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For: Cao LL, Peng M, Xie X, Chen GQ, Huang SY, Wang JY, Jiang F, Cui XW, Dietrich CF. Artificial intelligence in liver ultrasound. World J Gastroenterol 2022; 28(27): 3398-3409 [PMID: 36158262 DOI: 10.3748/wjg.v28.i27.3398]
URL: https://www.wjgnet.com/1007-9327/full/v28/i27/3398.htm
Number Citing Articles
1
Rong Liu, Lei Li, Ying Zhou, Lu Xiong, Aamir Ijaz. The application of CBL and mind mapping combined with Mini-CEX teaching mode in the cultivation of clinical competence of ultrasound residentsPLOS One 2025; 20(7): e0327739 doi: 10.1371/journal.pone.0327739
2
Alexander A. Huang, Samuel Y. Huang. The Visualization of the Importance of Covariance Importance in a Machine Learning Model for Advanced Liver Fibrosis in a Nationally Representative SampleJGH Open 2025; 9(7) doi: 10.1002/jgh3.70200
3
Roongruedee Chaiteerakij, Darlene Ariyaskul, Kittipat Kulkraisri, Terapap Apiparakoon, Sasima Sukcharoen, Oracha Chaichuen, Phaiboon Pensuwan, Thodsawit Tiyarattanachai, Rungsun Rerknimitr, Sanparith Marukatat. Artificial intelligence for ultrasonographic detection and diagnosis of hepatocellular carcinoma and cholangiocarcinomaScientific Reports 2024; 14(1) doi: 10.1038/s41598-024-71657-z
4
Laxmi Rani, Garima Jain, Pooja Mathur, Shaveta Ahalwat, Shailendra Bhatt. Advances in Disaster Management, Volume 2Springer Transactions in Civil and Environmental Engineering 2025; : 185 doi: 10.1007/978-981-96-4047-8_18
5
Fei Xia, Wei Wei, Junli Wang, Yayang Duan, Kun Wang, Chaoxue Zhang. Machine learning model for non-alcoholic steatohepatitis diagnosis based on ultrasound radiomicsBMC Medical Imaging 2024; 24(1) doi: 10.1186/s12880-024-01398-y
6
Ke Fan, Lei Yang, Fei Ren, Xueyuan Zhang, Bo Liu, Ze Zhao, Jianwen Gu. Intelligent imaging technology applications in multidisciplinary hospitalsChinese Medical Journal 2024; 137(24): 3083 doi: 10.1097/CM9.0000000000003436
7
Odysseas P. Chatzipanagiotou, Constantinos Loukas, Michail Vailas, Nikolaos Machairas, Stylianos Kykalos, Georgios Charalampopoulos, Dimitrios Filippiadis, Evangellos Felekouras, Dimitrios Schizas. Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literatureJournal of Gastroenterology and Hepatology 2024; 39(10): 1994 doi: 10.1111/jgh.16663
8
C Uma, K Gobikanila, P R Jeyaramraja, Syraji Yonas. Metal nanoparticles in liver cancer therapy: Advances, challenges, and future directionsNext Research 2025; 2(3): 100421 doi: 10.1016/j.nexres.2025.100421
9
Arinc Ozturk, Viksit Kumar, Theodore T. Pierce, Qian Li, Masoud Baikpour, Ivan Rosado-Mendez, Michael Wang, Peng Guo, Scott Schoen, Yuyang Gu, Sunethra Dayavansha, Joseph R. Grajo, Anthony E. Samir. The Future Is Beyond Bright: The Evolving Role of Quantitative US for Fatty Liver DiseaseRadiology 2023; 309(2) doi: 10.1148/radiol.223146
10
Eloise S. Ockenden, Sandrena Ruth Frischer, Huike Cheng, J. Alison Noble, Goylette F. Chami, Elizabeth J. Carlton. The role of point-of-care ultrasound in the assessment of schistosomiasis-induced liver fibrosis: A systematic scoping reviewPLOS Neglected Tropical Diseases 2024; 18(3): e0012033 doi: 10.1371/journal.pntd.0012033
11
Stacey May, Christina Hendricks, Diana Mishler, Matthew Schwartz, Nancy Barge, Kristin Pahl Kenozy, Anne-Marie Lugossy. Radiology in Global Health2025; : 117 doi: 10.1007/978-3-031-86485-8_11
12
Xin Wu Cui, Adrian Goudie, Michael Blaivas, Young Jun Chai, Maria Cristina Chammas, Yi Dong, Jonathon Stewart, Tian-An Jiang, Ping Liang, Chandra M. Sehgal, Xing-Long Wu, Peter Ching-Chang Hsieh, Saftoiu Adrian, Christoph F. Dietrich. WFUMB Commentary Paper on Artificial intelligence in Medical Ultrasound ImagingUltrasound in Medicine & Biology 2025; 51(3): 428 doi: 10.1016/j.ultrasmedbio.2024.10.016
13
Zhao-Chun Chi. Application and prospect of artificial intelligence in diagnosis, treatment, and prognosis of liver cancerWorld Chinese Journal of Digestology 2025; 33(6): 429 doi: 10.11569/wcjd.v33.i6.429
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14
Wenpeng Huang, Yushuo Peng, Lei Kang. Advancements of non‐invasive imaging technologies for the diagnosis and staging of liver fibrosis: Present and futureVIEW 2024; 5(4) doi: 10.1002/VIW.20240010
15
José E. Valerio, Guillermo de Jesús Aguirre Vera, Maria P. Fernandez Gomez, Jorge Zumaeta, Andrés M. Alvarez-Pinzon. AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic OutcomesBrain Sciences 2025; 15(5): 494 doi: 10.3390/brainsci15050494
16
Jingpu Zhao, Yongfeng Xu. PITX1 plays essential functions in cancerFrontiers in Oncology 2023; 13 doi: 10.3389/fonc.2023.1253238
17
Binqian Zhou, Jianxin Liu, Yaqin Yang, Xuewei Ye, Yang Liu, Mingfeng Mao, Xiaofeng Sun, Xinwu Cui, Qin Zhou. Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learningBMC Cancer 2024; 24(1) doi: 10.1186/s12885-024-12546-6
18
Laura S. Kupke, Ivor Dropco, Markus Götz, Paul Kupke, Friedrich Jung, Christian Stroszczynski, Ernst-Michael Jung. Contrast-Enhanced Intraoperative Ultrasound Shows Excellent Performance in Improving Intraoperative Decision-MakingLife 2024; 14(9): 1199 doi: 10.3390/life14091199
19
Pinqi Fang, Dong Lang, Di Cheng, Zhouyu Guan, Yiting Wu, Yulian Zhang, Tingting Hu, Yuqian Bao, Huating Li, Chengxing Shen, Jun Pu, Bin Sheng, Jie Shen. Multimodal and multi-time-point fusion approach for automated diagnosis and grading of carotid atherosclerosis using bilateral ultrasound images and metadataThe Visual Computer 2025;  doi: 10.1007/s00371-025-04100-7