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For: Choi KJ, Jang JK, Lee SS, Sung YS, Shim WH, Kim HS, Yun J, Choi JY, Lee Y, Kang BK, Kim JH, Kim SY, Yu ES. Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent-enhanced CT Images in the Liver. Radiology. 2018;289:688-697. [PMID: 30179104 DOI: 10.1148/radiol.2018180763] [Cited by in Crossref: 71] [Cited by in F6Publishing: 62] [Article Influence: 17.8] [Reference Citation Analysis]
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4 Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit Health 2020;2:e537-48. [PMID: 33328048 DOI: 10.1016/S2589-7500(20)30218-1] [Cited by in Crossref: 15] [Cited by in F6Publishing: 6] [Article Influence: 7.5] [Reference Citation Analysis]
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7 Masuda T, Nakaura T, Funama Y, Sato T, Arataki K, Oku T, Yoshiura T, Masuda S, Gotanda R, Arao K, Imaizumi H, Arao S, Hiratsuka J, Awai K. Enhancement rate of venous phase to portal venous phase computed tomography and its correlation with ultrasound elastography determination of liver fibrosis. Radiography (Lond) 2021:S1078-8174(21)00168-1. [PMID: 34702666 DOI: 10.1016/j.radi.2021.10.008] [Reference Citation Analysis]
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11 Sabottke CF, Spieler BM, Moawad AW, Elsayes KM. Artificial Intelligence in Imaging of Chronic Liver Diseases: Current Update and Future Perspectives. Magn Reson Imaging Clin N Am 2021;29:451-63. [PMID: 34243929 DOI: 10.1016/j.mric.2021.05.011] [Reference Citation Analysis]
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14 Nitsch J, Sack J, Halle MW, Moltz JH, Wall A, Rutherford AE, Kikinis R, Meine H. MRI-based radiomic feature analysis of end-stage liver disease for severity stratification. Int J Comput Assist Radiol Surg 2021;16:457-66. [PMID: 33646521 DOI: 10.1007/s11548-020-02295-9] [Reference Citation Analysis]
15 Draelos RL, Dov D, Mazurowski MA, Lo JY, Henao R, Rubin GD, Carin L. Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes. Med Image Anal 2021;67:101857. [PMID: 33129142 DOI: 10.1016/j.media.2020.101857] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
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17 Li Q, Li JF, Mao XR. Application of artificial intelligence in liver diseases: From diagnosis to treatment. Artif Intell Gastroenterol 2021; 2(5): 133-140 [DOI: 10.35712/aig.v2.i5.133] [Reference Citation Analysis]
18 Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 2020;26:1364-74. [PMID: 32908283 DOI: 10.1038/s41591-020-1034-x] [Cited by in Crossref: 85] [Cited by in F6Publishing: 71] [Article Influence: 42.5] [Reference Citation Analysis]
19 Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit Health 2020;2:e549-60. [PMID: 33328049 DOI: 10.1016/S2589-7500(20)30219-3] [Cited by in Crossref: 22] [Cited by in F6Publishing: 5] [Article Influence: 11.0] [Reference Citation Analysis]
20 Lubner MG, Graffy PM, Said A, Watson R, Zea R, Malecki KM, Pickhardt PJ. Utility of Multiparametric CT for Identification of High-Risk NAFLD. AJR Am J Roentgenol 2021;216:659-68. [PMID: 33474981 DOI: 10.2214/AJR.20.22842] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
21 Wang Y, Shao J, Wang P, Chen L, Ying M, Chai S, Ruan S, Tian W, Cheng Y, Zhang H, Zhang X, Wang X, Ding Y, Liang W, Wu L. Deep Learning Radiomics to Predict Regional Lymph Node Staging for Hilar Cholangiocarcinoma. Front Oncol 2021;11:721460. [PMID: 34765542 DOI: 10.3389/fonc.2021.721460] [Reference Citation Analysis]
22 Kise Y, Ikeda H, Fujii T, Fukuda M, Ariji Y, Fujita H, Katsumata A, Ariji E. Preliminary study on the application of deep learning system to diagnosis of Sjögren's syndrome on CT images. Dentomaxillofac Radiol 2019;48:20190019. [PMID: 31075042 DOI: 10.1259/dmfr.20190019] [Cited by in Crossref: 19] [Cited by in F6Publishing: 13] [Article Influence: 6.3] [Reference Citation Analysis]
23 Choi B, Choi IY, Cha SH, Yeom SK, Chung HH, Lee SH, Cha J, Lee JH. Feasibility of computed tomography texture analysis of hepatic fibrosis using dual-energy spectral detector computed tomography. Jpn J Radiol 2020;38:1179-89. [PMID: 32666182 DOI: 10.1007/s11604-020-01020-5] [Reference Citation Analysis]
24 Lee S, Summers RM. Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen. Radiol Clin North Am 2021;59:987-1002. [PMID: 34689882 DOI: 10.1016/j.rcl.2021.07.001] [Reference Citation Analysis]
