BPG is committed to discovery and dissemination of knowledge
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
1
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
2
Eun Bok Baek, Ji-Hee Hwang, Heejin Park, Byoung-Seok Lee, Hwa-Young Son, Yong-Bum Kim, Sang-Yeop Jun, Jun Her, Jaeku Lee, Jae-Woo Cho. Artificial Intelligence-Assisted Image Analysis of Acetaminophen-Induced Acute Hepatic Injury in Sprague-Dawley RatsDiagnostics 2022; 12(6): 1478 doi: 10.3390/diagnostics12061478
3
Fumitoshi Fukuzawa, Yasutaka Yanagita, Daiki Yokokawa, Shun Uchida, Shiho Yamashita, Yu Li, Kiyoshi Shikino, Tomoko Tsukamoto, Kazutaka Noda, Takanori Uehara, Masatomi Ikusaka. Importance of Patient History in Artificial Intelligence-Assisted Medical Diagnosis: A Comparison Study (Preprint)JMIR Medical Education 2023;  doi: 10.2196/52674
4
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
5
Xiaofei Fan, Xiaoming Qiao, Zhisheng Wang, Luetao Jiang, Yue Liu, Qingshan Sun, Arpit Bhardwaj. Artificial Intelligence-Based CT Imaging on Diagnosis of Patients with Lumbar Disc Herniation by Scalpel TreatmentComputational Intelligence and Neuroscience 2022; 2022: 1 doi: 10.1155/2022/3688630
6
Sudheer Babu, Dodala Anil Kumar, Kotha Siva Krishna. Next Generation of Internet of ThingsLecture Notes in Networks and Systems 2023; 445: 641 doi: 10.1007/978-981-19-1412-6_55
7
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
8
Longfei Ma, Rui Wang, Qiong He, Lijie Huang, Xingyue Wei, Xu Lu, Yanan Du, Jianwen Luo, Hongen Liao. Artificial intelligence-based ultrasound imaging technologies for hepatic diseasesiLIVER 2022; 1(4): 252 doi: 10.1016/j.iliver.2022.11.001
9
Rakesh Kumar, Sampurna Panda, Mini Anil, Anshul G., Ambali Pancholi. Communication, Networks and ComputingCommunications in Computer and Information Science 2023; 1893: 3 doi: 10.1007/978-3-031-43140-1_2
10
Tarik Kivrak, Jagadish Nayak, Mehmet Ali Gelen, Prabal Datta Barua, Mehmet Baygin, Hilal Erken Pamukcu, Sengul Dogan, Turker Tuncer, U. Rajendra Acharya. EfDenseNet: Automated Pulmonary Hypertension Detection Model Based on EfficientNetb0 and DenseNet201 Using CT ImagesIEEE Access 2023; 11: 142711 doi: 10.1109/ACCESS.2023.3338228
11
Aisha Siam, Abdel Rahman Alsaify, Bushra Mohammad, Md. Rafiul Biswas, Hazrat Ali, Zubair Shah. Multimodal deep learning for liver cancer applications: a scoping reviewFrontiers in Artificial Intelligence 2023; 6 doi: 10.3389/frai.2023.1247195
12
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 2022; 39(3) doi: 10.1111/exsy.12740
13
Chun-Li Cao, Qiao-Li Li, Jin Tong, Li-Nan Shi, Wen-Xiao Li, Ya Xu, Jing Cheng, Ting-Ting Du, Jun Li, Xin-Wu Cui. Artificial intelligence in thyroid ultrasoundFrontiers in Oncology 2023; 13 doi: 10.3389/fonc.2023.1060702
14
Brittany E. Levy, Jennifer T. Castle, Alexandr Virodov, Wesley S. Wilt, Cody Bumgardner, Thomas Brim, Erin McAtee, Morgan Schellenberg, Kenji Inaba, Zachary D. Warriner. Artificial intelligence evaluation of focused assessment with sonography in traumaJournal of Trauma and Acute Care Surgery 2023; 95(5): 706 doi: 10.1097/TA.0000000000004021
15
Biaoyang Lin, Yingying Ma, ShengJun Wu. Multi-Omics and Artificial Intelligence-Guided Data Integration in Chronic Liver Disease: Prospects and Challenges for Precision MedicineOMICS: A Journal of Integrative Biology 2022; 26(8): 415 doi: 10.1089/omi.2022.0079
16
Qiuxia Wei, Nengren Tan, Shiyu Xiong, Wanrong Luo, Haiying Xia, Baoming Luo. Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-AnalysisCancers 2023; 15(23): 5701 doi: 10.3390/cancers15235701
