BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M, Danese S, Peyrin-Biroulet L. Application of Artificial Intelligence to Gastroenterology and Hepatology. Gastroenterology 2020;158:76-94.e2. [PMID: 31593701 DOI: 10.1053/j.gastro.2019.08.058] [Cited by in Crossref: 211] [Cited by in F6Publishing: 215] [Article Influence: 70.3] [Reference Citation Analysis]
Number Citing Articles
1 Verma N, Choudhury A, Singh V, Duseja A, Al-Mahtab M, Devarbhavi H, Eapen CE, Goel A, Ning Q, Duan Z, Hamid S, Jafri W, Butt AS, Shukla A, Tan SS, Kim DJ, Hu J, Sood A, Goel O, Midha V, Ghaznian H, Sahu MK, Lee GH, Treeprasertsuk S, Shah S, Lesmana LA, Lesmana RC, Prasad VGM, Sarin SK; APASL ACLF Research Consortium (AARC) for APASL ACLF Working Party. APASL-ACLF Research Consortium-Artificial Intelligence (AARC-AI) model precisely predicts outcomes in acute-on-chronic liver failure patients. Liver Int 2023;43:442-51. [PMID: 35797245 DOI: 10.1111/liv.15361] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Da Rio L, Spadaccini M, Parigi TL, Gabbiadini R, Dal Buono A, Busacca A, Maselli R, Fugazza A, Colombo M, Carrara S, Franchellucci G, Alfarone L, Facciorusso A, Hassan C, Repici A, Armuzzi A. Artificial intelligence and inflammatory bowel disease: Where are we going? World J Gastroenterol 2023; 29(3): 508-520 [PMID: 36688019 DOI: 10.3748/wjg.v29.i3.508] [Reference Citation Analysis]
3 Martins M, Mascarenhas M, Afonso J, Ribeiro T, Cardoso P, Mendes F, Cardoso H, Andrade P, Ferreira J, Macedo G. Deep-Learning and Device-Assisted Enteroscopy: Automatic Panendoscopic Detection of Ulcers and Erosions. Medicina (Kaunas) 2023;59. [PMID: 36676796 DOI: 10.3390/medicina59010172] [Reference Citation Analysis]
4 Parra NS, Ross HM, Khan A, Wu M, Goldberg R, Shah L, Mukhtar S, Beiriger J, Gerber A, Halegoua-demarzio D. Advancements in the Diagnosis of Hepatocellular Carcinoma. IJTM 2023;3:51-65. [DOI: 10.3390/ijtm3010005] [Reference Citation Analysis]
5 Hewson DW, Bedforth NM. Closing the gap: artificial intelligence applied to ultrasound-guided regional anaesthesia. Br J Anaesth 2023:S0007-0912(22)00692-4. [PMID: 36639327 DOI: 10.1016/j.bja.2022.12.005] [Reference Citation Analysis]
6 Tang A, Tian L, Gao K, Liu R, Hu S, Liu J, Xu J, Fu T, Zhang Z, Wang W, Zeng L, Qu W, Dai Y, Hou R, Tang S, Wang X. Contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) MASTER: A novel deep learning-based system in pancreatic mass diagnosis. Cancer Med 2023. [PMID: 36606571 DOI: 10.1002/cam4.5578] [Reference Citation Analysis]
7 Liao J, Li X, Gan Y, Han S, Rong P, Wang W, Li W, Zhou L. Artificial intelligence assists precision medicine in cancer treatment. Front Oncol 2022;12:998222. [PMID: 36686757 DOI: 10.3389/fonc.2022.998222] [Reference Citation Analysis]
8 Kalapala R, Rughwani H, Reddy DN. Artificial Intelligence in Hepatology- Ready for the Primetime. J Clin Exp Hepatol 2023;13:149-61. [PMID: 36647407 DOI: 10.1016/j.jceh.2022.06.009] [Reference Citation Analysis]
9 Eschrich J, Kobus Z, Geisel D, Halskov S, Roßner F, Roderburg C, Mohr R, Tacke F. The Diagnostic Approach towards Combined Hepatocellular-Cholangiocarcinoma-State of the Art and Future Perspectives. Cancers (Basel) 2023;15. [PMID: 36612297 DOI: 10.3390/cancers15010301] [Reference Citation Analysis]
10 Rodrigues T, Keswani R. Endoscopy Training in the Age of Artificial Intelligence: Deep Learning or Artificial Competence? Clin Gastroenterol Hepatol 2023;21:8-10. [PMID: 36113552 DOI: 10.1016/j.cgh.2022.08.013] [Reference Citation Analysis]
11 D'Amico G, Colli A, Malizia G, Casazza G. The potential role of machine learning in modelling advanced chronic liver disease. Dig Liver Dis 2022:S1590-8658(22)00826-X. [PMID: 36586769 DOI: 10.1016/j.dld.2022.12.002] [Reference Citation Analysis]
12 Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3(5): 117-141 [DOI: 10.35712/aig.v3.i5.117] [Reference Citation Analysis]
13 Mahzari A. Artificial intelligence in nonalcoholic fatty liver disease. Egypt Liver Journal 2022;12:69. [DOI: 10.1186/s43066-022-00224-w] [Reference Citation Analysis]
14 Deng Y, Qin HY, Zhou YY, Liu HH, Jiang Y, Liu JP, Bao J. Artificial intelligence applications in pathological diagnosis of gastric cancer. Heliyon 2022;8:e12431. [PMID: 36619448 DOI: 10.1016/j.heliyon.2022.e12431] [Reference Citation Analysis]
15 Dahiya DS, Al-Haddad M, Chandan S, Gangwani MK, Aziz M, Mohan BP, Ramai D, Canakis A, Bapaye J, Sharma N. Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail? J Clin Med 2022;11. [PMID: 36556092 DOI: 10.3390/jcm11247476] [Reference Citation Analysis]
16 Li M, Huang Z, Shan Q, Chen S, Zhang N, Hu H, Wang W. Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps. BMC Gastroenterol 2022;22:517. [DOI: 10.1186/s12876-022-02605-2] [Reference Citation Analysis]
17 Huang J, Zhao C, Zhang X, Zhao Q, Zhang Y, Chen L, Dai G. Hepatitis B virus pathogenesis relevant immunosignals uncovering amino acids utilization related risk factors guide artificial intelligence-based precision medicine. Front Pharmacol 2022;13:1079566. [PMID: 36569318 DOI: 10.3389/fphar.2022.1079566] [Reference Citation Analysis]
18 Shen Q, Chen H. A novel risk classification system based on the eighth edition of TNM frameworks for esophageal adenocarcinoma patients: A deep learning approach. Front Oncol 2022;12:887841. [PMID: 36568200 DOI: 10.3389/fonc.2022.887841] [Reference Citation Analysis]
19 Arcidiacono PG, Santo E. Introduction. Best Pract Res Clin Gastroenterol 2022;60-61:101813. [PMID: 36577538 DOI: 10.1016/j.bpg.2022.101813] [Reference Citation Analysis]
20 Alshagathrh FM, Househ MS. Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review. Bioengineering (Basel) 2022;9. [PMID: 36550954 DOI: 10.3390/bioengineering9120748] [Reference Citation Analysis]
21 Halder S, Yamasaki J, Acharya S, Kou W, Elisha G, Carlson DA, Kahrilas PJ, Pandolfino JE, Patankar NA. Virtual disease landscape using mechanics-informed machine learning: Application to esophageal disorders. Artif Intell Med 2022;134:102435. [PMID: 36462900 DOI: 10.1016/j.artmed.2022.102435] [Reference Citation Analysis]
22 Levy JJ, Navas CM, Chandra JA, Christensen BC, Vaickus LJ, Curley M, Chey WD, Baker JR, Shah ED. Video-Based Deep Learning to Detect Dyssynergic Defecation with 3D High-Definition Anorectal Manometry. Dig Dis Sci 2022. [PMID: 36401758 DOI: 10.1007/s10620-022-07759-3] [Reference Citation Analysis]
23 Chadebecq F, Lovat LB, Stoyanov D. Artificial intelligence and automation in endoscopy and surgery. Nat Rev Gastroenterol Hepatol 2022. [PMID: 36352158 DOI: 10.1038/s41575-022-00701-y] [Reference Citation Analysis]
24 Parkash O, Siddiqui ATS, Jiwani U, Rind F, Padhani ZA, Rizvi A, Hoodbhoy Z, Das JK. Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis. Front Med 2022;9. [DOI: 10.3389/fmed.2022.1018937] [Reference Citation Analysis]
25 Niu X, Hu T, Hong Y, Li X, Shen Y, Wang W. The Role of Praziquantel in the Prevention and Treatment of Fibrosis Associated with Schistosomiasis: A Review. Journal of Tropical Medicine 2022;2022:1-8. [DOI: 10.1155/2022/1413711] [Reference Citation Analysis]
26 Li S, Yu G, Wei R, Wang X, Jiang Z. Development and validation of machine learning based models for predicting distant metastasis in colorectal cancer: a population-level study.. [DOI: 10.21203/rs.3.rs-2125523/v1] [Reference Citation Analysis]
27 Guez I, Focht G, Greer MC, Cytter-kuint R, Pratt L, Castro DA, Turner D, Griffiths AM, Freiman M. Development of a multimodal machine-learning fusion model to non-invasively assess ileal Crohn’s disease endoscopic activity. Computer Methods and Programs in Biomedicine 2022. [DOI: 10.1016/j.cmpb.2022.107207] [Reference Citation Analysis]
28 Con D, Vasudevan A. Real-World Guidance from Artificial Intelligence? Predicting Outcomes of Inflammatory Bowel Disease Using Machine Learning. Dig Dis Sci 2022;67:4604-5. [PMID: 35503484 DOI: 10.1007/s10620-022-07511-x] [Reference Citation Analysis]
