TM, Glissen Brown JR, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut, 2019, 68(10): 1813-1819.
21.	Vinsard DG, Mori Y, Misawa M, et al. Quality assurance of computer-aided detection and diagnosis in colonoscopy. Gastrointest Endosc, 2019, 90(1): 55-63.
22.	Misawa M, Kudo SE, Mori Y, et al. Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology, 2018, 154(8): 2027-2029.
23.	陈肖, 蔡建庭, 陈佳敏, 等. 结肠镜人工智能辅助诊断模型的构建. 中华消化内镜杂志, 2019, 36(4): 251-254.
24.	Barclay RL, Vicari JJ, Doughty AS, et al. Colonoscopic withdrawal times and adenoma detection during screening colonoscopy. N Engl J Med, 2006, 355(24): 2533-2541.
25.	Iacucci M, Fort Gasia M, Hassan C, et al. Complete mucosal healing defined by endoscopic Mayo subscore still demonstrates abnormalities by novel high definition colonoscopy and refined histological gradings. Endoscopy, 2015, 47(8): 726-734.
26.	Maeda Y, Kudo SE, Mori Y, et al. Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis (with video). Gastrointest Endosc, 2019, 89(2): 408-415.
27.	刘书豪, 苏柯帆, 张宪祥, 等. 人工智能影像辅助诊断平台对直肠癌壁外血管侵犯识别多中心临床研究. 中国实用外科杂志, 2019, 39(10): 1081-1084.
28.	周云朋, 李硕, 张宪祥, 等. 基于深度神经网络的高分辨 MRI 直肠淋巴结辅助诊断系统的临床应用价值研究. 中华外科杂志, 2019, 57(2): 108-113.
29.	Lu Y, Yu Q, Gao Y, et al. Identification of metastatic lymph nodes in MR imaging with faster region-based convolutional neural networks. Cancer Res, 2018, 78(17): 5135-5143.
30.	Wang D, Xu J, Zhang Z, et al. Evaluation of rectal cancer circumferential resection margin using faster region-based convolutional neural network in high-resolution magnetic resonance images. Dis Colon Rectum, 2020, 63(2): 143-151.
31.	徐吉华. 利用 Faster R-CNN 对直肠癌高分辨磁共振图像中环周切缘进行评估. 山东: 青岛大学. 2020.
32.	王兰, 张欢, 葛颖倩, 等. 胃癌肝转移病灶的人工智能辅助半自动分割软件的临床应用评估. 诊断学理论与实践, 2019, 18(5): 515-520.
33.	Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol, 2016, 34(18): 2157-2164.
34.	Li Y, Eresen A, Shangguan J, et al. Establishment of a new non-invasive imaging prediction model for liver metastasis in colon cancer. Am J Cancer Res, 2019, 9(11): 2482-2492.
35.	李芊, 周逸菲, 李峥艳, 等. 基于 CT 的直肠癌新辅助化疗后病理完全缓解预测模型的初步探索—DACCA 数据库的联合研究. 中国普外基础与临床杂志, 2020, 27(5): 606-611.
36.	高源, 张宪祥, 李帅. 人工智能技术在结直肠癌诊疗中的应用. 中华胃肠外科杂志, 2020, 23(12): 1155-1158.
37.	卢云, 刘广伟. 人工智能在结直肠癌诊治中应用现状、难点及对策. 中国实用外科杂志, 2020, 40(3): 271-274.
38.	张丹, 薛金萍. 呼吸功能与体能锻炼对肺移植术后患者康复护理的影响分析. 系统医学, 2019, 4(7): 187-189.
39.	Xu Y, Jia Z, Wang LB, et al. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinformatics, 2017, 18(1): 281.
40.	王顺正, 王继刚, 张月娟, 等. 卷积神经网络在胃癌转移淋巴结病理学诊断中的临床应用. 中华外科杂志, 2019, 57(12): 934-938.
41.	Kather JN, Krisam J, Charoentong P, et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Med, 2019, 16(1): e1002730.