17
Rakesh Kalapala, Hardik Rughwani, D. Nageshwar Reddy. Artificial Intelligence in Hepatology- Ready for the PrimetimeJournal of Clinical and Experimental Hepatology 2023; 13(1): 149 doi: 10.1016/j.jceh.2022.06.009
18
Yueqin Dun, Yu Kong, Jinshan Tang. Efficient Johnson-SB Mixture Model for Segmentation of CT Liver ImageJournal of Healthcare Engineering 2022; 2022: 1 doi: 10.1155/2022/5654424
19
Cognitive Intelligence and Big Data in Healthcare2022; : 349 doi: 10.1002/9781119771982.ch13
20
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
21
Sutthirak Tangruangkiat, Napatsorn Chaiwongkot, Chayanon Pamarapa, Thanatcha Rawangwong, Araya Khunnarong, Chanyanuch Chainarong, Preeyanun Sathapanawanthana, Pantajaree Hiranrat, Ruedeerat Keerativittayayut, Witaya Sungkarat, Monchai Phonlakrai. Diagnosis of focal liver lesions from ultrasound images using a pretrained residual neural networkJournal of Applied Clinical Medical Physics 2024; 25(1) doi: 10.1002/acm2.14210
22
Javier Briceño. Artificial intelligence and organ transplantation: challenges and expectationsCurrent Opinion in Organ Transplantation 2020; 25(4): 393 doi: 10.1097/MOT.0000000000000775
23
Kaori Tabata, Mana Hashimoto, Haruka Takahashi, Ziyi Wang, Noriyuki Nagaoka, Toru Hara, Hiroshi Kamioka. A morphometric analysis of the osteocyte canaliculus using applied automatic semantic segmentation by machine learningJournal of Bone and Mineral Metabolism 2022; 40(4): 571 doi: 10.1007/s00774-022-01321-x
24
Ricardo A. Serrano, Alan M. Smeltz. The Promise of Artificial Intelligence-Assisted Point-of-Care Ultrasonography in Perioperative CareJournal of Cardiothoracic and Vascular Anesthesia 2024;  doi: 10.1053/j.jvca.2024.01.034
25
B. Lakshmipriya, Biju Pottakkat, G. Ramkumar. Deep learning techniques in liver tumour diagnosis using CT and MR imaging - A systematic reviewArtificial Intelligence in Medicine 2023; 141: 102557 doi: 10.1016/j.artmed.2023.102557
26
Kareem Ahmed, Mai A. Gad, Amal Elsayed Aboutabl. Performance evaluation of salient object detection techniquesMultimedia Tools and Applications 2022; 81(15): 21741 doi: 10.1007/s11042-022-12567-y
27
Qi Zhao, Yadi Lan, Xunjun Yin, Kai Wang. Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysisBMC Medical Imaging 2023; 23(1) doi: 10.1186/s12880-023-01172-6
28
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
29
Shunsuke Koga, Wei Du. Integrating AI in medicine: Lessons from Chat-GPT's limitations in medical imagingDigestive and Liver Disease 2024;  doi: 10.1016/j.dld.2024.02.014
30
Kristoffer Knutsen Wickstrøm, Eirik Agnalt Østmo, Keyur Radiya, Karl Øyvind Mikalsen, Michael Christian Kampffmeyer, Robert Jenssen. A clinically motivated self-supervised approach for content-based image retrieval of CT liver imagesComputerized Medical Imaging and Graphics 2023; 107: 102239 doi: 10.1016/j.compmedimag.2023.102239
31
Nizar Alsharif, Mosleh Hmoud Al-Adhaileh, Mohammed Al-Yaari. Accurate Identification of Attention-deficit/Hyperactivity Disorder Using Machine Learning ApproachesJournal of Disability Research 2024; 3(1) doi: 10.57197/JDR-2023-0053
32
Emerson Nithiyaraj E, Arivazhagan Selvaraj. Morph-Rec: A Novel Computer-Aided Liver Segmentation Model based on Morphological Reconstruction OperationIETE Journal of Research 2023; : 1 doi: 10.1080/03772063.2023.2175052
33
