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Cited by in CrossRef
For: Streba CT, Ionescu M, Gheonea DI, Sandulescu L, Ciurea T, Saftoiu A, Vere CC, Rogoveanu I. Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors. World J Gastroenterol 2012; 18(32): 4427-4434 [PMID: 22969209 DOI: 10.3748/wjg.v18.i32.4427]
URL: https://www.wjgnet.com/1948-5182/full/v18/i32/4427.htm
Number Citing Articles
1
Mădălin Mămuleanu, Cristiana Marinela Urhuț, Larisa Daniela Săndulescu, Constantin Kamal, Ana-Maria Pătrașcu, Alin Gabriel Ionescu, Mircea-Sebastian Șerbănescu, Costin Teodor Streba. Deep Learning Algorithms in the Automatic Segmentation of Liver Lesions in Ultrasound InvestigationsLife 2022; 12(11): 1877 doi: 10.3390/life12111877
2
Benjamin Koh, Pojsakorn Danpanichkul, Meng Wang, Darren Jun Hao Tan, Cheng Han Ng. Application of artificial intelligence in the diagnosis of hepatocellular carcinomaeGastroenterology 2023; 1(2): e100002 doi: 10.1136/egastro-2023-100002
3
Dan Ionuț Gheonea, Costin Teodor Streba, Cristin Constantin Vere, Mircea Șerbănescu, Daniel Pirici, Maria Comănescu, Letiția Adela Maria Streba, Marius Eugen Ciurea, Stelian Mogoantă, Ion Rogoveanu. Diagnosis System for Hepatocellular Carcinoma Based on Fractal Dimension of Morphometric Elements Integrated in an Artificial Neural NetworkBioMed Research International 2014; 2014: 1 doi: 10.1155/2014/239706
4
Xiaoxuan Liu, Livia Faes, Aditya U Kale, Siegfried K Wagner, Dun Jack Fu, Alice Bruynseels, Thushika Mahendiran, Gabriella Moraes, Mohith Shamdas, Christoph Kern, Joseph R Ledsam, Martin K Schmid, Konstantinos Balaskas, Eric J Topol, Lucas M Bachmann, Pearse A Keane, Alastair K Denniston. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysisThe Lancet Digital Health 2019; 1(6): e271 doi: 10.1016/S2589-7500(19)30123-2
5
Thodsawit Tiyarattanachai, Simona Turco, John R. Eisenbrey, Corinne E. Wessner, Alexandra Medellin-Kowalewski, Stephanie Wilson, Andrej Lyshchik, Aya Kamaya, Ahmed El Kaffas. A Comprehensive Motion Compensation Method for In-Plane and Out-of-Plane Motion in Dynamic Contrast-Enhanced Ultrasound of Focal Liver LesionsUltrasound in Medicine & Biology 2022; 48(11): 2217 doi: 10.1016/j.ultrasmedbio.2022.06.007
6
Marcel Vetter, Maximilian J Waldner, Sebastian Zundler, Daniel Klett, Thomas Bocklitz, Markus F Neurath, Werner Adler, Daniel Jesper. Artificial intelligence for the classification of focal liver lesions in ultrasound – a systematic reviewUltraschall in der Medizin - European Journal of Ultrasound 2023; 44(04): 395 doi: 10.1055/a-2066-9372
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Kaizhi Wu, Xi Chen, Mingyue Ding. Deep learning based classification of focal liver lesions with contrast-enhanced ultrasoundOptik 2014; 125(15): 4057 doi: 10.1016/j.ijleo.2014.01.114
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Adriana Toro, Ahmed-Emad Mahfouz, Annalisa Ardiri, Michele Malaguarnera, Giulia Malaguarnera, Francesco Loria, Gaetano Bertino, Isidoro Di Carlo. What is changing in indications and treatment of hepatic hemangiomas. A reviewAnnals of Hepatology 2014; 13(4): 327 doi: 10.1016/S1665-2681(19)30839-7
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Qi Lang, Chongli Zhong, Zhiyun Liang, Yizhou Zhang, Baokang Wu, Feng Xu, Ling Cong, Shuodong Wu, Yu Tian. Six application scenarios of artificial intelligence in the precise diagnosis and treatment of liver cancerArtificial Intelligence Review 2021; 54(7): 5307 doi: 10.1007/s10462-021-10023-1
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Xiaomin Shen, Jinxin Wu, Junwei Su, Zhenyu Yao, Wei Huang, Li Zhang, Yiheng Jiang, Wei Yu, Zhao Li. Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH frameworkFrontiers in Genetics 2023; 14 doi: 10.3389/fgene.2023.1004481