29 Geetanjali, Malviya R, Awasthi R, Sharma PK, Kala N, Kumar V, Yadav SK. Applications of Artificial Intelligence, Blockchain, and Internet‐of‐Things in Management of Chronic Disease. Cognitive Intelligence and Big Data in Healthcare 2022. [DOI: 10.1002/9781119771982.ch13] [Reference Citation Analysis]
30 Kantrowitz MG. Medical malpractice and gastrointestinal endoscopy. Curr Opin Gastroenterol 2022;38:467-71. [PMID: 35881965 DOI: 10.1097/MOG.0000000000000863] [Reference Citation Analysis]
31 Uche-Anya E, Anyane-Yeboa A, Berzin TM, Ghassemi M, May FP. Artificial intelligence in gastroenterology and hepatology: how to advance clinical practice while ensuring health equity. Gut 2022;71:1909-15. [PMID: 35688612 DOI: 10.1136/gutjnl-2021-326271] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
32 Yin H, Zhang F, Yang X, Meng X, Miao Y, Noor Hussain MS, Yang L, Li Z. Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis. Front Oncol 2022;12:973999. [DOI: 10.3389/fonc.2022.973999] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
33 Wang L, Zhang L, Jiang B, Zhao K, Zhang Y, Xie X. Clinical application of deep learning and radiomics in hepatic disease imaging: a systematic scoping review. Br J Radiol 2022;95:20211136. [PMID: 35816550 DOI: 10.1259/bjr.20211136] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
34 Zhang L, Shi Z, Lu G, Jin Z. Study design of deep learning based automatic detection of cerebrovascular diseases on medical imaging: a position paper from Chinese Association of Radiologists. Intelligent Medicine 2022. [DOI: 10.1016/j.imed.2022.07.001] [Reference Citation Analysis]
35 Fang YJ, Mukundan A, Tsao YM, Huang CW, Wang HC. Identification of Early Esophageal Cancer by Semantic Segmentation. J Pers Med 2022;12:1204. [PMID: 35893299 DOI: 10.3390/jpm12081204] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
36 Pușcașu CI, Rimbaş M, Mateescu RB, Larghi A, Cauni V. Advances in the Diagnosis of Pancreatic Cystic Lesions. Diagnostics 2022;12:1779. [DOI: 10.3390/diagnostics12081779] [Reference Citation Analysis]
37 Wu H, Ou S, Zhang H, Huang R, Yu S, Zhao M, Tai S. Advances in biomarkers and techniques for pancreatic cancer diagnosis. Cancer Cell Int 2022;22. [DOI: 10.1186/s12935-022-02640-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
38 Rangwani S, Ardeshna DR, Rodgers B, Melnychuk J, Turner R, Culp S, Chao WL, Krishna SG. Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions. Biomimetics (Basel) 2022;7:79. [PMID: 35735595 DOI: 10.3390/biomimetics7020079] [Reference Citation Analysis]
39 Lorenzo-zúñiga V, Bustamante-balén M, Pons-beltrán V, Peña-gil C. Development of knowledge-based clinical decision support system for patients included in colorectal screening program. Gastroenterología y Hepatología (English Edition) 2022;45:419-423. [DOI: 10.1016/j.gastre.2021.05.008] [Reference Citation Analysis]
40 Li YD, Li HZ, Chen SS, Jin CH, Chen M, Cheng M, Ma MJ, Zhang XP, Wang X, Zhou JB, Chen MT, Chen JN, Yu S, Wang TJ, Fang WP, Cao XW, Yu XJ, Du LB, Wang S. Correlation of the detection rate of upper GI cancer with artificial intelligence score: results from a multicenter trial (with video). Gastrointest Endosc 2022;95:1138-1146.e2. [PMID: 34973966 DOI: 10.1016/j.gie.2021.12.019] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
41 Lu Z, Xu Y, Yao L, Zhou W, Gong W, Yang G, Guo M, Zhang B, Huang X, He C, Zhou R, Deng Y, Yu H. Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video). Gastrointest Endosc 2022;95:1186-1194.e3. [PMID: 34919941 DOI: 10.1016/j.gie.2021.11.049] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
42 Wu S, Wang J, Guo Q, Lan H, Zhang J, Wang L, Janne E, Luo X, Wang Q, Song Y, Mathew JL, Xun Y, Yang N, Lee MS, Chen Y. Application of artificial intelligence in clinical diagnosis and treatment: an overview of systematic reviews. Intelligent Medicine 2022;2:88-96. [DOI: 10.1016/j.imed.2021.12.001] [Reference Citation Analysis]
43 Cazacu IM, Saftoiu A, Bhutani MS. Advanced EUS Imaging Techniques. Dig Dis Sci 2022;67:1588-98. [PMID: 35451709 DOI: 10.1007/s10620-022-07486-9] [Reference Citation Analysis]
44 Ghosh NK, Kumar A. Colorectal cancer: Artificial intelligence and its role in surgical decision making. Artif Intell Gastroenterol 2022; 3(2): 36-45 [DOI: 10.35712/aig.v3.i2.36] [Reference Citation Analysis]
45 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]
46 Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14(4): 765-793 [DOI: 10.4251/wjgo.v14.i4.765] [Cited by in CrossRef: 1] [Article Influence: 1.0] [Reference Citation Analysis]
47 Shi N, Lan L, Luo J, Zhu P, Ward TRW, Szatmary P, Sutton R, Huang W, Windsor JA, Zhou X, Xia Q. Predicting the Need for Therapeutic Intervention and Mortality in Acute Pancreatitis: A Two-Center International Study Using Machine Learning. JPM 2022;12:616. [DOI: 10.3390/jpm12040616] [Reference Citation Analysis]
48 Dumoulin FL, Rodriguez-monaco FD, Ebigbo A, Steinbrück I. Artificial Intelligence in the Management of Barrett’s Esophagus and Early Esophageal Adenocarcinoma. Cancers 2022;14:1918. [DOI: 10.3390/cancers14081918] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
49 Hicks SA, Strümke I, Thambawita V, Hammou M, Riegler MA, Halvorsen P, Parasa S. On evaluation metrics for medical applications of artificial intelligence. Sci Rep 2022;12:5979. [PMID: 35395867 DOI: 10.1038/s41598-022-09954-8] [Cited by in Crossref: 8] [Cited by in F6Publishing: 11] [Article Influence: 8.0] [Reference Citation Analysis]
50 Plevris N, Lees CW. Disease Monitoring in Inflammatory Bowel Disease: Evolving Principles and Possibilities. Gastroenterology 2022;162:1456-1475.e1. [PMID: 35101422 DOI: 10.1053/j.gastro.2022.01.024] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
51 He X, Wu L, Dong Z, Gong D, Jiang X, Zhang H, Ai Y, Tong Q, Lv P, Lu B, Wu Q, Yuan J, Xu M, Yu H. Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos). Gastrointest Endosc 2022;95:671-678.e4. [PMID: 34896101 DOI: 10.1016/j.gie.2021.11.040] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
52 Karimian G, Petelos E, Evers SMAA. The ethical issues of the application of artificial intelligence in healthcare: a systematic scoping review. AI Ethics 2022. [DOI: 10.1007/s43681-021-00131-7] [Reference Citation Analysis]
53 Visaggi P, Barberio B, Gregori D, Azzolina D, Martinato M, Hassan C, Sharma P, Savarino E, de Bortoli N. Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases. Aliment Pharmacol Ther 2022;55:528-40. [PMID: 35098562 DOI: 10.1111/apt.16778] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 8.0] [Reference Citation Analysis]
54 Maulahela H, Annisa NG. Current advancements in application of artificial intelligence in clinical decision-making by gastroenterologists in gastrointestinal bleeding. Artif Intell Gastroenterol 2022; 3(1): 13-20 [DOI: 10.35712/aig.v3.i1.13] [Reference Citation Analysis]
55 Li S, Si P, Zhang Z, Zhu J, He X, Zhang N. DFCA-Net: Dual Feature Context Aggregation Network for Bleeding Areas Segmentation in Wireless Capsule Endoscopy Images. J Med Biol Eng . [DOI: 10.1007/s40846-022-00689-5] [Reference Citation Analysis]
56 Thomas LB, Mastorides SM, Viswanadhan NA, Jakey CE, Borkowski AA. Artificial Intelligence: Review of Current and Future Applications in Medicine. Fed Pract 2021;38:527-38. [PMID: 35136337 DOI: 10.12788/fp.0174] [Reference Citation Analysis]
57 Zhang Z, Wu Y, Lin N, Yin S, Meng Z. Monitoring Clinical-Pathological Grading of Hepatocellular Carcinoma Using MicroRNA-Guided Semiconducting Polymer Dots. ACS Appl Mater Interfaces 2022. [PMID: 35112844 DOI: 10.1021/acsami.1c24191] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