42.	Reichling C, Taieb J, Derangere V, et al. Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage Ⅲ colon cancer outcomes in PETACC08 study. Gut, 2020, 69(4): 681-690.
43.	Gupta P, Chiang SF, Sahoo PK, et al. Prediction of colon cancer stages and survival period with machine learning approach. Cancers (Basel), 2019, 11(12): 2007.

1. Wei WQ, Chen ZF, He YT, et al. Long-term follow-up of a community assignment, one-time endoscopic screening study of esophageal cancer in China. J Clin Oncol, 2015, 33(17): 1951-1957.
2. 蔡世伦, 阿依木克地斯·亚力孔, 李染, 等. 基于深度学习的人工智能辅助诊断在食管早癌中的应用. 中华消化内镜杂志, 2019, 36(4): 246-250.
3. Horie Y, Yoshio T, Aoyama K, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc, 2019, 89(1): 25-32.
4. 腾讯发布一个 AI 神器有望攻克食管癌早筛难题. 信息与电脑(理论版), 2017, 15: 5.
5. 石善江, 王宏光, 刘时助. 应用卷积神经网络的人工智能技术在早期食管癌诊断中的临床分析. 中外医疗, 2019, 38(18): 7-9, 16.
6. 郑荣寿, 孙可欣, 张思维, 等. 2015 年中国恶性肿瘤流行情况分析. 中华肿瘤杂志, 2019, 49(1): 19-28.
7. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
8. Hirasawa T, Aoyama K, Tanimoto T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer, 2018, 21(4): 653-660.
9. 李夏, 吴练练, 于红刚. 人工智能胃镜在盲区监测和自主图像采集中的应用研究. 中华消化内镜杂志, 2019, 36(4): 240-245.
10. Ishioka M, Hirasawa T, Tada T. Detecting gastric cancer from video images using convolutional neural networks. Dig Endosc, 2019, 31(2): e34-e35.
11. Itoh T, Kawahira H, Nakashima H, et al. Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images. Endosc Int Open, 2018, 6(2): E139-E144 .
12. Winawer SJ, Zauber AG, Ho MN, et al. Prevention of colorectal cancer by colonoscopic polypectomy. The National Polyp Study Workgroup. N Engl J Med, 1993, 329(27): 1977-1981.
13. Zauber AG, Winawer SJ, O’Brien MJ, et al. Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths. N Engl J Med, 2012, 366(8): 687-696.
14. Yamada M, Saito Y, Imaoka H, et al. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Sci Rep, 2019, 9(1): 14465-14473.
15. Leufkens AM, DeMarco DC, Rastogi A, et al. Effect of a retrograde-viewing device on adenoma detection rate during colonoscopy: the TERRACE study. Gastrointest Endosc, 2011, 73(3): 480-489.
16. DeMarco DC, Odstrcil E, Lara LF, et al. Impact of experience with a retrograde-viewing device on adenoma detection rates and withdrawal times during colonoscopy: the Third Eye Retroscope study group. Gastrointest Endosc, 2010, 71(3): 542-550.
17. Waye JD, Heigh RI, Fleischer DE, et al. A retrograde-viewing device improves detection of adenomas in the colon: a prospective efficacy evaluation (with videos). Gastrointest Endosc, 2010, 71(3): 551-556.
18. Gralnek IM, Siersema PD, Halpern Z, et al. Standard forward-viewing colonoscopy versus full-spectrum endoscopy: an international, multicentre, randomised, tandem colonoscopy trial. Lancet Oncol, 2014, 15(3): 353-360.
19. Wang P, Xiao X, Glissen Brown JR, et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng, 2018, 2(10): 741-748.
20. Wang P, Berzin TM, Glissen Brown JR, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut, 2019, 68(10): 1813-1819.
21. Vinsard DG, Mori Y, Misawa M, et al. Quality assurance of computer-aided detection and diagnosis in colonoscopy. Gastrointest Endosc, 2019, 90(1): 55-63.
22. Misawa M, Kudo SE, Mori Y, et al. Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology, 2018, 154(8): 2027-2029.
