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Cited by in CrossRef
For: Zhou LQ, Wang JY, Yu SY, Wu GG, Wei Q, Deng YB, Wu XL, Cui XW, Dietrich CF. Artificial intelligence in medical imaging of the liver. World J Gastroenterol 2019; 25(6): 672-682 [PMID: 30783371 DOI: 10.3748/wjg.v25.i6.672]
URL: https://www.wjgnet.com/1007-9327/full/v25/i6/672.htm
Number Citing Articles
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Sanjeevakumar M. Hatture, Nagaveni Kadakol. Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics2021; : 159 doi: 10.1016/B978-0-12-821633-0.00011-8
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Yu-Meng Lei, Miao Yin, Mei-Hui Yu, Jing Yu, Shu-E Zeng, Wen-Zhi Lv, Jun Li, Hua-Rong Ye, Xin-Wu Cui, Christoph F. Dietrich. Artificial Intelligence in Medical Imaging of the BreastFrontiers in Oncology 2021; 11 doi: 10.3389/fonc.2021.600557
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Xim Bokhimi. Learning the Use of Artificial Intelligence in Heterogeneous CatalysisFrontiers in Chemical Engineering 2021; 3 doi: 10.3389/fceng.2021.740270
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Dan Liu, Fei Liu, Xiaoyan Xie, Liya Su, Ming Liu, Xiaohua Xie, Ming Kuang, Guangliang Huang, Yuqi Wang, Hui Zhou, Kun Wang, Manxia Lin, Jie Tian. Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasoundEuropean Radiology 2020; 30(4): 2365 doi: 10.1007/s00330-019-06553-6
5
Yu Kong, Yueqin Dun, Jiandong Meng, Liang Wang, Wanqiang Zhang, Xinchun Li. Medical Imaging and Computer-Aided DiagnosisLecture Notes in Electrical Engineering 2020; 633: 107 doi: 10.1007/978-981-15-5199-4_11
6
Daniel Vasile Balaban, Mariana Jinga. Digital histology in celiac disease: A practice changerArtificial Intelligence in Gastroenterology 2020; 1(1): 1 doi: 10.35712/wjg.v1.i1.1
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7
Shouqin Jia, Ying Wang, Wuzhang Wang, Qiang Zhang, Xu Zhang. Value of medical imaging artificial intelligence in the diagnosis and treatment of new coronavirus pneumoniaExpert Systems 2021;  doi: 10.1111/exsy.12740
8
Daniel Vasile Balaban, Mariana Jinga. Digital histology in celiac disease: A practice changerArtificial Intelligence in Gastroenterology 2020; 1(1): 1-4 doi: 10.35712/aig.v1.i1.1
9
Wei Liu, Xue Liu, Mei Peng, Gong-Quan Chen, Peng-Hua Liu, Xin-Wu Cui, Fan Jiang, Christoph F Dietrich. Artificial intelligence for hepatitis evaluationWorld Journal of Gastroenterology 2021; 27(34): 5715-5726 doi: 10.3748/wjg.v27.i34.5715
10
Michihiro Kudou, Toshiyuki Kosuga, Eigo Otsuji. Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectivesArtificial Intelligence in Gastroenterology 2020; 1(4): 71-85 doi: 10.35712/aig.v1.i4.71
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Onur Dogan, Sanju Tiwari, M. A. Jabbar, Shankru Guggari. A systematic review on AI/ML approaches against COVID-19 outbreakComplex & Intelligent Systems 2021; 7(5): 2655 doi: 10.1007/s40747-021-00424-8
12
Carl F. Sabottke, Bradley M. Spieler, Ahmed W. Moawad, Khaled M. Elsayes. Artificial Intelligence in Imaging of Chronic Liver DiseasesMagnetic Resonance Imaging Clinics of North America 2021; 29(3): 451 doi: 10.1016/j.mric.2021.05.011
13
Grace Lai‐Hung Wong, Pong‐Chi Yuen, Andy Jinhua Ma, Anthony Wing‐Hung Chan, Howard Ho‐Wai Leung, Vincent Wai‐Sun Wong. Artificial intelligence in prediction of non‐alcoholic fatty liver disease and fibrosisJournal of Gastroenterology and Hepatology 2021; 36(3): 543 doi: 10.1111/jgh.15385
14
Javier Briceño. Artificial intelligence and organ transplantation: challenges and expectationsCurrent Opinion in Organ Transplantation 2020; 25(4): 393 doi: 10.1097/MOT.0000000000000775
