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For: Luo H, Xu G, Li C, He L, Luo L, Wang Z, Jing B, Deng Y, Jin Y, Li Y, Li B, Tan W, He C, Seeruttun SR, Wu Q, Huang J, Huang DW, Chen B, Lin SB, Chen QM, Yuan CM, Chen HX, Pu HY, Zhou F, He Y, Xu RH. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Lancet Oncol. 2019;20:1645-1654. [PMID: 31591062 DOI: 10.1016/s1470-2045(19)30637-0] [Cited by in Crossref: 92] [Cited by in F6Publishing: 48] [Article Influence: 30.7] [Reference Citation Analysis]
Number Citing Articles
1 Nawab K, Athwani R, Naeem A, Hamayun M, Wazir M. A Review of Applications of Artificial Intelligence in Gastroenterology. Cureus 2021;13:e19235. [PMID: 34877212 DOI: 10.7759/cureus.19235] [Reference Citation Analysis]
2 El-Nakeep S, El-Nakeep M. Artificial intelligence for cancer detection in upper gastrointestinal endoscopy, current status, and future aspirations. Artif Intell Gastroenterol 2021; 2(5): 124-132 [DOI: 10.35712/aig.v2.i5.124] [Reference Citation Analysis]
3 Luo H, Li C, He L, Wang Z, Xu RH. Artificial intelligence applications in upper gastrointestinal cancers - Authors' reply. Lancet Oncol 2020;21:e5. [PMID: 31908307 DOI: 10.1016/S1470-2045(19)30809-5] [Reference Citation Analysis]
4 Li Z, Guo C, Nie D, Lin D, Zhu Y, Chen C, Zhao L, Wu X, Dongye M, Xu F, Jin C, Zhang P, Han Y, Yan P, Lin H. Deep learning from "passive feeding" to "selective eating" of real-world data. NPJ Digit Med 2020;3:143. [PMID: 33145439 DOI: 10.1038/s41746-020-00350-y] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
5 Li J, Li P, Niu W. Artificial intelligence applications in upper gastrointestinal cancers. Lancet Oncol 2020;21:e4. [PMID: 31908305 DOI: 10.1016/S1470-2045(19)30721-1] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
6 Kan J, Li A, Zou H, Chen L, Du J. A Machine Learning Based Dose Prediction of Lutein Supplements for Individuals With Eye Fatigue. Front Nutr 2020;7:577923. [PMID: 33304916 DOI: 10.3389/fnut.2020.577923] [Reference Citation Analysis]
7 Mankoo R, Ali AH, Hammoud GM. Use of artificial intelligence in endoscopic ultrasound evaluation of pancreatic pathologies. Artif Intell Gastrointest Endosc 2021; 2(3): 89-94 [DOI: 10.37126/aige.v2.i3.89] [Reference Citation Analysis]
8 Xue P, Tang C, Li Q, Li Y, Shen Y, Zhao Y, Chen J, Wu J, Li L, Wang W, Li Y, Cui X, Zhang S, Zhang W, Zhang X, Ma K, Zheng Y, Qian T, Ng MTA, Liu Z, Qiao Y, Jiang Y, Zhao F. Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies. BMC Med 2020;18:406. [PMID: 33349257 DOI: 10.1186/s12916-020-01860-y] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
9 Shao L, Liu Z, Feng L, Lou X, Li Z, Zhang XY, Wan X, Zhou X, Sun K, Zhang DF, Wu L, Yang G, Sun YS, Xu R, Fan X, Tian J. Multiparametric MRI and Whole Slide Image-Based Pretreatment Prediction of Pathological Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Multicenter Radiopathomic Study. Ann Surg Oncol 2020;27:4296-306. [PMID: 32729045 DOI: 10.1245/s10434-020-08659-4] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
10 Li Y, Zhou D, Liu TT, Shen XZ. Application of deep learning in image recognition and diagnosis of gastric cancer. Artif Intell Gastrointest Endosc 2021; 2(2): 12-24 [DOI: 10.37126/aige.v2.i2.12] [Reference Citation Analysis]
11 Zhuang H, Bao A, Tan Y, Wang H, Xie Q, Qiu M, Xiong W, Liao F. Application and prospect of artificial intelligence in digestive endoscopy. Expert Rev Gastroenterol Hepatol 2021;:1-11. [PMID: 34937459 DOI: 10.1080/17474124.2022.2020646] [Reference Citation Analysis]