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Cristina Laura Sîrbu, Georgiana Simion, Cătălin Daniel Căleanu. Improving the Diagnostic of Contrast Enhanced Ultrasound Imaging using Optical Flow for Focal Liver Lesion Detection2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) 2022; : 258 doi: 10.1109/SYNASC57785.2022.00048
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Joseph C. Ahn, Vijay H. Shah. Artificial Intelligence in Clinical Practice2024; : 443 doi: 10.1016/B978-0-443-15688-5.00016-4
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Rayyan Azam Khan, Yigang Luo, Fang-Xiang Wu. Machine learning based liver disease diagnosis: A systematic reviewNeurocomputing 2022; 468: 492 doi: 10.1016/j.neucom.2021.08.138
14
Jia Hu, Zhi-Yu Zhou, Hong-Ling Ran, Xin-Chun Yuan, Xi Zeng, Zhe-Yuan Zhang. Diagnosis of liver tumors by multimodal ultrasound imagingMedicine 2020; 99(32): e21652 doi: 10.1097/MD.0000000000021652
15
Thodsawit Tiyarattanachai, Terapap Apiparakoon, Sanparith Marukatat, Sasima Sukcharoen, Sirinda Yimsawad, Oracha Chaichuen, Siwat Bhumiwat, Natthaporn Tanpowpong, Nutcha Pinjaroen, Rungsun Rerknimitr, Roongruedee Chaiteerakij. The feasibility to use artificial intelligence to aid detecting focal liver lesions in real-time ultrasound: a preliminary study based on videosScientific Reports 2022; 12(1) doi: 10.1038/s41598-022-11506-z
16
Yi-Shu Chen, Dan Chen, Chao Shen, Ming Chen, Chao-Hui Jin, Cheng-Fu Xu, Chao-Hui Yu, You-Ming Li. A novel model for predicting fatty liver disease by means of an artificial neural networkGastroenterology Report 2021; 9(1): 31 doi: 10.1093/gastro/goaa035
17
Mădălin Mămuleanu, Cristiana Urhuț, Larisa Săndulescu, Constantin Kamal, Ana-Maria Pătrașcu, Alin Ionescu, Mircea-Sebastian Șerbănescu, Costin Streba. An Automated Method for Classifying Liver Lesions in Contrast-Enhanced Ultrasound Imaging Based on Deep Learning AlgorithmsDiagnostics 2023; 13(6): 1062 doi: 10.3390/diagnostics13061062
18
Liu-Liu Cao, Mei Peng, Xiang Xie, Gong-Quan Chen, Shu-Yan Huang, Jia-Yu Wang, Fan Jiang, Xin-Wu Cui, Christoph F Dietrich. Artificial intelligence in liver ultrasoundWorld Journal of Gastroenterology 2022; 28(27): 3398-3409 doi: 10.3748/wjg.v28.i27.3398
19
Marinela-Cristiana Urhuț, Larisa Daniela Săndulescu, Costin Teodor Streba, Mădălin Mămuleanu, Adriana Ciocâlteu, Sergiu Marian Cazacu, Suzana Dănoiu. Diagnostic Performance of an Artificial Intelligence Model Based on Contrast-Enhanced Ultrasound in Patients with Liver Lesions: A Comparative Study with CliniciansDiagnostics 2023; 13(21): 3387 doi: 10.3390/diagnostics13213387
20
Alessandro Martinino, Mohammad Aloulou, Surobhi Chatterjee, Juan Pablo Scarano Pereira, Saurabh Singhal, Tapan Patel, Thomas Paul-Emile Kirchgesner, Salvatore Agnes, Salvatore Annunziata, Giorgio Treglia, Francesco Giovinazzo. Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic ReviewJournal of Clinical Medicine 2022; 11(21): 6368 doi: 10.3390/jcm11216368
21
Catalin Daniel Caleanu, Georgiana Simion. A Bag of Features Approach for CEUS Liver Lesions Investigation2019 42nd International Conference on Telecommunications and Signal Processing (TSP) 2019; : 323 doi: 10.1109/TSP.2019.8768851
22
Naoshi Nishida, Masatoshi Kudo. Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver TumorFrontiers in Oncology 2020; 10 doi: 10.3389/fonc.2020.594580
23
What the radiologist should know about artificial intelligence – an ESR white paperInsights into Imaging 2019; 10(1) doi: 10.1186/s13244-019-0738-2
24
Vagner Mendonça Gonçalves, Márcio Eduardo Delamaro, Fátima de Lourdes dos Santos Nunes. A systematic review on the evaluation and characteristics of computer-aided diagnosis systemsRevista Brasileira de Engenharia Biomédica 2014; 30(4): 355 doi: 10.1590/1517-3151.0517
25
Menglin Wu, Liang Li, Jiahui Wang, Yanyan Zhang, Qi Guo, Xue Li, Xuening Zhang. Contrast-enhanced US for characterization of focal liver lesions: a comprehensive meta-analysisEuropean Radiology 2018; 28(5): 2077 doi: 10.1007/s00330-017-5152-x
26