Tsai-Chun Chung, Ya-Hsin Hsu, Tianle Chen, Yang Li, Haochen Yang, Jin-Xiu Yu, I-Chi Lee, Ping-Shan Lai, Yi-Chen Ethan Li, Po-Yen Chen. Machine Learning Integrated Workflow for Predicting Schwann Cell Viability on Conductive MXene BiointerfacesACS Applied Materials & Interfaces 2023; 15(39): 46460 doi: 10.1021/acsami.3c08070
34
Tong Xu, Xian-Ya Zhang, Na Yang, Fan Jiang, Gong-Quan Chen, Xiao-Fang Pan, Yue-Xiang Peng, Xin-Wu Cui. A narrative review on the application of artificial intelligence in renal ultrasoundFrontiers in Oncology 2024; 13 doi: 10.3389/fonc.2023.1252630
35
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
36
Mohammed Yusuf Ansari, Yin Yang, Pramod Kumar Meher, Sarada Prasad Dakua. Dense-PSP-UNet: A neural network for fast inference liver ultrasound segmentationComputers in Biology and Medicine 2023; 153: 106478 doi: 10.1016/j.compbiomed.2022.106478
37
Rakesh Kumar, Mini Anil, Sampurna Panda, Ashish Raj. Medical imaging: Challenges and future directions in AI-Based systemsRECENT ADVANCES IN SCIENCES, ENGINEERING, INFORMATION TECHNOLOGY & MANAGEMENT 2023; 2782: 020147 doi: 10.1063/5.0154355
38
Ashok Kamalanathan, Babu Muthu, Patheri Kuniyil Kaleena. Marvels of Artificial and Computational Intelligence in Life Sciences2023; : 62 doi: 10.2174/9789815136807123010009
39
Lanping Wu, Bin Dong, Xiaoqing Liu, Wenjing Hong, Lijun Chen, Kunlun Gao, Qiuyang Sheng, Yizhou Yu, Liebin Zhao, Yuqi Zhang. Standard Echocardiographic View Recognition in Diagnosis of Congenital Heart Defects in Children Using Deep Learning Based on Knowledge DistillationFrontiers in Pediatrics 2022; 9 doi: 10.3389/fped.2021.770182
40
Anita Aminoshariae, Ali Nosrat, Venkateshbabu Nagendrababu, Omid Dianat, Hossein Mohammad-Rahimi, Abbey W. O'Keefe, Frank C. Setzer. Artificial Intelligence in Endodontic EducationJournal of Endodontics 2024;  doi: 10.1016/j.joen.2024.02.011
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
42
Chen Chen, Cheng Chen, Mingrui Ma, Xiaojian Ma, Xiaoyi Lv, Xiaogang Dong, Ziwei Yan, Min Zhu, Jiajia Chen. Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanismBMC Medical Informatics and Decision Making 2022; 22(1) doi: 10.1186/s12911-022-01919-1
43
Xim Bokhimi. Learning the Use of Artificial Intelligence in Heterogeneous CatalysisFrontiers in Chemical Engineering 2021; 3 doi: 10.3389/fceng.2021.740270
44
K. Sinha, Z. Uddin, H.I. Kawsar, S. Islam, M.J. Deen, M.M.R. Howlader. Analyzing chronic disease biomarkers using electrochemical sensors and artificial neural networksTrAC Trends in Analytical Chemistry 2023; 158: 116861 doi: 10.1016/j.trac.2022.116861
45
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
46
Almir Badnjević, Halida Avdihodžić, Lejla Gurbeta Pokvić. Artificial Intelligence in Medical Devices: Past, Present and FutureScience, Art and Religion 2022; 1(1-2): 101 doi: 10.5005/sar-1-1-2-101
47
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
48
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
49
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
50
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
51
V. Antony Asir Daniel, Ravi Ramaraj. A novel modified long short term memory architecture for automatic liver disease prediction from patient recordsConcurrency and Computation: Practice and Experience 2022; 34(28) doi: 10.1002/cpe.7372
52
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
53
Yusuf YILMAZ, Derya UZELLİ YILMAZ, Duygu YILDIRIM, Esra AKIN KORHAN, Derya ÖZER KAYA. Yapay Zeka ve Sağlıkta Yapay Zekanın Kullanımına Yönelik Sağlık Bilimleri Fakültesi Öğrencilerinin GörüşleriSüleyman Demirel Üniversitesi Sağlık Bilimleri Dergisi 2021; 12(3): 297 doi: 10.22312/sdusbed.950372