58 Donisi L, Ricciardi C, Cesarelli G, Coccia A, Amitrano F, Adamo S, D’addio G. Bidimensional and Tridimensional Poincaré Maps in Cardiology: A Multiclass Machine Learning Study. Electronics 2022;11:448. [DOI: 10.3390/electronics11030448] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
59 Kou W, Carlson DA, Baumann AJ, Donnan EN, Schauer JM, Etemadi M, Pandolfino JE. A multi-stage machine learning model for diagnosis of esophageal manometry. Artif Intell Med 2022;124:102233. [PMID: 35115131 DOI: 10.1016/j.artmed.2021.102233] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
60 Saraiva MM, Ribeiro T, Ferreira JPS, Boas FV, Afonso J, Santos AL, Parente MPL, Jorge RN, Pereira P, Macedo G. Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study. Gastrointest Endosc 2022;95:339-48. [PMID: 34508767 DOI: 10.1016/j.gie.2021.08.027] [Cited by in Crossref: 11] [Cited by in F6Publishing: 6] [Article Influence: 11.0] [Reference Citation Analysis]
61 Ferreira JPS, de Mascarenhas Saraiva MJDQEC, Afonso JPL, Ribeiro TFC, Cardoso HMC, Ribeiro Andrade AP, de Mascarenhas Saraiva MNG, Parente MPL, Natal Jorge R, Lopes SIO, de Macedo GMG. Identification of Ulcers and Erosions by the Novel Pillcam™ Crohn's Capsule Using a Convolutional Neural Network: A Multicentre Pilot Study. J Crohns Colitis 2022;16:169-72. [PMID: 34228113 DOI: 10.1093/ecco-jcc/jjab117] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 8.0] [Reference Citation Analysis]
62 Huang J, Zhao C, Zhang X, Zhao Q, Zhang Y, Chen L, Dai G. A GSVA based gene set synergizing with CD4+T cell bearing harmful factors yield risk signals in HBV related diseases via amalgamation of artificial intelligence.. [DOI: 10.1101/2022.01.19.476726] [Reference Citation Analysis]
63 Liang F, Wang S, Zhang K, Liu TJ, Li JN. Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World J Gastrointest Oncol 2022; 14(1): 124-152 [DOI: 10.4251/wjgo.v14.i1.124] [Cited by in CrossRef: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
64 Alfarone L, Dal Buono A, Craviotto V, Zilli A, Fiorino G, Furfaro F, D'Amico F, Danese S, Allocca M. Cross-Sectional Imaging Instead of Colonoscopy in Inflammatory Bowel Diseases: Lights and Shadows. J Clin Med 2022;11:353. [PMID: 35054047 DOI: 10.3390/jcm11020353] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
65 Falloon KA, Fiocchi C. Current Therapy in Inflammatory Bowel Disease: Why and How We Need to Change? EMJ Innov. [DOI: 10.33590/emjinnov/21-00134] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
66 Gunjan D, Das P. Endoscopic Instruments and Techniques Used by Gastroenterologists: A Primer for Pathologists. Surgical Pathology of the Gastrointestinal System 2022. [DOI: 10.1007/978-981-16-6395-6_3] [Reference Citation Analysis]
67 Davids J, Ashrafian H. AIM and mHealth, Smartphones and Apps. Artificial Intelligence in Medicine 2022. [DOI: 10.1007/978-3-030-64573-1_242] [Reference Citation Analysis]
68 Guo J, Cao W, Nie B, Qin Q. Unsupervised Learning Composite Network to Reduce Training Cost of Deep Learning Model for Colorectal Cancer Diagnosis. IEEE J Transl Eng Health Med 2022. [DOI: 10.1109/jtehm.2022.3224021] [Reference Citation Analysis]
69 Siddiqui MF, Mouna A, Nicolas G, Rahat SAA, Mitalipova A, Emmanuel N, Tashmatova N. Computational Intelligence: A Step Forward in Cancer Biomarker Discovery and Therapeutic Target Prediction. Computational Intelligence in Oncology 2022. [DOI: 10.1007/978-981-16-9221-5_14] [Reference Citation Analysis]
70 Varghese J. Big Data und Künstliche Intelligenz: Chancen und Anforderungen für einen erfolgreichen und nachhaltigen Einsatz im Gesundheitswesen. Digitalstrategie im Krankenhaus 2022. [DOI: 10.1007/978-3-658-36226-3_32] [Reference Citation Analysis]
71 Doguc O, Canbolat ZN, Silahtaroglu G. Recent applications of data mining in medical diagnosis and prediction. Big Data Analytics for Healthcare 2022. [DOI: 10.1016/b978-0-323-91907-4.00006-6] [Reference Citation Analysis]
72 Marmo R, Soncini M, Bucci C, Zullo A; GISED. Comparison of assessment tools in acute upper gastrointestinal bleeding: which one for which decision. Scand J Gastroenterol 2022;57:1-7. [PMID: 34534036 DOI: 10.1080/00365521.2021.1976268] [Reference Citation Analysis]
73 Visaggi P, de Bortoli N, Barberio B, Savarino V, Oleas R, Rosi EM, Marchi S, Ribolsi M, Savarino E. Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases. J Clin Gastroenterol 2022;56:23-35. [PMID: 34739406 DOI: 10.1097/MCG.0000000000001629] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 9.0] [Reference Citation Analysis]
74 Rasche C, Brehmer N. KI-Implementierungsoptionen in dateninflationären Versorgungsnetzen: Von der abstrakten Vision zur konkreten Wertschöpfungstransformation. Künstliche Intelligenz im Gesundheitswesen 2022. [DOI: 10.1007/978-3-658-33597-7_9] [Reference Citation Analysis]
75 Penrice DD, Rattan P, Simonetto DA. Artificial Intelligence and the Future of Gastroenterology and Hepatology. Gastro Hep Advances 2022;1:581-595. [DOI: 10.1016/j.gastha.2022.02.025] [Reference Citation Analysis]
76 Seven G, Silahtaroglu G, Kochan K, Ince AT, Arici DS, Senturk H. Use of Artificial Intelligence in the Prediction of Malignant Potential of Gastric Gastrointestinal Stromal Tumors. Dig Dis Sci 2022;67:273-81. [PMID: 33547537 DOI: 10.1007/s10620-021-06830-9] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
77 Strümke I, Hicks SA, Thambawita V, Jha D, Parasa S, Riegler MA, Halvorsen P. Artificial Intelligence in Gastroenterology. Artificial Intelligence in Medicine 2022. [DOI: 10.1007/978-3-030-58080-3_163-2] [Reference Citation Analysis]
78 Strümke I, Hicks SA, Thambawita V, Jha D, Parasa S, Riegler MA, Halvorsen P. Artificial Intelligence in Gastroenterology. Artificial Intelligence in Medicine 2022. [DOI: 10.1007/978-3-030-64573-1_163] [Reference Citation Analysis]
79 Alshagathrh F. Artificial Intelligence Technologies for Detection and Quantification of Hepatic Steatosis: A Scoping Review (Preprint).. [DOI: 10.2196/preprints.35949] [Reference Citation Analysis]
80 Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, Treeprasertsuk S. Application of artificial intelligence in non-alcoholic fatty liver disease and liver fibrosis: a systematic review and meta-analysis. Therap Adv Gastroenterol 2021;14:17562848211062807. [PMID: 34987607 DOI: 10.1177/17562848211062807] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
81 Jefferies JL, Spencer AK, Lau HA, Nelson MW, Giuliano JD, Zabinski JW, Boussios C, Curhan G, Gliklich RE, Warnock DG. A new approach to identifying patients with elevated risk for Fabry disease using a machine learning algorithm. Orphanet J Rare Dis 2021;16:518. [PMID: 34930374 DOI: 10.1186/s13023-021-02150-3] [Reference Citation Analysis]
82 Levy JJ, Navas CM, Chandra JA, Christensen BC, Vaickus LJ, Curley M, Chey WD, Baker JR, Shah ED. Video-Based Deep Learning to Detect Dyssynergic Defecation with 3D High-Definition Anorectal Manometry.. [DOI: 10.1101/2021.12.11.472233] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
83 Majam M, Phatsoane M, Hanna K, Faul C, Arora L, Makthal S, Kumar A, Jois K, Lalla-Edward ST. Utility of a Machine-Guided Tool for Assessing Risk Behavior Associated With Contracting HIV in Three Sites in South Africa: Protocol for an In-Field Evaluation. JMIR Res Protoc 2021;10:e30304. [PMID: 34860679 DOI: 10.2196/30304] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
84 Livovsky DM, Veikherman D, Golany T, Aides A, Dashinsky V, Rabani N, Ben Shimol D, Blau Y, Katzir L, Shimshoni I, Liu Y, Segol O, Goldin E, Corrado G, Lachter J, Matias Y, Rivlin E, Freedman D. Detection of elusive polyps using a large-scale artificial intelligence system (with videos). Gastrointest Endosc 2021;94:1099-1109.e10. [PMID: 34216598 DOI: 10.1016/j.gie.2021.06.021] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