25 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] [Reference Citation Analysis]
26 Qu H, Minacapelli CD, Tait C, Gupta K, Bhurwal A, Catalano C, Dafalla R, Metaxas D, Rustgi VK. Training of computational algorithms to predict NAFLD activity score and fibrosis stage from liver histopathology slides. Comput Methods Programs Biomed 2021;207:106153. [PMID: 34020377 DOI: 10.1016/j.cmpb.2021.106153] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
27 Miele L, Zocco MA, Pizzolante F, De Matthaeis N, Ainora ME, Liguori A, Gasbarrini A, Grieco A, Rapaccini G. Use of imaging techniques for non-invasive assessment in the diagnosis and staging of non-alcoholic fatty liver disease. Metabolism 2020;112:154355. [DOI: 10.1016/j.metabol.2020.154355] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
28 Elkilany A, Fehrenbach U, Auer TA, Müller T, Schöning W, Hamm B, Geisel D. A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI. Sci Rep 2021;11:10778. [PMID: 34031487 DOI: 10.1038/s41598-021-90257-9] [Reference Citation Analysis]
29 Cui E, Long W, Wu J, Li Q, Ma C, Lei Y, Lin F. Predicting the stages of liver fibrosis with multiphase CT radiomics based on volumetric features. Abdom Radiol (NY) 2021;46:3866-76. [PMID: 33751193 DOI: 10.1007/s00261-021-03051-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
30 Mazurowski MA. Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce. Journal of the American College of Radiology 2019;16:1077-82. [DOI: 10.1016/j.jacr.2019.01.026] [Cited by in Crossref: 19] [Cited by in F6Publishing: 16] [Article Influence: 6.3] [Reference Citation Analysis]
31 Zhen SH, Cheng M, Tao YB, Wang YF, Juengpanich S, Jiang ZY, Jiang YK, Yan YY, Lu W, Lue JM, Qian JH, Wu ZY, Sun JH, Lin H, Cai XJ. Deep Learning for Accurate Diagnosis of Liver Tumor Based on Magnetic Resonance Imaging and Clinical Data. Front Oncol. 2020;10:680. [PMID: 32547939 DOI: 10.3389/fonc.2020.00680] [Cited by in Crossref: 21] [Cited by in F6Publishing: 15] [Article Influence: 10.5] [Reference Citation Analysis]
32 Soufi M, Otake Y, Hori M, Moriguchi K, Imai Y, Sawai Y, Ota T, Tomiyama N, Sato Y. Liver shape analysis using partial least squares regression-based statistical shape model: application for understanding and staging of liver fibrosis. Int J Comput Assist Radiol Surg 2019;14:2083-93. [PMID: 31705418 DOI: 10.1007/s11548-019-02084-z] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
33 Smith AD. Enter the Era of Quantitative Liver CT. Radiology 2018;289:708-9. [DOI: 10.1148/radiol.2018181847] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
34 Ghadimi M, Habibabadi RR, Hazhirkarzar B, Shaghaghi M, Ameli S, Khoshpouri P, Ghasabeh MA, Gurakar A, Pawlik TM, Kamel IR. Advances in Imaging of Diffuse Parenchymal Liver Disease. J Clin Gastroenterol 2020;54:682-95. [PMID: 32554990 DOI: 10.1097/MCG.0000000000001380] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
35 Yang Q, Guo Y, Ou X, Wang J, Hu C. Automatic T Staging Using Weakly Supervised Deep Learning for Nasopharyngeal Carcinoma on MR Images. J Magn Reson Imaging 2020;52:1074-82. [PMID: 32583578 DOI: 10.1002/jmri.27202] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
36 Lv J, Xu Y, Xu L, Nie L. Quantitative Functional Evaluation of Liver Fibrosis in Mice with Dynamic Contrast-enhanced Photoacoustic Imaging. Radiology 2021;300:89-97. [PMID: 33904773 DOI: 10.1148/radiol.2021204134] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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38 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: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
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41 Kise Y, Shimizu M, Ikeda H, Fujii T, Kuwada C, Nishiyama M, Funakoshi T, Ariji Y, Fujita H, Katsumata A, Yoshiura K, Ariji E. Usefulness of a deep learning system for diagnosing Sjögren's syndrome using ultrasonography images. Dentomaxillofac Radiol 2020;49:20190348. [PMID: 31804146 DOI: 10.1259/dmfr.20190348] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 2.3] [Reference Citation Analysis]
42 Rajpurkar P, Park A, Irvin J, Chute C, Bereket M, Mastrodicasa D, Langlotz CP, Lungren MP, Ng AY, Patel BN. AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining. Sci Rep. 2020;10:3958. [PMID: 32127625 DOI: 10.1038/s41598-020-61055-6] [Cited by in Crossref: 13] [Cited by in F6Publishing: 8] [Article Influence: 6.5] [Reference Citation Analysis]