54
Ping-Hsun Lu, Chih-Chi Chiang, Wei-Hsuan Yu, Min-Chien Yu, Feng-Nan Hwang, Luminita Moraru. Machine Learning-Based Technique for the Severity Classification of Sublingual Varices according to Traditional Chinese MedicineComputational and Mathematical Methods in Medicine 2022; 2022: 1 doi: 10.1155/2022/3545712
55
Shanmugapriya Survarachakan, Pravda Jith Ray Prasad, Rabia Naseem, Javier Pérez de Frutos, Rahul Prasanna Kumar, Thomas Langø, Faouzi Alaya Cheikh, Ole Jakob Elle, Frank Lindseth. Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesionsArtificial Intelligence in Medicine 2022; 130: 102331 doi: 10.1016/j.artmed.2022.102331
56
Valeria Tonini, Gabriele Vigutto, Riccardo Donati. Liver surgery for colorectal metastasis: New paths and new goals with the help of artificial intelligenceArtificial Intelligence in Gastroenterology 2022; 3(2): 28-35 doi: 10.35712/aig.v3.i2.28
57
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
58
Aylin Tahmasebi, Shuo Wang, Corinne E. Wessner, Trang Vu, Ji‐Bin Liu, Flemming Forsberg, Jesse Civan, Flavius F. Guglielmo, John R. Eisenbrey. Ultrasound‐Based Machine Learning Approach for Detection of Nonalcoholic Fatty Liver DiseaseJournal of Ultrasound in Medicine 2023; 42(8): 1747 doi: 10.1002/jum.16194
59
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
60
Rajnish Kumar, Farhat Ullah Khan, Anju Sharma, Izzatdin B.A. Aziz, Nitesh Kumar Poddar. Recent Applications of Artificial Intelligence in the Detection of Gastrointestinal, Hepatic and Pancreatic DiseasesCurrent Medicinal Chemistry 2022; 29(1): 66 doi: 10.2174/0929867328666210405114938
61
Xing-Rui Wang, Xi Ma, Liu-Xu Jin, Yan-Jun Gao, Yong-Jie Xue, Jing-Long Li, Wei-Xian Bai, Miao-Fei Han, Qing Zhou, Feng Shi, Jing Wang. Application value of a deep learning method based on a 3D V-Net convolutional neural network in the recognition and segmentation of the auditory ossiclesFrontiers in Neuroinformatics 2022; 16 doi: 10.3389/fninf.2022.937891
62
Keith Feldman, Justin Baraboo, Deeyendal Dinakarpandian, Sherwin S. Chan. Machine Learning Algorithm Improves the Prediction of Transplant Hepatic Artery Stenosis or OcclusionUltrasound Quarterly 2023; 39(2): 86 doi: 10.1097/RUQ.0000000000000624
63
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
64
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; 59(12): 1309 doi: 10.1055/a-1645-3138
65
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
66
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
67
Shouyuan Wu, Jianjian Wang, Qiangqiang Guo, Hui Lan, Juanjuan Zhang, Ling Wang, Estill Janne, Xufei Luo, Qi Wang, Yang Song, Joseph L. Mathew, Yangqin Xun, Nan Yang, Myeong Soo Lee, Yaolong Chen. Application of artificial intelligence in clinical diagnosis and treatment: an overview of systematic reviewsIntelligent Medicine 2022; 2(2): 88 doi: 10.1016/j.imed.2021.12.001
68
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
69
Amelia K Barwise, Susan Curtis, Daniel A Diedrich, Brian W Pickering. Using artificial intelligence to promote equitable care for inpatients with language barriers and complex medical needs: clinical stakeholder perspectivesJournal of the American Medical Informatics Association 2024; 31(3): 611 doi: 10.1093/jamia/ocad224
70
Maki Kinugasa, Atsuyuki Inui, Shinichi Satsuma, Daisuke Kobayashi, Ryosuke Sakata, Masayuki Morishita, Izumi Komoto, Ryosuke Kuroda. Diagnosis of Developmental Dysplasia of the Hip by Ultrasound Imaging Using Deep LearningJournal of Pediatric Orthopaedics 2023; 43(7): e538 doi: 10.1097/BPO.0000000000002428