85 Zhong BY, Tang HH, Wang WS, Shen J, Zhang S, Li WC, Yin Y, Yang J, Liu F, Ni CF, Zhao JB, Zhu XL. Performance of artificial intelligence for prognostic prediction with the albumin-bilirubin and platelet-albumin-bilirubin for cirrhotic patients with acute variceal bleeding undergoing early transjugular intrahepatic portosystemic shunt. Eur J Gastroenterol Hepatol 2021;33:e153-60. [PMID: 33177378 DOI: 10.1097/MEG.0000000000001989] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
86 Konikoff T, Goren I, Yalon M, Tamir S, Avni-Biron I, Yanai H, Dotan I, Ollech JE. Machine learning for selecting patients with Crohn's disease for abdominopelvic computed tomography in the emergency department. Dig Liver Dis 2021;53:1559-64. [PMID: 34253482 DOI: 10.1016/j.dld.2021.06.020] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
87 Mengi M, Malhotra D. Artificial Intelligence Based Techniques for the Detection of Socio-Behavioral Disorders: A Systematic Review. Arch Computat Methods Eng. [DOI: 10.1007/s11831-021-09682-8] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
88 Storås AM, Strümke I, Riegler MA, Grauslund J, Hammer HL, Yazidi A, Halvorsen P, Gundersen KG, Utheim TP, Jackson CJ. Artificial intelligence in dry eye disease. Ocul Surf 2021;23:74-86. [PMID: 34843999 DOI: 10.1016/j.jtos.2021.11.004] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
89 Liu J, Zhang J, Huang H, Wang Y, Zhang Z, Ma Y, He X. A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population. Front Pediatr 2021;9:756095. [PMID: 34820343 DOI: 10.3389/fped.2021.756095] [Reference Citation Analysis]
90 Ogata N, Ohtsuka K, Ogawa M, Maeda Y, Ishida F, Kudo SE. Image-Enhanced Capsule Endoscopy Improves the Identification of Small Intestinal Lesions. Diagnostics (Basel) 2021;11:2122. [PMID: 34829469 DOI: 10.3390/diagnostics11112122] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
91 Fonollà R, van der Zander QEW, Schreuder RM, Subramaniam S, Bhandari P, Masclee AAM, Schoon EJ, van der Sommen F, de With PHN. Automatic image and text-based description for colorectal polyps using BASIC classification. Artif Intell Med 2021;121:102178. [PMID: 34763800 DOI: 10.1016/j.artmed.2021.102178] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
92 Vaz K, Goodwin T, Kemp W, Roberts S, Majeed A. Artificial Intelligence in Hepatology: A Narrative Review. Semin Liver Dis 2021;41:551-6. [PMID: 34327698 DOI: 10.1055/s-0041-1731706] [Reference Citation Analysis]
93 Goyal H, Sherazi SAA, Mann R, Gandhi Z, Perisetti A, Aziz M, Chandan S, Kopel J, Tharian B, Sharma N, Thosani N. Scope of Artificial Intelligence in Gastrointestinal Oncology. Cancers (Basel) 2021;13:5494. [PMID: 34771658 DOI: 10.3390/cancers13215494] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]
94 Chawla S, Schairer J, Kushnir V, Hernandez-Barco YG; ACG FDA-Related Matters Committee. Regulation of Artificial Intelligence-Based Applications in Gastroenterology. Am J Gastroenterol 2021;116:2159-62. [PMID: 34403380 DOI: 10.14309/ajg.0000000000001401] [Reference Citation Analysis]
95 Zhuang H, Zhang J, Liao F. A systematic review on application of deep learning in digestive system image processing. Vis Comput 2021;:1-16. [PMID: 34744231 DOI: 10.1007/s00371-021-02322-z] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
96 Zhou Q, Chen ZH, Cao YH, Peng S. Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review. NPJ Digit Med 2021;4:154. [PMID: 34711955 DOI: 10.1038/s41746-021-00524-2] [Cited by in Crossref: 11] [Cited by in F6Publishing: 15] [Article Influence: 5.5] [Reference Citation Analysis]
97 Li N, Jin SZ. Artificial intelligence and early esophageal cancer. Artif Intell Gastrointest Endosc 2021; 2(5): 198-210 [DOI: 10.37126/aige.v2.i5.198] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
98 Kröner PT, Engels MM, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27(40): 6794-6824 [PMID: 34790008 DOI: 10.3748/wjg.v27.i40.6794] [Cited by in CrossRef: 14] [Cited by in F6Publishing: 14] [Article Influence: 7.0] [Reference Citation Analysis]
99 Gazda J, Drotar P, Drazilova S, Gazda J, Gazda M, Janicko M, Jarcuska P. Artificial Intelligence and Its Application to Minimal Hepatic Encephalopathy Diagnosis. J Pers Med 2021;11:1090. [PMID: 34834442 DOI: 10.3390/jpm11111090] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
100 Zhao B, Sun WQ, Wang L, Hu M. Fusion of Selected Deep CNN and Handcrafted Features for Gastritis Detection from Wireless Capsule Endoscopy Images. 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) 2021. [DOI: 10.1109/cisp-bmei53629.2021.9624380] [Reference Citation Analysis]
101 Benamar S, Bennani Y, Bencherif S, Farahat Z, Souissi N, Ngote N, Megdiche K, Belmekki M. Diabetic Retinopathy Screening and Management in Morocco: Challenges and Possible Solutions. 2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) 2021. [DOI: 10.1109/icds53782.2021.9626731] [Reference Citation Analysis]
102 Con D, van Langenberg DR, Vasudevan A. Deep learning vs conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study. World J Gastroenterol 2021; 27(38): 6476-6488 [PMID: 34720536 DOI: 10.3748/wjg.v27.i38.6476] [Cited by in CrossRef: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
103 Haak HE, Gao X, Maas M, Waktola S, Benson S, Beets-Tan RGH, Beets GL, van Leerdam M, Melenhorst J. The use of deep learning on endoscopic images to assess the response of rectal cancer after chemoradiation. Surg Endosc 2021. [PMID: 34642794 DOI: 10.1007/s00464-021-08685-7] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
104 Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27(37): 6191-6223 [PMID: 34712027 DOI: 10.3748/wjg.v27.i37.6191] [Cited by in CrossRef: 14] [Cited by in F6Publishing: 13] [Article Influence: 7.0] [Reference Citation Analysis]
105 Cummins G. Smart pills for gastrointestinal diagnostics and therapy. Adv Drug Deliv Rev 2021;177:113931. [PMID: 34416311 DOI: 10.1016/j.addr.2021.113931] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
106 Tang R, Lu F, Liu L, Yan Y, Du Q, Zhang B, Zhou T, Fu H. Flexible pressure sensors with microstructures. Nano Select 2021;2:1874-901. [DOI: 10.1002/nano.202100003] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
107 Litvin A, Korenev S, Rumovskaya S, Sartelli M, Baiocchi G, Biffl WL, Coccolini F, Di Saverio S, Kelly MD, Kluger Y, Leppäniemi A, Sugrue M, Catena F. WSES project on decision support systems based on artificial neural networks in emergency surgery. World J Emerg Surg 2021;16:50. [PMID: 34565420 DOI: 10.1186/s13017-021-00394-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
108 Tsai CL, Mukundan A, Chung CS, Chen YH, Wang YK, Chen TH, Tseng YS, Huang CW, Wu IC, Wang HC. Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer. Cancers (Basel) 2021;13:4593. [PMID: 34572819 DOI: 10.3390/cancers13184593] [Cited by in Crossref: 8] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
109 Yeung M, Sala E, Schönlieb CB, Rundo L. Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy. Comput Biol Med 2021;137:104815. [PMID: 34507156 DOI: 10.1016/j.compbiomed.2021.104815] [Cited by in Crossref: 22] [Cited by in F6Publishing: 12] [Article Influence: 11.0] [Reference Citation Analysis]
110 Guez I, Focht G, Greer MC, Cytter-kuint R, Pratt L, Castro DA, Turner D, Griffiths AM, Freiman M. Magnetic Resonance Enterography indices for ileal Crohn’s disease assessment: Comparison between classical and machine-learning-based indices.. [DOI: 10.1101/2021.08.29.21262424] [Reference Citation Analysis]
111 Storås AM, Strümke I, Riegler MA, Grauslund J, Hammer HL, Yazidi A, Halvorsen P, Gundersen KG, Utheim TP, Jackson C. Artificial Intelligence in Dry Eye Disease.. [DOI: 10.1101/2021.09.02.21263021] [Reference Citation Analysis]