12 Li Q, Liu BR. Application of artificial intelligence-assisted endoscopic detection of early esophageal cancer. Shijie Huaren Xiaohua Zazhi 2021; 29(24): 1389-1395 [DOI: 10.11569/wcjd.v29.i24.1389] [Reference Citation Analysis]
13 Jin HY, Zhang M, Hu B. Techniques to integrate artificial intelligence systems with medical information in gastroenterology. Artif Intell Gastrointest Endosc 2020; 1(1): 19-27 [DOI: 10.37126/aige.v1.i1.19] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
14 Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021;11:1719. [PMID: 34574060 DOI: 10.3390/diagnostics11091719] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
15 Kim JH, Nam SJ, Park SC. Usefulness of artificial intelligence in gastric neoplasms. World J Gastroenterol 2021; 27(24): 3543-3555 [PMID: 34239268 DOI: 10.3748/wjg.v27.i24.3543] [Reference Citation Analysis]
16 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] [Reference Citation Analysis]
17 Miao Y, Zhang H, Su B, Wang J, Quan W, Li Q, Mi D. Construction and validation of an RNA-binding protein-associated prognostic model for colorectal cancer. PeerJ 2021;9:e11219. [PMID: 33868829 DOI: 10.7717/peerj.11219] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
18 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] [Reference Citation Analysis]
19 Chua IS, Gaziel-Yablowitz M, Korach ZT, Kehl KL, Levitan NA, Arriaga YE, Jackson GP, Bates DW, Hassett M. Artificial intelligence in oncology: Path to implementation. Cancer Med 2021;10:4138-49. [PMID: 33960708 DOI: 10.1002/cam4.3935] [Reference Citation Analysis]
20 Liu J, Zheng Q, Mu X, Zuo Y, Xu B, Jin Y, Wang Y, Tian H, Yang Y, Xue Q, Huang Z, Chen L, Gu B, Hou X, Shen L, Guo Y, Li Y. Automated tumor proportion score analysis for PD-L1 (22C3) expression in lung squamous cell carcinoma. Sci Rep 2021;11:15907. [PMID: 34354151 DOI: 10.1038/s41598-021-95372-1] [Reference Citation Analysis]
21 Wu S, Chen X, Pan J, Dong W, Diao X, Zhang R, Zhang Y, Zhang Y, Qian G, Chen H, Lin H, Xu S, Chen Z, Zhou X, Mei H, Wu C, Lv Q, Yuan B, Chen Z, Liao W, Yang X, Chen H, Huang J, Lin T. An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic Study. J Natl Cancer Inst 2021:djab179. [PMID: 34473310 DOI: 10.1093/jnci/djab179] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Kennedy-Metz LR, Mascagni P, Torralba A, Dias RD, Perona P, Shah JA, Padoy N, Zenati MA. Computer Vision in the Operating Room: Opportunities and Caveats. IEEE Trans Med Robot Bionics 2021;3:2-10. [PMID: 33644703 DOI: 10.1109/tmrb.2020.3040002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
23 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: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 García-Peraza-Herrera LC, Everson M, Lovat L, Wang HP, Wang WL, Haidry R, Stoyanov D, Ourselin S, Vercauteren T. Intrapapillary capillary loop classification in magnification endoscopy: open dataset and baseline methodology. Int J Comput Assist Radiol Surg 2020;15:651-9. [PMID: 32166574 DOI: 10.1007/s11548-020-02127-w] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
25 Li Z, Jiang J, Qiang W, Guo L, Liu X, Weng H, Wu S, Zheng Q, Chen W. Comparison of deep learning systems and cornea specialists in detecting corneal diseases from low-quality images. iScience 2021;24:103317. [PMID: 34778732 DOI: 10.1016/j.isci.2021.103317] [Reference Citation Analysis]