43 Xiang K, Jiang B, Shang D. The overview of the deep learning integrated into the medical imaging of liver: a review. Hepatol Int 2021;15:868-80. [PMID: 34264509 DOI: 10.1007/s12072-021-10229-z] [Reference Citation Analysis]
44 Kise Y, Kuwada C, Ariji Y, Naitoh M, Ariji E. Preliminary Study on the Diagnostic Performance of a Deep Learning System for Submandibular Gland Inflammation Using Ultrasonography Images. J Clin Med 2021;10:4508. [PMID: 34640523 DOI: 10.3390/jcm10194508] [Reference Citation Analysis]
45 Taouli B, Alves FC. Imaging biomarkers of diffuse liver disease: current status. Abdom Radiol (NY) 2020;45:3381-5. [PMID: 32583139 DOI: 10.1007/s00261-020-02619-y] [Reference Citation Analysis]
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47 Yin Y, Yakar D, Dierckx RAJO, Mouridsen KB, Kwee TC, de Haas RJ. Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model. Eur Radiol 2021. [PMID: 34014382 DOI: 10.1007/s00330-021-08046-x] [Reference Citation Analysis]
48 Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ;  SPIRIT-AI and CONSORT-AI Working Group;  SPIRIT-AI and CONSORT-AI Steering Group;  SPIRIT-AI and CONSORT-AI Consensus Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med. 2020;26:1351-1363. [PMID: 32908284 DOI: 10.1038/s41591-020-1037-7] [Cited by in Crossref: 57] [Cited by in F6Publishing: 50] [Article Influence: 28.5] [Reference Citation Analysis]
49 Sui H, Ma R, Liu L, Gao Y, Zhang W, Mo Z. Detection of Incidental Esophageal Cancers on Chest CT by Deep Learning. Front Oncol 2021;11:700210. [PMID: 34604036 DOI: 10.3389/fonc.2021.700210] [Reference Citation Analysis]
50 Wei J, Jiang H, Gu D, Niu M, Fu F, Han Y, Song B, Tian J. Radiomics in liver diseases: Current progress and future opportunities. Liver Int 2020;40:2050-63. [PMID: 32515148 DOI: 10.1111/liv.14555] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
51 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]
52 Kulkarni NM, Fung A, Kambadakone AR, Yeh BM. Computed Tomography Techniques, Protocols, Advancements, and Future Directions in Liver Diseases. Magn Reson Imaging Clin N Am 2021;29:305-20. [PMID: 34243919 DOI: 10.1016/j.mric.2021.05.002] [Reference Citation Analysis]
53 Xue LY, Jiang ZY, Fu TT, Wang QM, Zhu YL, Dai M, Wang WP, Yu JH, Ding H. Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis.Eur Radiol. 2020;30:2973-2983. [PMID: 31965257 DOI: 10.1007/s00330-019-06595-w] [Cited by in Crossref: 16] [Cited by in F6Publishing: 14] [Article Influence: 8.0] [Reference Citation Analysis]
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55 Zheng R, Wang L, Wang C, Yu X, Chen W, Li Y, Li W, Yan F, Wang H, Li R. Feasibility of automatic detection of small hepatocellular carcinoma (≤2 cm) in cirrhotic liver based on pattern matching and deep learning. Phys Med Biol 2021;66. [PMID: 33780910 DOI: 10.1088/1361-6560/abf2f8] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
56 Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK;  SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ. 2020;370:m3164. [PMID: 32909959 DOI: 10.1136/bmj.m3164] [Cited by in Crossref: 29] [Cited by in F6Publishing: 32] [Article Influence: 14.5] [Reference Citation Analysis]
57 Kang B, Yuan X, Wang H, Qin S, Song X, Yu X, Zhang S, Sun C, Zhou Q, Wei Y, Shi F, Yang S, Wang X. Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors. Front Oncol 2021;11:750875. [PMID: 34631589 DOI: 10.3389/fonc.2021.750875] [Reference Citation Analysis]
58 Li Y, Li L, Weng HL, Liebe R, Ding HG. Computed tomography vs liver stiffness measurement and magnetic resonance imaging in evaluating esophageal varices in cirrhotic patients: A systematic review and meta-analysis. World J Gastroenterol 2020; 26(18): 2247-2267 [PMID: 32476790 DOI: 10.3748/wjg.v26.i18.2247] [Reference Citation Analysis]
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61 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]
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65 Drukker L, Sharma H, Droste R, Alsharid M, Chatelain P, Noble JA, Papageorghiou AT. Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video. Sci Rep 2021;11:14109. [PMID: 34238950 DOI: 10.1038/s41598-021-92829-1] [Reference Citation Analysis]
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