112 Skamnelos A, Lazaridis N, Vlachou E, Koukias N, Apostolopoulos P, Murino A, Christodoulou D, Despott EJ. The role of small-bowel endoscopy in inflammatory bowel disease: an updated review on the state-of-the-art in 2021. Ann Gastroenterol 2021;34:599-611. [PMID: 34475730 DOI: 10.20524/aog.2021.0652] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
113 Leenhardt R, Souchaud M, Houist G, Le Mouel JP, Saurin JC, Cholet F, Rahmi G, Leandri C, Histace A, Dray X. A neural network-based algorithm for assessing the cleanliness of small bowel during capsule endoscopy. Endoscopy 2021;53:932-6. [PMID: 33137834 DOI: 10.1055/a-1301-3841] [Cited by in Crossref: 13] [Cited by in F6Publishing: 12] [Article Influence: 6.5] [Reference Citation Analysis]
114 Pecere S, Milluzzo SM, Esposito G, Dilaghi E, Telese A, Eusebi LH. Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases. Diagnostics (Basel) 2021;11:1575. [PMID: 34573917 DOI: 10.3390/diagnostics11091575] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
115 Qin K, Li J, Fang Y, Xu Y, Wu J, Zhang H, Li H, Liu S, Li Q. Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis. Surg Endosc 2021. [PMID: 34426876 DOI: 10.1007/s00464-021-08689-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
116 Lin N, Yu T, Zheng W, Hu H, Xiang L, Ye G, Zhong X, Ye B, Wang R, Deng W, Li J, Wang X, Han F, Zhuang K, Zhang D, Xu H, Ding J, Zhang X, Shen Y, Lin H, Zhang Z, Kim JJ, Liu J, Hu W, Duan H, Si J. Simultaneous Recognition of Atrophic Gastritis and Intestinal Metaplasia on White Light Endoscopic Images Based on Convolutional Neural Networks: A Multicenter Study. Clin Transl Gastroenterol 2021;12:e00385. [PMID: 34342293 DOI: 10.14309/ctg.0000000000000385] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
117 Olveres J, González G, Torres F, Moreno-Tagle JC, Carbajal-Degante E, Valencia-Rodríguez A, Méndez-Sánchez N, Escalante-Ramírez B. What is new in computer vision and artificial intelligence in medical image analysis applications. Quant Imaging Med Surg 2021;11:3830-53. [PMID: 34341753 DOI: 10.21037/qims-20-1151] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
118 Mitteilungen der GARPS. Gastroenterologe 2021;16:401-402. [DOI: 10.1007/s11377-021-00557-9] [Reference Citation Analysis]
119 Kim JM, Kang JG, Kim S, Cheon JH. Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis. J Gastroenterol Hepatol 2021;36:2141-8. [PMID: 33554375 DOI: 10.1111/jgh.15433] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
120 Tian L, Hunt B, Bell MAL, Yi J, Smith JT, Ochoa M, Intes X, Durr NJ. Deep Learning in Biomedical Optics. Lasers Surg Med 2021;53:748-75. [PMID: 34015146 DOI: 10.1002/lsm.23414] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 8.0] [Reference Citation Analysis]
121 Bojarski C, Waldner M, Rath T, Schürmann S, Neurath MF, Atreya R, Siegmund B. Innovative Diagnostic Endoscopy in Inflammatory Bowel Diseases: From High-Definition to Molecular Endoscopy. Front Med (Lausanne) 2021;8:655404. [PMID: 34368180 DOI: 10.3389/fmed.2021.655404] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
122 Chen L, Li DC. Artificial intelligence and inflammatory bowel disease. Shijie Huaren Xiaohua Zazhi 2021; 29(13): 684-689 [DOI: 10.11569/wcjd.v29.i13.684] [Reference Citation Analysis]
123 Carretero C, Carbonnel F, Ferrante M, Knudsen T, Van Lent N, Lobo AJ, Negreanu L, Vojvodic A, Oliva S. Monitoring established Crohn's disease with pan-intestinal video capsule endoscopy in Europe: clinician consultation using the nominal group technique. Curr Med Res Opin 2021;37:1547-54. [PMID: 34132150 DOI: 10.1080/03007995.2021.1940910] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
124 Solitano V, Alfarone L, D'Amico F, Peyrin-Biroulet L, Danese S. IBD goes home: from telemedicine to self-administered advanced therapies. Expert Opin Biol Ther 2021;:1-13. [PMID: 34116611 DOI: 10.1080/14712598.2021.1942833] [Reference Citation Analysis]
125 Lu YF, Lyu B. Current situation and prospect of artificial intelligence application in endoscopic diagnosis of Helicobacter pylori infection. Artif Intell Gastrointest Endosc 2021; 2(3): 50-62 [DOI: 10.37126/aige.v2.i3.50] [Reference Citation Analysis]
126 Tanabe S, Perkins EJ, Ono R, Sasaki H. Artificial intelligence in gastrointestinal diseases. Artif Intell Gastroenterol 2021; 2(3): 69-76 [DOI: 10.35712/aig.v2.i3.69] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
127 Visaggi P, Barberio B, Ghisa M, Ribolsi M, Savarino V, Fassan M, Valmasoni M, Marchi S, de Bortoli N, Savarino E. Modern Diagnosis of Early Esophageal Cancer: From Blood Biomarkers to Advanced Endoscopy and Artificial Intelligence. Cancers (Basel) 2021;13:3162. [PMID: 34202763 DOI: 10.3390/cancers13133162] [Cited by in Crossref: 18] [Cited by in F6Publishing: 19] [Article Influence: 9.0] [Reference Citation Analysis]
128 de Maissin A, Vallée R, Flamant M, Fondain-Bossiere M, Berre CL, Coutrot A, Normand N, Mouchère H, Coudol S, Trang C, Bourreille A. Multi-expert annotation of Crohn's disease images of the small bowel for automatic detection using a convolutional recurrent attention neural network. Endosc Int Open 2021;9:E1136-44. [PMID: 34222640 DOI: 10.1055/a-1468-3964] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
129 Neuberger J, Cain O. The Need for Alternatives to Liver Biopsies: Non-Invasive Analytics and Diagnostics. Hepat Med 2021;13:59-69. [PMID: 34163263 DOI: 10.2147/HMER.S278076] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
130 Yalchin M, Baker AM, Graham TA, Hart A. Predicting Colorectal Cancer Occurrence in IBD. Cancers (Basel) 2021;13:2908. [PMID: 34200768 DOI: 10.3390/cancers13122908] [Cited by in Crossref: 8] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
131 Lorenzo-Zúñiga V, Bustamante-Balén M, Pons-Beltrán V, Peña-Gil C. Development of knowledge-based clinical decision support system for patients included in colorectal screening program. Gastroenterol Hepatol 2021:S0210-5705(21)00194-1. [PMID: 34118316 DOI: 10.1016/j.gastrohep.2021.05.011] [Reference Citation Analysis]
132 Ravi K. Artificial intelligence: finding the intersection of predictive modeling and clinical utility. Gastrointest Endosc 2021;93:1273-5. [PMID: 33691975 DOI: 10.1016/j.gie.2020.12.008] [Reference Citation Analysis]
133 Goldberg D, Mantero A, Newcomb C, Delgado C, Forde KA, Kaplan DE, John B, Nuchovich N, Dominguez B, Emanuel E, Reese PP. Predicting survival after liver transplantation in patients with hepatocellular carcinoma using the LiTES-HCC score. J Hepatol 2021;74:1398-406. [PMID: 33453328 DOI: 10.1016/j.jhep.2020.12.021] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 5.5] [Reference Citation Analysis]
134 Aguilar C, Regensburger AP, Knieling F, Wagner AL, Siebenlist G, Woelfle J, Koehler H, Hoerning A, Jüngert J. Pediatric Buried Bumper Syndrome: Diagnostic Validity of Transabdominal Ultrasound and Artificial Intelligence. Ultraschall Med 2021. [PMID: 34034349 DOI: 10.1055/a-1471-3039] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
135 Iacucci M, Jeffery L, Acharjee A, Nardone OM, Zardo D, Smith SCL, Bazarova A, Cannatelli R, Shivaji UN, Williams J, Gkoutos G, Ghosh S. Ultra-high Magnification Endocytoscopy and Molecular Markers for Defining Endoscopic and Histologic Remission in Ulcerative Colitis-An Exploratory Study to Define Deep Remission. Inflamm Bowel Dis 2021;27:1719-30. [PMID: 34019073 DOI: 10.1093/ibd/izab059] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 3.5] [Reference Citation Analysis]
136 Majam M, Phatsoane M, Hanna K, Faul C, Arora L, Makthal S, Kumar A, Jois K, Lalla-edward ST. Utility of a Machine-Guided Tool for Assessing Risk Behavior Associated With Contracting HIV in Three Sites in South Africa: Protocol for an In-Field Evaluation (Preprint).. [DOI: 10.2196/preprints.30304] [Reference Citation Analysis]