71
Md. Maniruzzaman, Jungpil Shin, Md. Al Mehedi Hasan. Predicting Children with ADHD Using Behavioral Activity: A Machine Learning AnalysisApplied Sciences 2022; 12(5): 2737 doi: 10.3390/app12052737
72
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
73
Mubasher Hussain, Najia Saher, Salman Qadri. Computer Vision Approach for Liver Tumor Classification Using CT DatasetApplied Artificial Intelligence 2022; 36(1) doi: 10.1080/08839514.2022.2055395
74
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
75
Gökhan Serhat DURAN, Ebru YURDAKURBAN, Rüveyda DOĞRUGÖREN, Serkan GÖRGÜLÜ. Current Trends in Cleft Lip and Palate Publications During the Last 10 Years: A Bibliometric AnalysisSelcuk Dental Journal 2022; 9(3): 777 doi: 10.15311/selcukdentj.1005295
76
Yafang Zhang, Qingyue Wei, Yini Huang, Zhao Yao, Cuiju Yan, Xuebin Zou, Jing Han, Qing Li, Rushuang Mao, Ying Liao, Lan Cao, Min Lin, Xiaoshuang Zhou, Xiaofeng Tang, Yixin Hu, Lingling Li, Yuanyuan Wang, Jinhua Yu, Jianhua Zhou. Deep Learning of Liver Contrast-Enhanced Ultrasound to Predict Microvascular Invasion and Prognosis in Hepatocellular CarcinomaFrontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.878061
77
Zhen Yuan, Esther Puyol-Antón, Haran Jogeesvaran, Nicola Smith, Baba Inusa, Andrew P. King. Deep learning-based quality-controlled spleen assessment from ultrasound imagesBiomedical Signal Processing and Control 2022; 76: 103724 doi: 10.1016/j.bspc.2022.103724
78
Se-Yeol Rhyou, Jae-Chern Yoo. Aggregated micropatch-based deep learning neural network for ultrasonic diagnosis of cirrhosisArtificial Intelligence in Medicine 2023; 139: 102541 doi: 10.1016/j.artmed.2023.102541
79
Mohammed Yusuf Ansari, Iffa Afsa Changaai Mangalote, Dima Masri, Sarada Prasad Dakua. Neural Network-based Fast Liver Ultrasound Image Segmentation2023 International Joint Conference on Neural Networks (IJCNN) 2023; : 1 doi: 10.1109/IJCNN54540.2023.10191085
80
Hongliang Li, Manish Bhatt, Zhen Qu, Shiming Zhang, Martin C. Hartel, Ali Khademhosseini, Guy Cloutier. Deep learning in ultrasound elastography imaging: A reviewMedical Physics 2022; 49(9): 5993 doi: 10.1002/mp.15856
81
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
82
S.N. Buyanova, N.A. Shchukina, A.Yu. Temlyakov, T.A. Glebov. Artificial intelligence in pregnancy predictionRossiiskii vestnik akushera-ginekologa 2023; 23(2): 83 doi: 10.17116/rosakush20232302183
83
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
84
Yunus DOĞAN, Fatma RIDAOUI. Knowledge Discovery Using Clustering Methods in Medical Database: A Case Study for Reflux DiseaseSakarya University Journal of Science 2021; 25(2): 439 doi: 10.16984/saufenbilder.837209
85
Manal Makram, Mohammad Elhemeily, Ammar Mohammed. Deep Learning Approach for Liver Tumor Diagnosis2023 Intelligent Methods, Systems, and Applications (IMSA) 2023; : 210 doi: 10.1109/IMSA58542.2023.10217588
86
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
87
艳 任. Application of Artificial Intelligence in Ultrasonic Diagnosis of Liver DiseasesAdvances in Clinical Medicine 2023; 13(10): 16355 doi: 10.12677/ACM.2023.13102288
88
Judit Csore, Christof Karmonik, Kayla Wilhoit, Lily Buckner, Trisha L. Roy. Automatic Classification of Magnetic Resonance Histology of Peripheral Arterial Chronic Total Occlusions Using a Variational Autoencoder: A Feasibility StudyDiagnostics 2023; 13(11): 1925 doi: 10.3390/diagnostics13111925