Ji-Qiao Liu, Jia-Yu Ren, Xiao-Lan Xu, Li-Yan Xiong, Yue-Xiang Peng, Xiao-Fang Pan, Christoph F Dietrich, Xin-Wu Cui. Ultrasound-based artificial intelligence in gastroenterology and hepatologyWorld Journal of Gastroenterology 2022; 28(38): 5530-5546 doi: 10.3748/wjg.v28.i38.5530
27
Joseph C Ahn, Touseef Ahmad Qureshi, Amit G Singal, Debiao Li, Ju-Dong Yang. Deep learning in hepatocellular carcinoma: Current status and future perspectivesWorld Journal of Hepatology 2021; 13(12): 2039-2051 doi: 10.4254/wjh.v13.i12.2039
28
Adrian Săftoiu, Peter Vilmann, Christoph F. Dietrich, Julio Iglesias-Garcia, Michael Hocke, Andrada Seicean, Andre Ignee, Hazem Hassan, Costin Teodor Streba, Ana Maria Ioncică, Dan Ionuţ Gheonea, Tudorel Ciurea. Quantitative contrast-enhanced harmonic EUS in differential diagnosis of focal pancreatic masses (with videos)Gastrointestinal Endoscopy 2015; 82(1): 59 doi: 10.1016/j.gie.2014.11.040
29
Salvatore Claudio Fanni, Sherif Mohsen Shalaby, Emanuele Neri. Imaging in GeriatricsPractical Issues in Geriatrics 2023; : 445 doi: 10.1007/978-3-031-14877-4_17
30
Xiangfei Feng, Wenjia Cai, Rongqin Zheng, Lina Tang, Jianhua Zhou, Hui Wang, Jintang Liao, Baoming Luo, Wen Cheng, An Wei, Weian Zhao, Xiang Jing, Ping Liang, Jie Yu, Qinghua Huang. Diagnosis of hepatocellular carcinoma using deep network with multi-view enhanced patterns mined in contrast-enhanced ultrasound dataEngineering Applications of Artificial Intelligence 2023; 118: 105635 doi: 10.1016/j.engappai.2022.105635
31
Cătălin Daniel Căleanu, Cristina Laura Sîrbu, Georgiana Simion. Deep Neural Architectures for Contrast Enhanced Ultrasound (CEUS) Focal Liver Lesions Automated DiagnosisSensors 2021; 21(12): 4126 doi: 10.3390/s21124126
32
Carlos Alberto Campello, Everton Bruno Castanha, Marina Vilardo, Pedro V. Staziaki, Martina Zaguini Francisco, Bahram Mohajer, Guilherme Watte, Fabio Ynoe Moraes, Bruno Hochhegger, Stephan Altmayer. Machine learning for malignant versus benign focal liver lesions on US and CEUS: a meta-analysisAbdominal Radiology 2023; 48(10): 3114 doi: 10.1007/s00261-023-03984-0
33
Elena Codruta Gheorghe, Carmen Nicolau, Adina Kamal, Anca Udristoiu, Lucian Gruionu, Adrian Saftoiu. Artificial Intelligence (AI)-Enhanced Ultrasound Techniques Used in Non-Alcoholic Fatty Liver Disease: Are They Ready for Prime Time?Applied Sciences 2023; 13(8): 5080 doi: 10.3390/app13085080
34
Yu Jiang, Kang Wang, Yu-Ran Wang, Yan-Jun Xiang, Zong-Han Liu, Jin-Kai Feng, Shu-Qun Cheng. Preoperative and Prognostic Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Review Based on Artificial IntelligenceTechnology in Cancer Research & Treatment 2023; 22 doi: 10.1177/15330338231212726
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Satoshi Kondo, Kazuya Takagi, Mutsumi Nishida, Takahito Iwai, Yusuke Kudo, Kouji Ogawa, Toshiya Kamiyama, Hitoshi Shibuya, Kaoru Kahata, Chikara Shimizu. Computer-Aided Diagnosis of Focal Liver Lesions Using Contrast-Enhanced Ultrasonography With Perflubutane MicrobubblesIEEE Transactions on Medical Imaging 2017; 36(7): 1427 doi: 10.1109/TMI.2017.2659734
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Le-Hang Guo, Dan Wang, Yi-Yi Qian, Xiao Zheng, Chong-Ke Zhao, Xiao-Long Li, Xiao-Wan Bo, Wen-Wen Yue, Qi Zhang, Jun Shi, Hui-Xiong Xu. A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound imagesClinical Hemorheology and Microcirculation 2018; 69(3): 343 doi: 10.3233/CH-170275
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Costin Teodor Streba, Mihaela Ionescu, Cristin Constantin Vere, Ion Rogoveanu. Translational Bioinformatics and Its ApplicationTranslational Medicine Research 2017; : 371 doi: 10.1007/978-94-024-1045-7_16
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Cristina Laura Sirbu, Georgiana Simion, Catalin Daniel Caleanu. Deep CNN for Contrast-Enhanced Ultrasound Focal Liver Lesions Diagnosis2020 International Symposium on Electronics and Telecommunications (ISETC) 2020; : 1 doi: 10.1109/ISETC50328.2020.9301116
