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Cited by in F6Publishing
For: Huang L, Lu X, Huang X, Zou X, Wu L, Zhou Z, Wu D, Tang D, Chen D, Wan X, Zhu Z, Deng T, Shen L, Liu J, Zhu Y, Gong D, Zhong Y, Liu F, Yu H. Intelligent difficulty scoring and assistance system for endoscopic extraction of common bile duct stones based on deep learning: multicenter study. Endoscopy. 2020;. [PMID: 32838430 DOI: 10.1055/a-1244-5698] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
Number Citing Articles
1 Yin M, Lin J, Liu L, Gao J, Xu W, Yu C, Qu S, Liu X, Qian L, Xu C, Zhu J. Development of a Deep Learning Model for Malignant Small Bowel Tumors Survival: A SEER-Based Study. Diagnostics 2022;12:1247. [DOI: 10.3390/diagnostics12051247] [Reference Citation Analysis]
2 Correia FP, Lourenço LC. Artificial intelligence in the endoscopic approach of biliary tract diseases: A current review. Artif Intell Gastrointest Endosc 2022; 3(2): 9-15 [DOI: 10.37126/aige.v3.i2.9] [Reference Citation Analysis]
3 Chang KP, Lin SH, Chu YW. Artificial intelligence in gastrointestinal radiology: A review with special focus on recent development of magnetic resonance and computed tomography. Artif Intell Gastroenterol 2021; 2(2): 27-41 [DOI: 10.35712/aig.v2.i2.27] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Ji X, Yang Z, Ma S, Jia W, Zhao Q, Xu L, Kan Y, Cao Y, Wang Y, Fan B. New common bile duct morphological subtypes: Risk predictors of common bile duct stone recurrence. WJGS 2022;14:236-46. [DOI: 10.4240/wjgs.v14.i3.236] [Reference Citation Analysis]
5 Hann A, Meining A. Artificial Intelligence in Endoscopy. Visc Med 2021;37:471-5. [PMID: 35083312 DOI: 10.1159/000519407] [Reference Citation Analysis]