15
Tai-Hui Xia, Man Tan, Jing-Hua Li, Jing-Jing Wang, Qing-Qing Wu, De-Xing Kong. Establish a normal fetal lung gestational age grading model and explore the potential value of deep learning algorithms in fetal lung maturity evaluationChinese Medical Journal 2021; 134(15): 1828 doi: 10.1097/CM9.0000000000001547
16
Tommaso Vincenzo Bartolotta, Adele Taibbi, Angelo Randazzo, Cesare Gagliardo. New frontiers in liver ultrasound: From mono to multi parametricityWorld Journal of Gastrointestinal Oncology 2021; 13(10): 1302-1316 doi: 10.4251/wjgo.v13.i10.1302
17
Hyo Jung Park, Bumwoo Park, Seung Soo Lee. Radiomics and Deep Learning: Hepatic ApplicationsKorean Journal of Radiology 2020; 21(4): 387 doi: 10.3348/kjr.2019.0752
18
Yuyao Yuan, Zitong Zhao, Liyan Xue, Guangxi Wang, Huajie Song, Ruifang Pang, Juntuo Zhou, Jianyuan Luo, Yongmei Song, Yuxin Yin. Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learningBritish Journal of Cancer 2021; 125(3): 351 doi: 10.1038/s41416-021-01395-w
19
Anna Castaldo, Davide Raffaele De Lucia, Giuseppe Pontillo, Marco Gatti, Sirio Cocozza, Lorenzo Ugga, Renato Cuocolo. State of the Art in Artificial Intelligence and Radiomics in Hepatocellular CarcinomaDiagnostics 2021; 11(7): 1194 doi: 10.3390/diagnostics11071194
20
Clara Balsano, Anna Alisi, Maurizia R. Brunetto, Pietro Invernizzi, Patrizia Burra, Fabio Piscaglia, Domenico Alvaro, Ferruccio Bonino, Marco Carbone, Francesco Faita, Alessio Gerussi, Marcello Persico, Silvano Junior Santini, Alberto Zanetto. The application of artificial intelligence in hepatology: A systematic reviewDigestive and Liver Disease 2021;  doi: 10.1016/j.dld.2021.06.011
21
Connie Y. Chang, Colleen Buckless, Kaitlyn J. Yeh, Martin Torriani. Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural networkSkeletal Radiology 2021;  doi: 10.1007/s00256-021-03873-x
22
F. Brunelle, P. Brunelle. Intelligence artificielle et imagerie médicale : définition, état des lieux et perspectivesBulletin de l'Académie Nationale de Médecine 2019; 203(8-9): 683 doi: 10.1016/j.banm.2019.06.016
23
Shu-Hui Wang, Xin-Jun Han, Jing Du, Zhen-Chang Wang, Chunwang Yuan, Yinan Chen, Yajing Zhu, Xin Dou, Xiao-Wei Xu, Hui Xu, Zheng-Han Yang. Saliency-based 3D convolutional neural network for categorising common focal liver lesions on multisequence MRIInsights into Imaging 2021; 12(1) doi: 10.1186/s13244-021-01117-z
24
H.C. Stephen Chan, Hanbin Shan, Thamani Dahoun, Horst Vogel, Shuguang Yuan. Advancing Drug Discovery via Artificial IntelligenceTrends in Pharmacological Sciences 2019; 40(8): 592 doi: 10.1016/j.tips.2019.06.004
25
Sunpreet Singh, Gurminder Singh, Chander Prakash, Seeram Ramakrishna, Luciano Lamberti, Catalin I. Pruncu. 3D printed biodegradable composites: An insight into mechanical properties of PLA/chitosan scaffoldPolymer Testing 2020; 89: 106722 doi: 10.1016/j.polymertesting.2020.106722
26
Amit Das, Mary Connell, Shailesh Khetarpal. Digital image analysis of ultrasound images using machine learning to diagnose pediatric nonalcoholic fatty liver diseaseClinical Imaging 2021; 77: 62 doi: 10.1016/j.clinimag.2021.02.038
27
Yunus DOĞAN, Fatma RIDAOUI. Knowledge Discovery Using Clustering Methods in Medical Database: A Case Study for Reflux DiseaseSakarya University Journal of Science 2021;  doi: 10.16984/saufenbilder.837209
28
Yunus DOĞAN, Fatma RIDAOUI. Knowledge Discovery Using Clustering Methods in Medical Database: A Case Study for Reflux DiseaseSakarya University Journal of Science 2020;  doi: 10.16984/saufenbilder.755121