137 Perrod G, Rahmi G, Cellier C. Colorectal cancer screening in Lynch syndrome: Indication, techniques and future perspectives. Dig Endosc 2021;33:520-8. [PMID: 32314431 DOI: 10.1111/den.13702] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
138 Diehl DL. Artificial intelligence applications in EUS: the journey of a thousand miles begins with a single step. Gastrointest Endosc 2021;93:1131-2. [PMID: 33685626 DOI: 10.1016/j.gie.2020.09.034] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
139 Bibault JE, Chang DT, Xing L. Development and validation of a model to predict survival in colorectal cancer using a gradient-boosted machine. Gut 2021;70:884-9. [PMID: 32887732 DOI: 10.1136/gutjnl-2020-321799] [Cited by in Crossref: 14] [Cited by in F6Publishing: 13] [Article Influence: 7.0] [Reference Citation Analysis]
140 Bhandari P, Longcroft-Wheaton G, Libanio D, Pimentel-Nunes P, Albeniz E, Pioche M, Sidhu R, Spada C, Anderloni A, Repici A, Haidry R, Barthet M, Neumann H, Antonelli G, Testoni A, Ponchon T, Siersema PD, Fuccio L, Hassan C, Dinis-Ribeiro M. Revising the European Society of Gastrointestinal Endoscopy (ESGE) research priorities: a research progress update. Endoscopy 2021;53:535-54. [PMID: 33822332 DOI: 10.1055/a-1397-3005] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
141 Handa Y, Nakaji K, Hyogo K, Kawakami M, Yamamoto T, Fujiwara A, Kanda R, Osawa M, Handa O, Matsumoto H, Umegaki E, Shiotani A. Evaluation of Performance in Colon Capsule Endoscopy Reading by Endoscopy Nurses. Can J Gastroenterol Hepatol 2021;2021:8826100. [PMID: 34007836 DOI: 10.1155/2021/8826100] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
142 Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27(16): 1664-1690 [PMID: 33967550 DOI: 10.3748/wjg.v27.i16.1664] [Cited by in CrossRef: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
143 Laoveeravat P, Abhyankar PR, Brenner AR, Gabr MM, Habr FG, Atsawarungruangkit A. Artificial intelligence for pancreatic cancer detection: Recent development and future direction . Artif Intell Gastroenterol 2021; 2(2): 56-68 [DOI: 10.35712/aig.v2.i2.56] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
144 Solitano V, D'Amico F, Allocca M, Fiorino G, Zilli A, Loy L, Gilardi D, Radice S, Correale C, Danese S, Peyrin-Biroulet L, Furfaro F. Rediscovering histology: what is new in endoscopy for inflammatory bowel disease? Therap Adv Gastroenterol 2021;14:17562848211005692. [PMID: 33948114 DOI: 10.1177/17562848211005692] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
145 Hansen US, Landau E, Patel M, Hayee B. Novel artificial intelligence-driven software significantly shortens the time required for annotation in computer vision projects. Endosc Int Open 2021;9:E621-6. [PMID: 33869736 DOI: 10.1055/a-1341-0689] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
146 Hicks SA, Strümke I, Thambawita V, Hammou M, Riegler MA, Halvorsen P, Parasa S. On evaluation metrics for medical applications of artificial intelligence.. [DOI: 10.1101/2021.04.07.21254975] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
147 Mendoza Ladd A, Diehl DL. Artificial intelligence for early detection of pancreatic adenocarcinoma: The future is promising. World J Gastroenterol 2021; 27(13): 1283-1295 [PMID: 33833482 DOI: 10.3748/wjg.v27.i13.1283] [Cited by in CrossRef: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
148 Kochhar GS, Carleton NM, Thakkar S. Assessing perspectives on artificial intelligence applications to gastroenterology. Gastrointest Endosc 2021;93:971-975.e2. [PMID: 33144237 DOI: 10.1016/j.gie.2020.10.029] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
149 Robertson AR, Koulaouzidis A, Rondonotti E, Bruno M, Pennazio M. The Role of Video Capsule Endoscopy in Liver Disease. Gastrointest Endosc Clin N Am 2021;31:363-76. [PMID: 33743931 DOI: 10.1016/j.giec.2020.12.007] [Reference Citation Analysis]
150 Sullivan P, Gupta S, Powers PD, Marya NB. Artificial Intelligence Research and Development for Application in Video Capsule Endoscopy. Gastrointest Endosc Clin N Am 2021;31:387-97. [PMID: 33743933 DOI: 10.1016/j.giec.2020.12.009] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
151 Yen SY, Huang HE, Lien GS, Liu CW, Chu CF, Huang WM, Suk FM. Automatic lumen detection and magnetic alignment control for magnetic-assisted capsule colonoscope system optimization. Sci Rep 2021;11:6460. [PMID: 33742067 DOI: 10.1038/s41598-021-86101-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
152 Choi SJ, Kim ES, Choi K. Prediction of the histology of colorectal neoplasm in white light colonoscopic images using deep learning algorithms. Sci Rep 2021;11:5311. [PMID: 33674628 DOI: 10.1038/s41598-021-84299-2] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
153 Doğan Y, Ridaoui F. Knowledge Discovery Using Clustering Methods in Medical Database: A Case Study for Reflux Disease. Sakarya University Journal of Science 2021. [DOI: 10.16984/saufenbilder.837209] [Reference Citation Analysis]
154 Guleria S, Shah TU, Pulido JV, Fasullo M, Ehsan L, Lippman R, Sali R, Mutha P, Cheng L, Brown DE, Syed S. Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy. Sci Rep 2021;11:5086. [PMID: 33658592 DOI: 10.1038/s41598-021-84510-4] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
155 Limdi JK, Farraye FA. Automated endoscopic assessment in ulcerative colitis: the next frontier. Gastrointest Endosc 2021;93:737-9. [PMID: 33583524 DOI: 10.1016/j.gie.2020.10.032] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
156 Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol 2021;36:569-80. [PMID: 33709606 DOI: 10.1111/jgh.15415] [Cited by in Crossref: 19] [Cited by in F6Publishing: 17] [Article Influence: 9.5] [Reference Citation Analysis]
157 Poon AIF, Sung JJY. Opening the black box of AI-Medicine. J Gastroenterol Hepatol 2021;36:581-4. [PMID: 33709609 DOI: 10.1111/jgh.15384] [Cited by in Crossref: 25] [Cited by in F6Publishing: 23] [Article Influence: 12.5] [Reference Citation Analysis]
158 Lee HW, Sung JJY, Ahn SH. Artificial intelligence in liver disease. J Gastroenterol Hepatol 2021;36:539-42. [PMID: 33709605 DOI: 10.1111/jgh.15409] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
159 Manandhar I, Alimadadi A, Aryal S, Munroe PB, Joe B, Cheng X. Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases. Am J Physiol Gastrointest Liver Physiol 2021;320:G328-37. [PMID: 33439104 DOI: 10.1152/ajpgi.00360.2020] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 7.5] [Reference Citation Analysis]
160 Mascarenhas M, Afonso J, Andrade P, Cardoso H, Macedo G. Artificial intelligence and capsule endoscopy: unravelling the future. Ann Gastroenterol 2021;34:300-9. [PMID: 33948053 DOI: 10.20524/aog.2021.0606] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
161 Sutton RA, Sharma P. Overcoming barriers to implementation of artificial intelligence in gastroenterology. Best Pract Res Clin Gastroenterol 2021;52-53:101732. [PMID: 34172254 DOI: 10.1016/j.bpg.2021.101732] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
162 Goyal H, Mann R, Gandhi Z, Perisetti A, Zhang Z, Sharma N, Saligram S, Inamdar S, Tharian B. Application of artificial intelligence in pancreaticobiliary diseases.Ther Adv Gastrointest Endosc. 2021;14:2631774521993059. [PMID: 33644756 DOI: 10.1177/2631774521993059] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
163 Malik A, Patel P, Ehsan L, Guleria S, Hartka T, Adewole S, Syed S. Ten simple rules for engaging with artificial intelligence in biomedicine. PLoS Comput Biol 2021;17:e1008531. [PMID: 33571194 DOI: 10.1371/journal.pcbi.1008531] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
164 Ferrarese A, Sartori G, Orrù G, Frigo AC, Pelizzaro F, Burra P, Senzolo M. Machine learning in liver transplantation: a tool for some unsolved questions? Transpl Int 2021;34:398-411. [PMID: 33428298 DOI: 10.1111/tri.13818] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