89
Meilong Wu, Liping Liu, Xiaojuan Wang, Ying Xiao, Shizhong Yang, Jiahong Dong. Radiomic features on contrast-enhanced images of the remnant liver predict the prognosis of hepatocellular carcinoma after partial hepatectomyiLIVER 2024; 3(1): 100079 doi: 10.1016/j.iliver.2024.100079
90
Andreas Teufel, Harald Binder. Clinical Decision Support SystemsVisceral Medicine 2021; 37(6): 491 doi: 10.1159/000519420
91
梦莹 邢. Advances in the Application of Artificial Intelligence in the Field of Chronic Wound CareAdvances in Clinical Medicine 2022; 12(12): 11013 doi: 10.12677/ACM.2022.12121586
92
Li-Qiang Zhou, Shu-E. Zeng, Jian-Wei Xu, Wen-Zhi Lv, Dong Mei, Jia-Jun Tu, Fan Jiang, Xin-Wu Cui, Christoph F. Dietrich. Deep learning predicts cervical lymph node metastasis in clinically node-negative papillary thyroid carcinomaInsights into Imaging 2023; 14(1) doi: 10.1186/s13244-023-01550-2
93
Yingjie Tian, Minghao Liu, Yu Sun, Saiji Fu. When liver disease diagnosis encounters deep learning: Analysis, challenges, and prospectsiLIVER 2023; 2(1): 73 doi: 10.1016/j.iliver.2023.02.002
94
Keyur Radiya, Henrik Lykke Joakimsen, Karl Øyvind Mikalsen, Eirik Kjus Aahlin, Rolv-Ole Lindsetmo, Kim Erlend Mortensen. Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic reviewEuropean Radiology 2023; 33(10): 6689 doi: 10.1007/s00330-023-09609-w
95
Bhaswar Ghosh, Soham Choudhuri. Plasmodium Species and Drug Resistance2021;  doi: 10.5772/intechopen.98695
96
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
97
Feifei Lu, Yao Meng, Xiaoting Song, Xiaotong Li, Zhuang Liu, Chunru Gu, Xiaojie Zheng, Yi Jing, Wei Cai, Kanokwan Pinyopornpanish, Andrea Mancuso, Fernando Gomes Romeiro, Nahum Méndez-Sánchez, Xingshun Qi. Artificial Intelligence in Liver Diseases: Recent AdvancesAdvances in Therapy 2024; 41(3): 967 doi: 10.1007/s12325-024-02781-5
98
Bing Wang, Zheng Wan, Chen Li, Mingbo Zhang, YiLei Shi, Xin Miao, Yanbing Jian, Yukun Luo, Jing Yao, Wen Tian. Identification of benign and malignant thyroid nodules based on dynamic AI ultrasound intelligent auxiliary diagnosis systemFrontiers in Endocrinology 2022; 13 doi: 10.3389/fendo.2022.1018321
99
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
100
Hila Chalutz Ben-Gal. Artificial intelligence (AI) acceptance in primary care during the coronavirus pandemic: What is the role of patients' gender, age and health awareness? A two-phase pilot studyFrontiers in Public Health 2023; 10 doi: 10.3389/fpubh.2022.931225
101
Adrian Truszkiewicz, Dorota Bartusik-Aebisher, Łukasz Wojtas, Grzegorz Cieślar, Aleksandra Kawczyk-Krupka, David Aebisher. Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T1 and T2 Relaxation Times with Application to Cancer Cell CultureInternational Journal of Molecular Sciences 2023; 24(2): 1554 doi: 10.3390/ijms24021554
102
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
103
Hai Yang, Xiaohui Sun, Yang Sun, Ligang Cui, Bingshan Li. Ultrasound Image-Based Diagnosis of Cirrhosis with an End-to-End Deep Learning model2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2020; : 1193 doi: 10.1109/BIBM49941.2020.9313579
104
Sergio J. Sanabria, Amir M. Pirmoazen, Jeremy Dahl, Aya Kamaya, Ahmed El Kaffas. Comparative Study of Raw Ultrasound Data Representations in Deep Learning to Classify Hepatic SteatosisUltrasound in Medicine & Biology 2022; 48(10): 2060 doi: 10.1016/j.ultrasmedbio.2022.05.031
105