23. 陈肖, 蔡建庭, 陈佳敏, 等. 结肠镜人工智能辅助诊断模型的构建. 中华消化内镜杂志, 2019, 36(4): 251-254.
24. Barclay RL, Vicari JJ, Doughty AS, et al. Colonoscopic withdrawal times and adenoma detection during screening colonoscopy. N Engl J Med, 2006, 355(24): 2533-2541.
25. Iacucci M, Fort Gasia M, Hassan C, et al. Complete mucosal healing defined by endoscopic Mayo subscore still demonstrates abnormalities by novel high definition colonoscopy and refined histological gradings. Endoscopy, 2015, 47(8): 726-734.
26. Maeda Y, Kudo SE, Mori Y, et al. Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis (with video). Gastrointest Endosc, 2019, 89(2): 408-415.
27. 刘书豪, 苏柯帆, 张宪祥, 等. 人工智能影像辅助诊断平台对直肠癌壁外血管侵犯识别多中心临床研究. 中国实用外科杂志, 2019, 39(10): 1081-1084.
28. 周云朋, 李硕, 张宪祥, 等. 基于深度神经网络的高分辨 MRI 直肠淋巴结辅助诊断系统的临床应用价值研究. 中华外科杂志, 2019, 57(2): 108-113.
29. Lu Y, Yu Q, Gao Y, et al. Identification of metastatic lymph nodes in MR imaging with faster region-based convolutional neural networks. Cancer Res, 2018, 78(17): 5135-5143.
30. Wang D, Xu J, Zhang Z, et al. Evaluation of rectal cancer circumferential resection margin using faster region-based convolutional neural network in high-resolution magnetic resonance images. Dis Colon Rectum, 2020, 63(2): 143-151.
31. 徐吉华. 利用 Faster R-CNN 对直肠癌高分辨磁共振图像中环周切缘进行评估. 山东: 青岛大学. 2020.
32. 王兰, 张欢, 葛颖倩, 等. 胃癌肝转移病灶的人工智能辅助半自动分割软件的临床应用评估. 诊断学理论与实践, 2019, 18(5): 515-520.
33. Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol, 2016, 34(18): 2157-2164.
34. Li Y, Eresen A, Shangguan J, et al. Establishment of a new non-invasive imaging prediction model for liver metastasis in colon cancer. Am J Cancer Res, 2019, 9(11): 2482-2492.
35. 李芊, 周逸菲, 李峥艳, 等. 基于 CT 的直肠癌新辅助化疗后病理完全缓解预测模型的初步探索—DACCA 数据库的联合研究. 中国普外基础与临床杂志, 2020, 27(5): 606-611.
36. 高源, 张宪祥, 李帅. 人工智能技术在结直肠癌诊疗中的应用. 中华胃肠外科杂志, 2020, 23(12): 1155-1158.
37. 卢云, 刘广伟. 人工智能在结直肠癌诊治中应用现状、难点及对策. 中国实用外科杂志, 2020, 40(3): 271-274.
38. 张丹, 薛金萍. 呼吸功能与体能锻炼对肺移植术后患者康复护理的影响分析. 系统医学, 2019, 4(7): 187-189.
39. Xu Y, Jia Z, Wang LB, et al. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinformatics, 2017, 18(1): 281.
40. 王顺正, 王继刚, 张月娟, 等. 卷积神经网络在胃癌转移淋巴结病理学诊断中的临床应用. 中华外科杂志, 2019, 57(12): 934-938.
41. Kather JN, Krisam J, Charoentong P, et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Med, 2019, 16(1): e1002730.
42. Reichling C, Taieb J, Derangere V, et al. Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage Ⅲ colon cancer outcomes in PETACC08 study. Gut, 2020, 69(4): 681-690.
43. Gupta P, Chiang SF, Sahoo PK, et al. Prediction of colon cancer stages and survival period with machine learning approach. Cancers (Basel), 2019, 11(12): 2007.

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CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Current situation and prospect of artificial intelligence in the diagnosis and treatment of gastrointestinal tumors using image deep learning

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