165 Wu J, Chen J, Cai J. Application of Artificial Intelligence in Gastrointestinal Endoscopy. J Clin Gastroenterol 2021;55:110-20. [PMID: 32925304 DOI: 10.1097/MCG.0000000000001423] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
166 Ludwig T, Oukid I, Wong J, Ting S, Huysentruyt K, Roy P, Foussat AC, Vandenplas Y. Machine Learning Supports Automated Digital Image Scoring of Stool Consistency in Diapers. J Pediatr Gastroenterol Nutr 2021;72:255-61. [PMID: 33275399 DOI: 10.1097/MPG.0000000000003007] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
167 Piccialli F, Somma VD, Giampaolo F, Cuomo S, Fortino G. A survey on deep learning in medicine: Why, how and when? Information Fusion 2021;66:111-37. [DOI: 10.1016/j.inffus.2020.09.006] [Cited by in Crossref: 84] [Cited by in F6Publishing: 95] [Article Influence: 42.0] [Reference Citation Analysis]
168 Li J, Qian JM. Artificial intelligence in inflammatory bowel disease: current status and opportunities. Chin Med J (Engl). 2020;133:757-759. [PMID: 32132365 DOI: 10.1097/cm9.0000000000000714] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
169 Wang YK, Syu HY, Chen YH, Chung CS, Tseng YS, Ho SY, Huang CW, Wu IC, Wang HC. Endoscopic Images by a Single-Shot Multibox Detector for the Identification of Early Cancerous Lesions in the Esophagus: A Pilot Study. Cancers (Basel) 2021;13:321. [PMID: 33477274 DOI: 10.3390/cancers13020321] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
170 Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, Treeprasertsuk S. Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis. BMC Gastroenterol 2021;21:10. [PMID: 33407169 DOI: 10.1186/s12876-020-01585-5] [Cited by in Crossref: 19] [Cited by in F6Publishing: 23] [Article Influence: 9.5] [Reference Citation Analysis]
171 Kou W, Carlson DA, Baumann AJ, Donnan E, Luo Y, Pandolfino JE, Etemadi M. A deep-learning-based unsupervised model on esophageal manometry using variational autoencoder. Artif Intell Med 2021;112:102006. [PMID: 33581826 DOI: 10.1016/j.artmed.2020.102006] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 4.5] [Reference Citation Analysis]
172 Jiang Y, Liang X, Wang W, Chen C, Yuan Q, Zhang X, Li N, Chen H, Yu J, Xie Y, Xu Y, Zhou Z, Li G, Li R. Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning. JAMA Netw Open 2021;4:e2032269. [PMID: 33399858 DOI: 10.1001/jamanetworkopen.2020.32269] [Cited by in Crossref: 22] [Cited by in F6Publishing: 22] [Article Influence: 11.0] [Reference Citation Analysis]
173 Trasolini R, Byrne MF. Artificial intelligence and deep learning for small bowel capsule endoscopy. Dig Endosc 2021;33:290-7. [PMID: 33211357 DOI: 10.1111/den.13896] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
174 Catania LJ. AI applications in prevalent diseases and disorders. Foundations of Artificial Intelligence in Healthcare and Bioscience 2021. [DOI: 10.1016/b978-0-12-824477-7.00007-9] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
175 Kothari DJ, Sheth SG. Opportunity Is Knocking: Brainstorming Neural Networks for Management of Acute Pancreatitis. Pancreas 2021;50:e11-3. [PMID: 33370040 DOI: 10.1097/MPA.0000000000001716] [Reference Citation Analysis]
176 Akshintala VS, Khashab MA. Artificial intelligence in pancreaticobiliary endoscopy. J Gastroenterol Hepatol 2021;36:25-30. [PMID: 33448514 DOI: 10.1111/jgh.15343] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
177 Strümke I, Hicks SA, Thambawita V, Jha D, Parasa S, Riegler MA, Halvorsen P. Artificial Intelligence in Medicine. Artificial Intelligence in Medicine 2021. [DOI: 10.1007/978-3-030-58080-3_163-1] [Reference Citation Analysis]
178 Davids J, Ashrafian H. AIM and mHealth, Smartphones and Apps. Artificial Intelligence in Medicine 2021. [DOI: 10.1007/978-3-030-58080-3_242-1] [Reference Citation Analysis]
179 Zhong BY, Yan ZP, Sun JH, Zhang L, Hou ZH, Yang MJ, Zhou GH, Wang WS, Li Z, Huang P, Zhang S, Zhu XL, Ni CF. Prognostic Performance of Albumin-Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib. Front Oncol 2020;10:525461. [PMID: 33392064 DOI: 10.3389/fonc.2020.525461] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 3.3] [Reference Citation Analysis]
180 Zhang W. Application of Attention Model Hybrid Guiding based on Artificial Intelligence in the Course of Intelligent Architecture History. 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) 2020. [DOI: 10.1109/iciss49785.2020.9316091] [Reference Citation Analysis]
181 Ghose S, Datta S, Batta V, Malathy C, M G. Artificial Intelligence based identification of Total Knee Arthroplasty Implants. 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) 2020. [DOI: 10.1109/iciss49785.2020.9315956] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
182 Meher D, Gogoi M, Bharali P, Anirvan P, Singh SP. Artificial Intelligence in Small Bowel Endoscopy: Current Perspectives and Future Directions. Journal of Digestive Endoscopy 2020;11:245-252. [DOI: 10.1055/s-0040-1717824] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
183 Kudou M, Kosuga T, Otsuji E. Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives. Artif Intell Gastroenterol 2020; 1(4): 71-85 [DOI: 10.35712/aig.v1.i4.71] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
184 Marlicz W, Koulaouzidis G, Koulaouzidis A. Artificial Intelligence in Gastroenterology-Walking into the Room of Little Miracles. J Clin Med 2020;9:E3675. [PMID: 33207649 DOI: 10.3390/jcm9113675] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
185 Couper R. Future of paediatric gastroenterology. J Paediatr Child Health 2020;56:1674-1676. [DOI: 10.1111/jpc.15023] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
186 van der Laan JJH, van der Waaij AM, Gabriëls RY, Festen EAM, Dijkstra G, Nagengast WB. Endoscopic imaging in inflammatory bowel disease: current developments and emerging strategies. Expert Rev Gastroenterol Hepatol 2021;15:115-26. [PMID: 33094654 DOI: 10.1080/17474124.2021.1840352] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
187 Atsawarungruangkit A, Elfanagely Y, Asombang AW, Rupawala A, Rich HG. Understanding deep learning in capsule endoscopy: Can artificial intelligence enhance clinical practice? Artif Intell Gastrointest Endosc 2020; 1(2): 33-43 [DOI: 10.37126/aige.v1.i2.33] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
188 Huang LM, Yang WJ, Huang ZY, Tang CW, Li J. Artificial intelligence technique in detection of early esophageal cancer. World J Gastroenterol 2020; 26(39): 5959-5969 [PMID: 33132647 DOI: 10.3748/wjg.v26.i39.5959] [Cited by in CrossRef: 13] [Cited by in F6Publishing: 13] [Article Influence: 4.3] [Reference Citation Analysis]
189 Varghese J. Artificial Intelligence in Medicine: Chances and Challenges for Wide Clinical Adoption. Visc Med 2020;36:443-9. [PMID: 33442551 DOI: 10.1159/000511930] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 6.3] [Reference Citation Analysis]
190 Doğan Y, Ridaoui F. Knowledge Discovery Using Clustering Methods in Medical Database: A Case Study for Reflux Disease. Sakarya University Journal of Science 2020. [DOI: 10.16984/saufenbilder.755121] [Reference Citation Analysis]
191 Jiménez Pérez M, Grande RG. Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review. World J Gastroenterol 2020; 26(37): 5617-5628 [PMID: 33088156 DOI: 10.3748/wjg.v26.i37.5617] [Cited by in CrossRef: 19] [Cited by in F6Publishing: 19] [Article Influence: 6.3] [Reference Citation Analysis]
192 Arista Romeu EJ, Rivera Fernández JD, Roa Tort K, Valor A, Escobedo G, Fabila Bustos DA, Stolik S, de la Rosa JM, Guzmán C. Combined methods of optical spectroscopy and artificial intelligence in the assessment of experimentally induced non-alcoholic fatty liver. Comput Methods Programs Biomed 2021;198:105777. [PMID: 33069975 DOI: 10.1016/j.cmpb.2020.105777] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