29
Sergio J Sanabria, Jeremy Dahl, Amir Pirmoazen, Aya Kamaya, Ahmed ElKaffas. Learning steatosis staging with two-dimensional Convolutional Neural Networks: comparison of accuracy of clinical B-mode with a co-registered spectrogram representation of RF Data2020 IEEE International Ultrasonics Symposium (IUS) 2020; : 1 doi: 10.1109/IUS46767.2020.9251329
30
Bradley Spieler, Carl Sabottke, Ahmed W. Moawad, Ahmed M. Gabr, Mustafa R. Bashir, Richard Kinh Gian Do, Vahid Yaghmai, Radu Rozenberg, Marielia Gerena, Joseph Yacoub, Khaled M. Elsayes. Artificial intelligence in assessment of hepatocellular carcinoma treatment responseAbdominal Radiology 2021; 46(8): 3660 doi: 10.1007/s00261-021-03056-1
31
Uli Fehrenbach, Siyi Xin, Alexander Hartenstein, Timo Alexander Auer, Franziska Dräger, Konrad Froböse, Henning Jann, Martina Mogl, Holger Amthauer, Dominik Geisel, Timm Denecke, Bertram Wiedenmann, Tobias Penzkofer. Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI—A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-MakingCancers 2021; 13(11): 2726 doi: 10.3390/cancers13112726
32
Nitin Chaubal, Thomas Thomsen, Adnan Kabaalioglu, David Srivastava, Stephanie Simone Rösch, Christoph F. Dietrich. Ultrasound and contrast-enhanced ultrasound (CEUS) in infective liver lesions Zeitschrift für Gastroenterologie 2021;  doi: 10.1055/a-1645-3138
33
Chunfeng Zheng, Lei Chen, Jihua Jian, Juan Li, Zhonghui Gao. Efficacy evaluation of interventional therapy for primary liver cancer using magnetic resonance imaging and CT scanning under deep learning and treatment of vasovagal reflexThe Journal of Supercomputing 2021; 77(7): 7535 doi: 10.1007/s11227-020-03539-w
34
Gopi Battineni, Getu Gamo Sagaro, Nalini Chinatalapudi, Francesco Amenta. Applications of Machine Learning Predictive Models in the Chronic Disease DiagnosisJournal of Personalized Medicine 2020; 10(2): 21 doi: 10.3390/jpm10020021
35
Ryota Masuzaki, Tatsuo Kanda, Reina Sasaki, Naoki Matsumoto, Kazushige Nirei, Masahiro Ogawa, Mitsuhiko Moriyama. Application of artificial intelligence in hepatology: MinireviewArtificial Intelligence in Gastroenterology 2020; 1(1): 5-11 doi: 10.35712/aig.v1.i1.5
36
Bhaswar Ghosh, Soham Choudhuri. Plasmodium Species and Drug Resistance2021;  doi: 10.5772/intechopen.98695
37
Wang, MD Yaoting, Chai, MD Huihui, Ye, MD Ruizhong, Li, MD, PhD Jingzhi, Liu, MD Ji-Bin, Lin Chen, Peng, MD Chengzhong. Point-of-Care Ultrasound: New Concepts and Future TrendsADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY 2021; 5(3): 268 doi: 10.37015/AUDT.2021.210023
38
Michel L. Leite, Lorena S. de Loiola Costa, Victor A. Cunha, Victor Kreniski, Mario de Oliveira Braga Filho, Nicolau B. da Cunha, Fabricio F. Costa. Artificial intelligence and the future of life sciencesDrug Discovery Today 2021; 26(11): 2515 doi: 10.1016/j.drudis.2021.07.002
39
Gavin Sugrue, Ruth M. Conroy, Michael Sugrue. Resources for Optimal Care of Emergency SurgeryHot Topics in Acute Care Surgery and Trauma 2020; : 55 doi: 10.1007/978-3-030-49363-9_7
40
Chi-Chih Wang, Yu-Ching Chiu, Wei-Liang Chen, Tzu-Wei Yang, Ming-Chang Tsai, Ming-Hseng Tseng. A Deep Learning Model for Classification of Endoscopic Gastroesophageal Reflux DiseaseInternational Journal of Environmental Research and Public Health 2021; 18(5): 2428 doi: 10.3390/ijerph18052428
41
Soo Yun Choi, Sunggyun Park, Minchul Kim, Jongchan Park, Ye Ra Choi, Kwang Nam Jin. Evaluation of a deep learning-based computer-aided detection algorithm on chest radiographsMedicine 2021; 100(16): e25663 doi: 10.1097/MD.0000000000025663