Rini Widyaningrum, Ika Candradewi, Nur Rahman Ahmad Seno Aji, Rona Aulianisa. Comparison of Multi-Label U-Net and Mask R-CNN for panoramic radiograph segmentation to detect periodontitisImaging Science in Dentistry 2022; 52(4): 383 doi: 10.5624/isd.20220105
106
Ranjita Misra, Malathi Sampath. Artificial Intelligence Based Cancer Nanomedicine: Diagnostics, Therapeutics and Bioethics2022; : 27 doi: 10.2174/9789815050561122010007
107
Hsu-Heng Yen, Hui-Yu Tsai, Chi-Chih Wang, Ming-Chang Tsai, Ming-Hseng Tseng. An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine LearningDiagnostics 2022; 12(11): 2827 doi: 10.3390/diagnostics12112827
108
Takahisa Akashi, Tomoyuki Okumura, Kenji Terabayashi, Yuki Yoshino, Haruyoshi Tanaka, Takeyoshi Yamazaki, Yoshihisa Numata, Takuma Fukuda, Takahiro Manabe, Hayato Baba, Takeshi Miwa, Toru Watanabe, Katsuhisa Hirano, Takamichi Igarashi, Shinichi Sekine, Isaya Hashimoto, Kazuto Shibuya, Shozo Hojo, Isaku Yoshioka, Koshi Matsui, Akane Yamada, Tohru Sasaki, Tsutomu Fujii. The use of an artificial intelligence algorithm for circulating tumor cell detection in patients with esophageal cancerOncology Letters 2023; 26(1) doi: 10.3892/ol.2023.13906
109
Stephanie Batista Niño, Jorge Bernardino, Inês Domingues. Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical PerspectiveSensors 2024; 24(6): 1752 doi: 10.3390/s24061752
110
Jing-wen Shi, Qi Zhang, Tian-tong Zhu, Ying Huang. Multilayer Perceptron Predicting Cervical Lymph Node Metastasis for Papillary Thyroid CarcinomaBIO Integration 2022; 3(1) doi: 10.15212/bioi-2021-0029
111
Dae Kon Kim, Byeong Soo Kim, Yu Jin Kim, Sungwan Kim, Dan Yoon, Dong Keon Lee, Joo Jeong, You Hwan Jo. Development and validation of an artificial intelligence algorithm for detecting vocal cords in video laryngoscopyMedicine 2023; 102(51): e36761 doi: 10.1097/MD.0000000000036761
112
Huili Zhang, Lehang Guo, Jun Wang, Shihui Ying, Jun Shi. Multi-View Feature Transformation Based SVM+ for Computer-Aided Diagnosis of Liver Cancers With Ultrasound ImagesIEEE Journal of Biomedical and Health Informatics 2023; 27(3): 1512 doi: 10.1109/JBHI.2022.3233717
113
A. Amruthamathi, D. Devi. Diagnosis of liver failure using flask web frameworkINTERNATIONAL CONFERENCE ON INNOVATIONS IN ROBOTICS, INTELLIGENT AUTOMATION AND CONTROL 2023; 2914: 050016 doi: 10.1063/5.0176551
114
Kevin Y. Kim, Rajeev Nowrangi, Arianna McGehee, Neil Joshi, Patricia T. Acharya. Assessment of germinal matrix hemorrhage on head ultrasound with deep learning algorithmsPediatric Radiology 2022; 52(3): 533 doi: 10.1007/s00247-021-05239-w
115
Fahad Muflih Alshagathrh, Mowafa Said Househ. Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic ReviewBioengineering 2022; 9(12): 748 doi: 10.3390/bioengineering9120748
116
Nor Asiah Muhamad, Nur Hasnah Maamor, Fatin Norhasny Leman, Zuraifah Asrah Mohamad, Sophia Karen Bakon, Mohd Hatta Abdul Mutalip, Izzah Athirah Rosli, Tahir Aris, Nai Ming Lai, Muhammad Radzi Abu Hassan. The Global Prevalence of Nonalcoholic Fatty Liver Disease and its Association With Cancers: Systematic Review and Meta-AnalysisInteractive Journal of Medical Research 2023; 12: e40653 doi: 10.2196/40653
117
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
118
S. Saravanan, Kannan Ramkumar, K. Adalarasu, Venkatesh Sivanandam, S. Rakesh Kumar, S. Stalin, Rengarajan Amirtharajan. A Systematic Review of Artificial Intelligence (AI) Based Approaches for the Diagnosis of Parkinson’s DiseaseArchives of Computational Methods in Engineering 2022; 29(6): 3639 doi: 10.1007/s11831-022-09710-1
119
