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.	蔡世倫, 阿依木克地斯·亞力孔, 李染, 等. 基于深度學習的人工智能輔助診斷在食管早癌中的應用. 中華消化內鏡雜志, 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.	騰訊發布一個 AI 神器有望攻克食管癌早篩難題. 信息與電腦(理論版), 2017, 15: 5.
5.	石善江, 王宏光, 劉時助. 應用卷積神經網絡的人工智能技術在早期食管癌診斷中的臨床分析. 中外醫療, 2019, 38(18): 7-9, 16.
6.	鄭榮壽, 孫可欣, 張思維, 等. 2015 年中國惡性腫瘤流行情況分析. 中華腫瘤雜志, 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, 吳練練, 于紅剛. 人工智能胃鏡在盲區監測和自主圖像采集中的應用研究. 中華消化內鏡雜志, 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.	陳肖, 蔡建庭, 陳佳敏, 等. 結腸鏡人工智能輔助診斷模型的構建. 中華消化內鏡雜志, 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.	劉書豪, 蘇柯帆, 張憲祥, 等. 人工智能影像輔助診斷平臺對直腸癌壁外血管侵犯識別多中心臨床研究. 中國實用外科雜志, 2019, 39(10): 1081-1084.
28.	周云朋, 李碩, 張憲祥, 等. 基于深度神經網絡的高分辨 MRI 直腸淋巴結輔助診斷系統的臨床應用價值研究. 中華外科雜志, 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.	徐吉華. 利用 Faster R-CNN 對直腸癌高分辨磁共振圖像中環周切緣進行評估. 山東: 青島大學. 2020.
32.	王蘭, 張歡, 葛穎倩, 等. 胃癌肝轉移病灶的人工智能輔助半自動分割軟件的臨床應用評估. 診斷學理論與實踐, 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, 李崢艷, 等. 基于 CT 的直腸癌新輔助化療后病理完全緩解預測模型的初步探索—DACCA 數據庫的聯合研究. 中國普外基礎與臨床雜志, 2020, 27(5): 606-611.
36.	高源, 張憲祥, 李帥. 人工智能技術在結直腸癌診療中的應用. 中華胃腸外科雜志, 2020, 23(12): 1155-1158.
37.	盧云, 劉廣偉. 人工智能在結直腸癌診治中應用現狀、難點及對策. 中國實用外科雜志, 2020, 40(3): 271-274.
38.	張丹, 薛金萍. 呼吸功能與體能鍛煉對肺移植術后患者康復護理的影響分析. 系統醫學, 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.	王順正, 王繼剛, 張月娟, 等. 卷積神經網絡在胃癌轉移淋巴結病理學診斷中的臨床應用. 中華外科雜志, 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. 蔡世倫, 阿依木克地斯·亞力孔, 李染, 等. 基于深度學習的人工智能輔助診斷在食管早癌中的應用. 中華消化內鏡雜志, 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. 騰訊發布一個 AI 神器有望攻克食管癌早篩難題. 信息與電腦(理論版), 2017, 15: 5.
5. 石善江, 王宏光, 劉時助. 應用卷積神經網絡的人工智能技術在早期食管癌診斷中的臨床分析. 中外醫療, 2019, 38(18): 7-9, 16.
6. 鄭榮壽, 孫可欣, 張思維, 等. 2015 年中國惡性腫瘤流行情況分析. 中華腫瘤雜志, 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, 吳練練, 于紅剛. 人工智能胃鏡在盲區監測和自主圖像采集中的應用研究. 中華消化內鏡雜志, 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. 陳肖, 蔡建庭, 陳佳敏, 等. 結腸鏡人工智能輔助診斷模型的構建. 中華消化內鏡雜志, 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. 劉書豪, 蘇柯帆, 張憲祥, 等. 人工智能影像輔助診斷平臺對直腸癌壁外血管侵犯識別多中心臨床研究. 中國實用外科雜志, 2019, 39(10): 1081-1084.
28. 周云朋, 李碩, 張憲祥, 等. 基于深度神經網絡的高分辨 MRI 直腸淋巴結輔助診斷系統的臨床應用價值研究. 中華外科雜志, 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. 徐吉華. 利用 Faster R-CNN 對直腸癌高分辨磁共振圖像中環周切緣進行評估. 山東: 青島大學. 2020.
32. 王蘭, 張歡, 葛穎倩, 等. 胃癌肝轉移病灶的人工智能輔助半自動分割軟件的臨床應用評估. 診斷學理論與實踐, 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, 李崢艷, 等. 基于 CT 的直腸癌新輔助化療后病理完全緩解預測模型的初步探索—DACCA 數據庫的聯合研究. 中國普外基礎與臨床雜志, 2020, 27(5): 606-611.
36. 高源, 張憲祥, 李帥. 人工智能技術在結直腸癌診療中的應用. 中華胃腸外科雜志, 2020, 23(12): 1155-1158.
37. 盧云, 劉廣偉. 人工智能在結直腸癌診治中應用現狀、難點及對策. 中國實用外科雜志, 2020, 40(3): 271-274.
38. 張丹, 薛金萍. 呼吸功能與體能鍛煉對肺移植術后患者康復護理的影響分析. 系統醫學, 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. 王順正, 王繼剛, 張月娟, 等. 卷積神經網絡在胃癌轉移淋巴結病理學診斷中的臨床應用. 中華外科雜志, 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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