26 D'Ugo D, Agnes A, Grieco M, Biondi A, Persiani R. Global updates in the treatment of gastric cancer: a systematic review. Part 2: perioperative management, multimodal therapies, new technologies, standardization of the surgical treatment and educational aspects. Updates Surg 2020;72:355-78. [PMID: 32306277 DOI: 10.1007/s13304-020-00771-0] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
27 Pannala R, Krishnan K, Melson J, Parsi MA, Schulman AR, Sullivan S, Trikudanathan G, Trindade AJ, Watson RR, Maple JT, Lichtenstein DR. Artificial intelligence in gastrointestinal endoscopy. VideoGIE. 2020;5:598-613. [PMID: 33319126 DOI: 10.1016/j.vgie.2020.08.013] [Cited by in Crossref: 4] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
28 Okagawa Y, Abe S, Yamada M, Oda I, Saito Y. Artificial Intelligence in Endoscopy. Dig Dis Sci 2021. [PMID: 34155567 DOI: 10.1007/s10620-021-07086-z] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
29 Hsiao YJ, Wen YC, Lai WY, Lin YY, Yang YP, Chien Y, Yarmishyn AA, Hwang DK, Lin TC, Chang YC, Lin TY, Chang KJ, Chiou SH, Jheng YC. Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer. World J Gastroenterol 2021; 27(22): 2979-2993 [PMID: 34168402 DOI: 10.3748/wjg.v27.i22.2979] [Reference Citation Analysis]
30 Qu JY, Li Z, Su JR, Ma MJ, Xu CQ, Zhang AJ, Liu CX, Yuan HP, Chu YL, Lang CC, Huang LY, Lu L, Li YQ, Zuo XL. Development and Validation of an Automatic Image-Recognition Endoscopic Report Generation System: A Multicenter Study. Clin Transl Gastroenterol. 2020;12:e00282. [PMID: 33395075 DOI: 10.14309/ctg.0000000000000282] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
31 Marlicz W, Ren X, Robertson A, Skonieczna-Żydecka K, Łoniewski I, Dario P, Wang S, Plevris JN, Koulaouzidis A, Ciuti G. Frontiers of Robotic Gastroscopy: A Comprehensive Review of Robotic Gastroscopes and Technologies. Cancers (Basel) 2020;12:E2775. [PMID: 32998213 DOI: 10.3390/cancers12102775] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
32 Ryu JY, Chung HY, Choi KY. Potential role of artificial intelligence in craniofacial surgery. Arch Craniofac Surg 2021;22:223-31. [PMID: 34732033 DOI: 10.7181/acfs.2021.00507] [Reference Citation Analysis]
33 Liu Y. Artificial intelligence-assisted endoscopic detection of esophageal neoplasia in early stage: The next step? World J Gastroenterol 2021; 27(14): 1392-1405 [PMID: 33911463 DOI: 10.3748/wjg.v27.i14.1392] [Reference Citation Analysis]
34 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: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
35 Zhang YH, Guo LJ, Yuan XL, Hu B. Artificial intelligence-assisted esophageal cancer management: Now and future. World J Gastroenterol 2020; 26(35): 5256-5271 [PMID: 32994686 DOI: 10.3748/wjg.v26.i35.5256] [Cited by in CrossRef: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
36 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: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
37 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] [Reference Citation Analysis]
38 Lazăr DC, Avram MF, Faur AC, Romoşan I, Goldiş A. The role of computer-assisted systems for upper-endoscopy quality monitoring and assessment of gastric lesions. Gastroenterol Rep (Oxf) 2021;9:185-204. [PMID: 34316369 DOI: 10.1093/gastro/goab008] [Reference Citation Analysis]
39 Kleppe A, Skrede OJ, De Raedt S, Liestøl K, Kerr DJ, Danielsen HE. Designing deep learning studies in cancer diagnostics. Nat Rev Cancer 2021;21:199-211. [PMID: 33514930 DOI: 10.1038/s41568-020-00327-9] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