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Jiakang Zhou, Fengxin Pan, Wei Li, Hangtong Hu, Wei Wang, Qinghua Huang. Feature Fusion for Diagnosis of Atypical Hepatocellular Carcinoma in Contrast- Enhanced UltrasoundIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2022; 69(1): 114 doi: 10.1109/TUFFC.2021.3110590
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Casey N. Ta, Yuko Kono, Mohammad Eghtedari, Young Taik Oh, Michelle L. Robbin, Richard G. Barr, Andrew C. Kummel, Robert F. Mattrey. Focal Liver Lesions: Computer-aided Diagnosis by Using Contrast-enhanced US Cine RecordingsRadiology 2018; 286(3): 1062 doi: 10.1148/radiol.2017170365
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Norio Nakata, Tsuyoshi Siina. Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic MassesBioengineering 2023; 10(1): 69 doi: 10.3390/bioengineering10010069
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Qinghua Huang, Fengxin Pan, Wei Li, Feiniu Yuan, Hangtong Hu, Jinhua Huang, Jie Yu, Wei Wang. Differential Diagnosis of Atypical Hepatocellular Carcinoma in Contrast-Enhanced Ultrasound Using Spatio-Temporal Diagnostic SemanticsIEEE Journal of Biomedical and Health Informatics 2020; 24(10): 2860 doi: 10.1109/JBHI.2020.2977937
43
Ana I. Faustino-Rocha, Adelina Gama, Paula A. Oliveira, Katrien Vanderperren, Jimmy H. Saunders, Maria J. Pires, Rita Ferreira, Mário Ginja. Modulation of mammary tumor vascularization by mast cells: Ultrasonographic and histopathological approachesLife Sciences 2017; 176: 35 doi: 10.1016/j.lfs.2017.03.013
44
Rodrigo Siqueira-Batista, Rodrigo Roger Vitorino, Andréia Patrícia Gomes, Alcione de Paiva Oliveira, Ricardo dos Santos Ferreira, Vanderson Esperidião-Antonio, Luiz Alberto Santana, Fabio Ribeiro Cerqueira. As redes neurais artificiais e o ensino da medicinaRevista Brasileira de Educação Médica 2014; 38(4): 548 doi: 10.1590/S0100-55022014000400017
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Simona Turco, Thodsawit Tiyarattanachai, Kambez Ebrahimkheil, John Eisenbrey, Aya Kamaya, Massimo Mischi, Andrej Lyshchik, Ahmed El Kaffas. Interpretable Machine Learning for Characterization of Focal Liver Lesions by Contrast-Enhanced UltrasoundIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2022; 69(5): 1670 doi: 10.1109/TUFFC.2022.3161719
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Feng Li, Wensheng Xu, Yujin Feng, Wengang Wang, Hui Tian, Suhuan He, Liang Li, Bai Xiang, Yueheng Wang. Preparation of ultrasound contrast agents: The exploration of the structure-echogenicity relationship of contrast agents based on neural network modelFrontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.964314
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Naoshi Nishida, Makoto Yamakawa, Tsuyoshi Shiina, Masatoshi Kudo. Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technologyHepatology International 2019; 13(4): 416 doi: 10.1007/s12072-019-09937-4
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Huili Zhang, Lehang Guo, Dan Wang, Jun Wang, Lili Bao, Shihui Ying, Huixiong Xu, Jun Shi. Multi-Source Transfer Learning Via Multi-Kernel Support Vector Machine Plus for B-Mode Ultrasound-Based Computer-Aided Diagnosis of Liver CancersIEEE Journal of Biomedical and Health Informatics 2021; 25(10): 3874 doi: 10.1109/JBHI.2021.3073812
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Mohammad Amin Salehi, Hamid Harandi, Soheil Mohammadi, Mohammad Shahrabi Farahani, Shayan Shojaei, Ramy R. Saleh. Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysisJournal of Imaging Informatics in Medicine 2024;  doi: 10.1007/s10278-024-01058-1
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S. Rabipour, Z. Asadi. Applied Genetic Algorithm and Its VariantsSpringer Tracts in Nature-Inspired Computing 2023; : 161 doi: 10.1007/978-981-99-3428-7_7