193 Lui TKL, Tsui VWM, Leung WK. Accuracy of artificial intelligence-assisted detection of upper GI lesions: a systematic review and meta-analysis. Gastrointest Endosc 2020;92:821-830.e9. [PMID: 32562608 DOI: 10.1016/j.gie.2020.06.034] [Cited by in Crossref: 43] [Cited by in F6Publishing: 36] [Article Influence: 14.3] [Reference Citation Analysis]
194 . UEG Week 2020 Poster Presentations. United European Gastroenterol J 2020;8:144-887. [PMID: 33043826 DOI: 10.1177/2050640620927345] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
195 Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc 2020;92:807-12. [PMID: 32565184 DOI: 10.1016/j.gie.2020.06.040] [Cited by in Crossref: 107] [Cited by in F6Publishing: 102] [Article Influence: 35.7] [Reference Citation Analysis]
196 Formica V, Morelli C, Riondino S, Renzi N, Nitti D, Roselli M. Artificial intelligence for the study of colorectal cancer tissue slides. Artif Intell Gastroenterol 2020; 1(3): 51-59 [DOI: 10.35712/aig.v1.i3.51] [Reference Citation Analysis]
197 Kobrinskii BA, Khavkin AI, Volynets GV. Prospects for the use of artificial intelligence systems in gastroenterology. jour 2020. [DOI: 10.31146/1682-8658-ecg-179-7-109-117] [Reference Citation Analysis]
198 Zhang YH, Guo LJ, Yuan XL, Hu B. Artificial intelligence-assisted esophageal cancer management: Now and future. World J Gastroenterol 2020;26:5256-71. [PMID: 32994686 DOI: 10.3748/wjg.v26.i35.5256] [Cited by in CrossRef: 13] [Cited by in F6Publishing: 14] [Article Influence: 4.3] [Reference Citation Analysis]
199 Hansen US, Landau E, Patel M, Hayee B. Novel artificial intelligence-driven software significantly shortens the time required for annotation in computer vision projects.. [DOI: 10.1101/2020.09.11.20192500] [Reference Citation Analysis]
200 Ardevol A, Alvarado-Tapias E, Garcia-Guix M, Brujats A, Gonzalez L, Hernández-Gea V, Aracil C, Pavel O, Cuyas B, Graupera I, Colomo A, Poca M, Torras X, Concepción M, Villanueva C. Early rebleeding increases mortality of variecal bleeders on secondary prophylaxis with β-blockers and ligation. Dig Liver Dis 2020;52:1017-25. [PMID: 32653417 DOI: 10.1016/j.dld.2020.06.005] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.7] [Reference Citation Analysis]
201 Ponnoprat D, Inkeaw P, Chaijaruwanich J, Traisathit P, Sripan P, Inmutto N, Na Chiangmai W, Pongnikorn D, Chitapanarux I. Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans.Med Biol Eng Comput. 2020;58:2497-2515. [PMID: 32794015 DOI: 10.1007/s11517-020-02229-2] [Cited by in Crossref: 10] [Cited by in F6Publishing: 13] [Article Influence: 3.3] [Reference Citation Analysis]
202 Kernebeck S, Busse TS, Böttcher MD, Weitz J, Ehlers J, Bork U. Impact of mobile health and medical applications on clinical practice in gastroenterology. World J Gastroenterol 2020; 26(29): 4182-4197 [PMID: 32848328 DOI: 10.3748/wjg.v26.i29.4182] [Cited by in CrossRef: 21] [Cited by in F6Publishing: 21] [Article Influence: 7.0] [Reference Citation Analysis]
203 Carleton NM, Thakkar S. How to Approach and Interpret Studies on AI in Gastroenterology. Gastroenterology 2020;159:428-432.e1. [PMID: 32272112 DOI: 10.1053/j.gastro.2020.04.001] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
204 Hwang JH, Jamidar P, Kyanam Kabir Baig KR, Leung FW, Lightdale JR, Maranki JL, Okolo PI 3rd, Swanstrom LL, Chak A. GIE Editorial Board top 10 topics: advances in GI endoscopy in 2019. Gastrointest Endosc 2020;92:241-51. [PMID: 32470427 DOI: 10.1016/j.gie.2020.05.021] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 2.7] [Reference Citation Analysis]
205 Oh DJ, Kim KS, Lim YJ. A New Active Locomotion Capsule Endoscopy under Magnetic Control and Automated Reading Program. Clin Endosc. 2020;53:395-401. [PMID: 32746536 DOI: 10.5946/ce.2020.127] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 3.3] [Reference Citation Analysis]
206 Masuzaki R, Kanda T, Sasaki R, Matsumoto N, Nirei K, Ogawa M, Moriyama M. Application of artificial intelligence in hepatology: Minireview. Artif Intell Gastroenterol 2020; 1(1): 5-11 [DOI: 10.35712/aig.v1.i1.5] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
207 Morreale GC, Sinagra E, Vitello A, Shahini E, Shahini E, Maida M. Emerging artificial intelligence applications in gastroenterology: A review of the literature. Artif Intell Gastrointest Endosc 2020; 1(1): 6-18 [DOI: 10.37126/aige.v1.i1.6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
208 Morreale GC, Sinagra E, Vitello A, Shahini E, Shahini E, Maida M. Emerging artificia intelligence applications in gastroenterology: A review of the literature. AIGE 2020;1:19-27. [DOI: 10.37126/wjem.v1.i1.19] [Reference Citation Analysis]
209 Lazăr DC, Avram MF, Faur AC, Goldiş A, Romoşan I, Tăban S, Cornianu M. The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future. Medicina (Kaunas) 2020;56:E364. [PMID: 32708343 DOI: 10.3390/medicina56070364] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 3.0] [Reference Citation Analysis]
210 Yang YJ. The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements. Clin Endosc. 2020;53:387-394. [PMID: 32668529 DOI: 10.5946/ce.2020.133] [Cited by in Crossref: 18] [Cited by in F6Publishing: 19] [Article Influence: 6.0] [Reference Citation Analysis]
211 Bilal M, Glissen Brown JR, Berzin TM. Using Computer-Aided Polyp Detection During Colonoscopy. Am J Gastroenterol 2020;115:963-6. [PMID: 32618638 DOI: 10.14309/ajg.0000000000000646] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
212 Cheng X, Wu D, Cheng Y, Qiao T, Wang X. New focuses of clinical and translational medicine in 2020. Clin Transl Med 2020;10:17-9. [PMID: 32508045 DOI: 10.1002/ctm2.9] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
213 Kim HS, Peng FB, Gomez Cifuentes JD. Regarding: Shung et al: Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding. Gastroenterology 2020;158:2308-9. [PMID: 32201181 DOI: 10.1053/j.gastro.2020.01.055] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 0.7] [Reference Citation Analysis]
214 Tang S, Lin L, Cheng J, Zhao J, Xuan Q, Shao J, Zhou Y, Zhang Y. The prognostic value of preoperative fibrinogen-to-prealbumin ratio and a novel FFC score in patients with resectable gastric cancer. BMC Cancer 2020;20:382. [PMID: 32375697 DOI: 10.1186/s12885-020-06866-6] [Cited by in Crossref: 9] [Cited by in F6Publishing: 12] [Article Influence: 3.0] [Reference Citation Analysis]
215 Ghoshal UC, Rai S, Kulkarni A, Gupta A. Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence. JGH Open. 2020;4:889-897. [PMID: 33102760 DOI: 10.1002/jgh3.12342] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
216 Mujtaba S, Chawla S, Massaad JF. Diagnosis and Management of Non-Variceal Gastrointestinal Hemorrhage: A Review of Current Guidelines and Future Perspectives. J Clin Med. 2020;9:402. [PMID: 32024301 DOI: 10.3390/jcm9020402] [Cited by in Crossref: 18] [Cited by in F6Publishing: 20] [Article Influence: 6.0] [Reference Citation Analysis]
217 Abdelsalam MM. Effective blood vessels reconstruction methodology for early detection and classification of diabetic retinopathy using OCTA images by artificial neural network. Informatics in Medicine Unlocked 2020;20:100390. [DOI: 10.1016/j.imu.2020.100390] [Cited by in Crossref: 12] [Cited by in F6Publishing: 14] [Article Influence: 4.0] [Reference Citation Analysis]
218 Cienfuegos JA, Pérez-cuadrado Martínez E. Small but great steps. Rev Esp Enferm Dig 2019;112. [DOI: 10.17235/reed.2019.6758/2019] [Reference Citation Analysis]
219 Liu E, Bhutani MS, Sun S. Artificial intelligence: The new wave of innovation in EUS. Endosc Ultrasound 2021;10:79-83. [PMID: 33885005 DOI: 10.4103/EUS-D-21-00052] [Reference Citation Analysis]