| [1] |
Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015[J]. CA Cancer J Clin, 2016, 66(2): 115-132.
|
| [2] |
Zauber AG, Winawer SJ, O'Brien MJ, et al. Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths[J]. N Engl J Med, 2012, 366(8): 687-696.
|
| [3] |
Nishihara R, Wu K, Lochhead P, et al. Long-term colorectal-cancer incidence and mortality after lower endoscopy[J]. N Engl J Med, 2013, 369(12): 1095-1105.
|
| [4] |
Pilonis ND, Bugajski M, Wieszczy P, et al. Long-term colorectal cancer incidence and mortality after a single negative screening colonoscopy[J]. Ann Intern Med, 2020, 173(2): 81-91.
|
| [5] |
Zhao S, Wang S, Pan P, et al. Magnitude, risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: a systematic review and meta-analysis[J]. Gastroenterology, 2019, 156(6): 1661-1674.
|
| [6] |
Goyal H, Mann R, Gandhi Z, et al. Scope of artificial intelligence in screening and diagnosis of colorectal cancer[J]. J Clin Med, 2020, 9(10): 3313.
|
| [7] |
Yao L, Zhang L, Liu J, et al. Effect of an artificial intelligence-based quality improvement system on efficacy of a computer-aided detection system in colonoscopy: a four-group parallel study[J]. Endoscopy, 2021, Nov 25.
|
| [8] |
Veitch AM, Uedo N, Yao K, et al. Optimizing early upper gastrointestinal cancer detection at endoscopy[J]. Nat Rev Gastroenterol Hepatol, 2015, 12(11): 660-667.
|
| [9] |
van der Sommen F, Zinger S, Curvers WL, et al. Computer-aided detection of early neoplastic lesions in Barrett's esophagus[J]. Endoscopy, 2016, 48(7): 617-624.
|
| [10] |
Urban G, Tripathi P, Alkayali T, et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy[J]. Gastroenterology, 2018, 155(4): 1069-1078.
|
| [11] |
Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology[J]. World J Gastroenterol, 2019, 25(14): 1666-1683.
|
| [12] |
Ruffle JK, Farmer AD, Aziz Q. Artificial intelligence-assisted gastroenterology- promises and pitfalls[J]. Am J Gastroenterol, 2019, 114(3): 422-428.
|
| [13] |
Ahmad OF, Soares AS, Mazomenos E, et al. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions[J]. Lancet Gastroenterol Hepatol, 2019, 4(1): 71-80.
|
| [14] |
Horie Y, Yoshio T, Aoyama K, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks[J]. Gastrointest Endosc, 2019, 89(1): 25-32.
|
| [15] |
Cai SL, Li B, Tan WM, et al. Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video)[J]. Gastrointest Endosc, 2019, 90(5): 745-753.
|
| [16] |
de Groof AJ, Struyvenberg MR, van der Putten J, et al. Deep-learning system detects neoplasia in patients with barrett's esophagus with higher accuracy than endoscopists in a multistep training and validation study with benchmarking[J]. Gastroenterology, 2020, 158(4): 915-929.
|
| [17] |
Guo L, Xiao X, Wu C, et al. Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos)[J]. Gastrointest Endosc, 2020, 91(1): 41-51.
|
| [18] |
Tokai Y, Yoshio T, Aoyama K, et al. Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma[J]. Esophagus, 2020, 17(3): 250-256.
|
| [19] |
Nakagawa K, Ishihara R, Aoyama K, et al. Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists[J]. Gastrointest Endosc, 2019, 90(3): 407-414.
|
| [20] |
王智杰, 高杰, 孟茜茜, 等. 基于深度学习的人工智能技术在早期胃癌诊断中的应用[J]. 中华消化内镜杂志, 2018, 35(8): 551-556.
|
| [21] |
Kanesaka T, Lee TC, Uedo N, et al. Computer-aided diagnosis for identifying and delineating early gastric cancers in magnifying narrow-band imaging[J]. Gastrointest Endosc, 2018, 87(5): 1339-1344.