40 Tang D, Zhou J, Wang L, Ni M, Chen M, Hassan S, Luo R, Chen X, He X, Zhang L, Ding X, Yu H, Xu G, Zou X. A Novel Model Based on Deep Convolutional Neural Network Improves Diagnostic Accuracy of Intramucosal Gastric Cancer (With Video). Front Oncol 2021;11:622827. [PMID: 33959495 DOI: 10.3389/fonc.2021.622827] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
41 Wang WA, Dong P, Zhang A, Wang WJ, Guo CA, Wang J, Liu HB. Artificial intelligence: A new budding star in gastric cancer. Artif Intell Gastroenterol 2020; 1(4): 60-70 [DOI: 10.35712/aig.v1.i4.60] [Reference Citation Analysis]
42 Jin P, Ji X, Kang W, Li Y, Liu H, Ma F, Ma S, Hu H, Li W, Tian Y. Artificial intelligence in gastric cancer: a systematic review. J Cancer Res Clin Oncol. 2020;146:2339-2350. [PMID: 32613386 DOI: 10.1007/s00432-020-03304-9] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
43 Niu PH, Zhao LL, Wu HL, Zhao DB, Chen YT. Artificial intelligence in gastric cancer: Application and future perspectives. World J Gastroenterol 2020; 26(36): 5408-5419 [PMID: 33024393 DOI: 10.3748/wjg.v26.i36.5408] [Cited by in CrossRef: 12] [Cited by in F6Publishing: 7] [Article Influence: 6.0] [Reference Citation Analysis]
44 Cai YW, Dong FF, Shi YH, Lu LY, Chen C, Lin P, Xue YS, Chen JH, Chen SY, Luo XB. Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging. World J Clin Cases 2021; 9(31): 9376-9385 [PMID: 34877273 DOI: 10.12998/wjcc.v9.i31.9376] [Reference Citation Analysis]
45 Sharma R, Singh D, Gaur P, Joshi D. Intelligent automated drug administration and therapy: future of healthcare. Drug Deliv Transl Res 2021;11:1878-902. [PMID: 33447941 DOI: 10.1007/s13346-020-00876-4] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
46 Yang R, Yan C, Lu S, Li J, Ji J, Yan R, Yuan F, Zhu Z, Yu Y. Tracking cancer lesions on surgical samples of gastric cancer by artificial intelligent algorithms. J Cancer 2021;12:6473-83. [PMID: 34659538 DOI: 10.7150/jca.63879] [Reference Citation Analysis]
47 Tang D, Wang L, Jiang J, Liu Y, Ni M, Fu Y, Guo H, Wang Z, An F, Zhang K, Hu Y, Zhan Q, Xu G, Zou X. A Novel Deep Learning System for Diagnosing Early Esophageal Squamous Cell Carcinoma: A Multicenter Diagnostic Study. Clin Transl Gastroenterol 2021;12:e00393. [PMID: 34346911 DOI: 10.14309/ctg.0000000000000393] [Reference Citation Analysis]
48 Chen ZH, Lin L, Wu CF, Li CF, Xu RH, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond) 2021;41:1100-15. [PMID: 34613667 DOI: 10.1002/cac2.12215] [Reference Citation Analysis]
49 Gulati S, Emmanuel A, Patel M, Williams S, Haji A, Hayee B, Neumann H. Artificial intelligence in luminal endoscopy. Ther Adv Gastrointest Endosc 2020;13:2631774520935220. [PMID: 32637935 DOI: 10.1177/2631774520935220] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
50 Xi Y, Dong W, Qiao L, Han K, Chen W, Wang W. Trends in incidence and mortality of esophageal cancer in Inner Mongolia, 2010-2015. Thorac Cancer 2020;11:2440-8. [PMID: 32716130 DOI: 10.1111/1759-7714.13552] [Reference Citation Analysis]
51 Glissen Brown JR, Berzin TM. Adoption of New Technologies: Artificial Intelligence. Gastrointest Endosc Clin N Am 2021;31:743-58. [PMID: 34538413 DOI: 10.1016/j.giec.2021.05.010] [Reference Citation Analysis]
52 Yang R, Yu Y. Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis. Front Oncol 2021;11:638182. [PMID: 33768000 DOI: 10.3389/fonc.2021.638182] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