Demeng Xia, Gaoqi Chen, Kaiwen Wu, Mengxin Yu, Zhentao Zhang, Yixian Lu, Lisha Xu, Yin Wang. Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011–2021: A bibliometric analysisFrontiers in Public Health 2022; 10 doi: 10.3389/fpubh.2022.990708
120
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
121
Narmatha Sasi Prakash, Lakshmi Chandran, Madhana Kumar Sivakumar, Ankul Singh Suresh Pratap Singh. Perspectives of Artificial Intelligence (AI) in Health Care Management: Prospect and ProtestThe Chinese Journal of Artificial Intelligence 2022; 1(2) doi: 10.2174/2666782701666220920091940
122
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 2022; 54(3): 299 doi: 10.1016/j.dld.2021.06.011
123
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 2022; 51(2): 391 doi: 10.1007/s00256-021-03873-x
124
He-Li Xu, Ting-Ting Gong, Fang-Hua Liu, Hong-Yu Chen, Qian Xiao, Yang Hou, Ying Huang, Hong-Zan Sun, Yu Shi, Song Gao, Yan Lou, Qing Chang, Yu-Hong Zhao, Qing-Lei Gao, Qi-Jun Wu. Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysiseClinicalMedicine 2022; 53: 101662 doi: 10.1016/j.eclinm.2022.101662
125
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
126
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
127
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
128
An-Zi Yen, Cheng-Kuang Wu, Hsin-Hsi Chen. Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine in Liver Diseases2023; : 281 doi: 10.1016/B978-0-323-99136-0.00009-X
129
Hyunsu Choi, Leonard Sunwoo, Se Jin Cho, Sung Hyun Baik, Yun Jung Bae, Byung Se Choi, Cheolkyu Jung, Jae Hyoung Kim. A Nationwide Web-Based Survey of Neuroradiologists’ Perceptions of Artificial Intelligence Software for Neuro-Applications in KoreaKorean Journal of Radiology 2023; 24(5): 454 doi: 10.3348/kjr.2022.0905
130
Carolina Río Bártulos, Karin Senk, Mona Schumacher, Jan Plath, Nico Kaiser, Ragnar Bade, Jan Woetzel, Philipp Wiggermann. Assessment of Liver Function With MRI: Where Do We Stand?Frontiers in Medicine 2022; 9 doi: 10.3389/fmed.2022.839919
131
Kai Liu, Haitao Sun, Xingxing Wang, Xixi Wen, Jun Yang, Xingjian Zhang, Caizhong Chen, Mengsu Zeng. Feasibility of the application of deep learning-reconstructed ultra-fast respiratory-triggered T2-weighted imaging at 3 T in liver imagingMagnetic Resonance Imaging 2024; 109: 27 doi: 10.1016/j.mri.2024.03.001
132
Frank Mayta-Tovalino, Fran Espinoza-Carhuancho, Daniel Alvitez-Temoche, Cesar Mauricio-Vilchez, Arnaldo Munive-Degregori, John Barja-Ore. Scientometric analysis on the use of ChatGPT, artificial intelligence, or intelligent conversational agent in the role of medical trainingEducación Médica 2024; 25(2): 100873 doi: 10.1016/j.edumed.2023.100873
133
Yiftach Barash, Eyal Klang, Adar Lux, Eli Konen, Nir Horesh, Ron Pery, Nadav Zilka, Rony Eshkenazy, Ido Nachmany, Niv Pencovich. Artificial intelligence for identification of focal lesions in intraoperative liver ultrasonographyLangenbeck's Archives of Surgery 2022; 407(8): 3553 doi: 10.1007/s00423-022-02674-7
134
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
135
Thifhelimbilu Luvhengo, Thulo Molefi, Demetra Demetriou, Rodney Hull, Zodwa Dlamini. Artificial Intelligence and Precision Oncology2023; : 49 doi: 10.1007/978-3-031-21506-3_3
136
Gi Kim, Ho Zhang, Yong Cho, Seung Ryu. Differential Screening of Herniated Lumbar Discs Based on Bag of Visual Words Image Classification Using Digital Infrared Thermographic ImagesHealthcare 2022; 10(6): 1094 doi: 10.3390/healthcare10061094
137
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