|
| [22] |
Shichijo S, Nomura S, Aoyama K, et al. Application of convolutional neural networks in the diagnosis of helicobacter pylori infection based on endoscopic images[J]. E Bio Medicine, 2017, 25: 106-111.
|
| [23] |
Zhou T, Han G, Li BN, et al. Quantitative analysis of patients with celiac disease by video capsule endoscopy: A deep learning method[J]. Comput Biol Med, 2017: 851-856.
|
| [24] |
Dimas G, Spyrou E, Iakovidis DK, et al. Intelligent visual localization of wireless capsule endoscopes enhanced by color information[J]. Comput Biol Med, 2017, 89: 429-440.
|
| [25] |
Froehlich F, Wietlisbach V, Gonvers JJ, et al. Impact of colonic cleansing on quality and diagnostic yield of colonoscopy: the European panel of appropriateness of gastrointestinal endoscopy European multicenter study[J]. Gastrointest Endosc, 2005, 61(3): 378-384.
|
| [26] |
Su JR, Li Z, Shao XJ, et al. Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos)[J]. Gastrointest Endosc, 2020, 91(2): 415-424.
|
| [27] |
Lee JY, Calderwood AH, Karnes W, et al. Artificial intelligence for the assessment of bowel preparation[J]. Gastrointest Endosc, 2022, 95(3): 512-518.
|
| [28] |
Zhou J, Wu L, Wan X, et al. A novel artificial intelligence system for the assessment of bowel preparation (with video)[J]. Gastrointest Endosc, 2020, 91(2): 428-435.
|
| [29] |
Rembacken B, Hassan C, Riemann JF, et al. Quality in screening colonoscopy: position statement of the European Society of Gastrointestinal Endoscopy (ESGE)[J]. Endoscopy, 2012, 44(10): 957-968.
|
| [30] |
Gong D, Wu L, Zhang J, et al. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study[J]. Lancet Gastroenterol Hepatol, 2020, 5(4): 352-361.
|
| [31] |
Haj-Hassan H, Chaddad A, Harkouss Y, et al. Classifications of multispectral colorectal cancer tissues using convolution neural network[J]. J Pathol Inform, 2017, (8): 1.
|
| [32] |
Endoscopic Classification Review Group. Update on the paris classification of superficial neoplastic lesions in the digestive tract[J]. Endoscopy, 2005, 37(6): 570-578.
|
| [33] |
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[J]. Gut, 2019, 68(10): 1813-1819.
|
| [34] |
Wang P, Liu P, Glissen Brown JR, et al. Lower adenoma miss rate of computer-aided detection-assisted colonoscopy vs routine white-light colonoscopy in a prospective tandem study[J]. Gastroenterology, 2020, 159(4): 1252-1261.
|
| [35] |
Repici A, Badalamenti M, Maselli R, et al. Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial[J]. Gastroenterology, 2020, 159(2): 512-520.
|
| [36] |
Zhao SB, Yang W, Wang SL, et al. Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning[J]. World J Gastroenterol, 2021, 27(31): 5232-5246.
|
| [37] |
Yamada M, Saito Y, Imaoka H, et al. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy[J]. Sci Rep, 2019, 9(1): 14465.
|
| [38] |
Tong Y, Lu K, Yang Y, et al. Can natural language processing help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neural network approaches[J]. BMC Med Inform Decis Mak, 2020, 20(1): 248.
|
| [39] |
Takemura Y, Yoshida S, Tanaka S, et al. Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video)[J]. Gastrointest Endosc, 2012, 75(1): 179-185.
|
| [40] |
Takemura Y, Yoshida S, Tanaka S, et al. Quantitative analysis and development of a computer-aided system for identification of regular pit patterns of colorectal lesions[J]. Gastrointest Endosc, 2010, 72(5): 1047-1051.
|
| [41] |
He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine[J]. Nat Med, 2019, 25(1): 30-36.
|