53 Sakamoto T, Goto T, Fujiogi M, Kawarai Lefor A. Machine learning in gastrointestinal surgery. Surg Today 2021. [PMID: 34559310 DOI: 10.1007/s00595-021-02380-9] [Reference Citation Analysis]
54 He J, Zhang Z, Yang Y, Ren F, Li J, Zhu S, Ma F, Wu R, Lv Y, He G, Guo B, Chu D. Injectable Self-Healing Adhesive pH-Responsive Hydrogels Accelerate Gastric Hemostasis and Wound Healing. Nanomicro Lett 2021;13:80. [PMID: 34138263 DOI: 10.1007/s40820-020-00585-0] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
55 Shao S, Liu L, Zhao Y, Mu L, Lu Q, Qin J. Application of Machine Learning for Predicting Anastomotic Leakage in Patients with Gastric Adenocarcinoma Who Received Total or Proximal Gastrectomy. J Pers Med 2021;11:748. [PMID: 34442391 DOI: 10.3390/jpm11080748] [Reference Citation Analysis]
56 Wang Q, Yuan L, Ding X, Zhou Z. Prediction and Diagnosis of Venous Thromboembolism Using Artificial Intelligence Approaches: A Systematic Review and Meta-Analysis. Clin Appl Thromb Hemost 2021;27:10760296211021162. [PMID: 34184560 DOI: 10.1177/10760296211021162] [Reference Citation Analysis]
57 Zhou J, Hu N, Huang Z, Song B, Wu C, Zeng F, Wu M. Application of artificial intelligence in gastrointestinal disease: a narrative review. Ann Transl Med 2021;9:1188-1188. [DOI: 10.21037/atm-21-3001] [Reference Citation Analysis]
58 Tan JW, Wang L, Chen Y, Xi W, Ji J, Xu X, Zou LK, Feng JX, Zhang J, Zhang H. Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation. J Cancer. 2020;11:7224-7236. [PMID: 33193886 DOI: 10.7150/jca.46704] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]
59 Lee J, Wallace MB. State of the Art: The impact of artificial intelligence in endoscopy 2020.Curr Gastroenterol Rep. 2021;23:7. [PMID: 33855659 DOI: 10.1007/s11894-021-00810-9] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
60 Jiang K, Jiang X, Pan J, Wen Y, Huang Y, Weng S, Lan S, Nie K, Zheng Z, Ji S, Liu P, Li P, Liu F. Current Evidence and Future Perspective of Accuracy of Artificial Intelligence Application for Early Gastric Cancer Diagnosis With Endoscopy: A Systematic and Meta-Analysis. Front Med (Lausanne) 2021;8:629080. [PMID: 33791323 DOI: 10.3389/fmed.2021.629080] [Reference Citation Analysis]
61 Li LS, Guo XY, Sun K. Recent advances in blood-based and artificial intelligence-enhanced approaches for gastrointestinal cancer diagnosis. World J Gastroenterol 2021; 27(34): 5666-5681 [PMID: 34629793 DOI: 10.3748/wjg.v27.i34.5666] [Reference Citation Analysis]
62 Liu L, Wang Y, Liu X, Han S, Jia L, Meng L, Yang Z, Chen W, Zhang Y, Qiao X. Computer-aided diagnostic system based on deep learning for classifying colposcopy images. Ann Transl Med 2021;9:1045. [PMID: 34422957 DOI: 10.21037/atm-21-885] [Reference Citation Analysis]
63 Cui P, Zhao S, Chen W. Identification of the Vas Deferens in Laparoscopic Inguinal Hernia Repair Surgery Using the Convolutional Neural Network. J Healthc Eng 2021;2021:5578089. [PMID: 34603649 DOI: 10.1155/2021/5578089] [Reference Citation Analysis]
64 Dindorf C, Konradi J, Wolf C, Taetz B, Bleser G, Huthwelker J, Werthmann F, Bartaguiz E, Kniepert J, Drees P, Betz U, Fröhlich M. Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (XAI). Sensors (Basel) 2021;21:6323. [PMID: 34577530 DOI: 10.3390/s21186323] [Reference Citation Analysis]
65 Schröder W, Gisbertz SS, Voeten DM, Gutschow CA, Fuchs HF, van Berge Henegouwen MI. Surgical Therapy of Esophageal Adenocarcinoma-Current Standards and Future Perspectives. Cancers (Basel) 2021;13:5834. [PMID: 34830988 DOI: 10.3390/cancers13225834] [Reference Citation Analysis]