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For: Parasher G, Wong M, Rawat M. Evolving role of artificial intelligence in gastrointestinal endoscopy. World J Gastroenterol 2020; 26(46): 7287-7298 [PMID: 33362384 DOI: 10.3748/wjg.v26.i46.7287]
URL: https://www.wjgnet.com/1007-9327/full/v26/i46/7287.htm
Number Citing Articles
1
Hai-Yang Chen, Peng Ge, Jia-Yue Liu, Jia-Lin Qu, Fang Bao, Cai-Ming Xu, Hai-Long Chen, Dong Shang, Gui-Xin Zhang. Artificial intelligence: Emerging player in the diagnosis and treatment of digestive diseaseWorld Journal of Gastroenterology 2022; 28(20): 2152-2162 doi: 10.3748/wjg.v28.i20.2152
2
Caesar Ferrari, Micheal Tadros. Enhancing the Quality of Upper Gastrointestinal Endoscopy: Current Indicators and Future TrendsGastroenterology Insights 2023; 15(1): 1 doi: 10.3390/gastroent15010001
3
Joanna Ejdys, Magdalena Czerwińska, Romualdas Ginevičius . Social acceptance of artificial intelligence (AI) application for improving medical service diagnosticsHuman Technology 2024; 20(1): 155 doi: 10.14254/1795-6889.2024.20-1.8
4
Utkarsh Ojha, James Ayathamattam, Kenneth Okonkwo, Innocent Ogunmwonyi. Recent Updates and Technological Developments in Evaluating Cardiac Syncope in the Emergency DepartmentCurrent Cardiology Reviews 2022; 18(6) doi: 10.2174/1573403X18666220421110935
5
Liang Wang, Hui Song, Ming Wang, Hui Wang, Ran Ge, Yan Shen, Yongli Yu, Kalidoss Rajakani. Utilization of Ultrasonic Image Characteristics Combined with Endoscopic Detection on the Basis of Artificial Intelligence Algorithm in Diagnosis of Early Upper Gastrointestinal CancerJournal of Healthcare Engineering 2021; 2021: 1 doi: 10.1155/2021/2773022
6
Hasan Maulahela, Nagita Gianty Annisa. Current advancements in application of artificial intelligence in clinical decision-making by gastroenterologists in gastrointestinal bleedingArtificial Intelligence in Gastroenterology 2022; 3(1): 13-20 doi: 10.35712/aig.v3.i1.13
7
Kareem Khalaf, Maria Terrin, Manol Jovani, Tommy Rizkala, Marco Spadaccini, Katarzyna M. Pawlak, Matteo Colombo, Marta Andreozzi, Alessandro Fugazza, Antonio Facciorusso, Fabio Grizzi, Cesare Hassan, Alessandro Repici, Silvia Carrara. A Comprehensive Guide to Artificial Intelligence in Endoscopic UltrasoundJournal of Clinical Medicine 2023; 12(11): 3757 doi: 10.3390/jcm12113757
8
Shuangyang Mo, Cheng Huang, Yingwei Wang, Huaying Zhao, Haixiao Wei, Haiyan Qin, Haixing Jiang, Shanyu Qin. Construction and validation of an endoscopic ultrasonography-based ultrasomics nomogram for differentiating pancreatic neuroendocrine tumors from pancreatic cancerFrontiers in Oncology 2024; 14 doi: 10.3389/fonc.2024.1359364
9
Tao Yan, Pak Kin Wong, Ye-Ying Qin. Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A reviewWorld Journal of Gastroenterology 2021; 27(20): 2531-2544 doi: 10.3748/wjg.v27.i20.2531
10
Mohadeseh Mahmoudi Ghehsareh, Nastaran Asri, Sepehr Maleki, Mostafa Rezaei-Tavirani, Somayeh Jahani-Sherafat, Mohammad Rostami-Nejad. Application of Artificial Intelligence in Celiac Disease: from diagnosis to patient follow-upIranian Journal of Blood and Cancer 2023; 15(3): 125 doi: 10.61186/ijbc.15.3.125
11
Glen Purnomo, Seng-Jin Yeo, Ming Han Lincoln Liow. Artificial intelligence in arthroplastyArthroplasty 2021; 3(1) doi: 10.1186/s42836-021-00095-3
12
Danny Con, Daniel R van Langenberg, Abhinav Vasudevan. Deep learning <i>vs</i> conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept studyWorld Journal of Gastroenterology 2021; 27(38): 6476-6488 doi: 10.3748/wjg.v27.i38.6476
13
Shuangyang Mo, Yingwei Wang, Cheng Huang, Wenhong Wu, Shanyu Qin. A novel endoscopic ultrasomics-based machine learning model and nomogram to predict the pathological grading of pancreatic neuroendocrine tumorsHeliyon 2024; : e34344 doi: 10.1016/j.heliyon.2024.e34344
14
Jie-Hyun Kim, Sang-Il Oh, So-Young Han, Ji-Soo Keum, Kyung-Nam Kim, Jae-Young Chun, Young-Hoon Youn, Hyojin Park. An Optimal Artificial Intelligence System for Real-Time Endoscopic Prediction of Invasion Depth in Early Gastric CancerCancers 2022; 14(23): 6000 doi: 10.3390/cancers14236000
15
Jeffrey R. Fetzer, Renisha Redij, Joshika Agarwal, Anjali Rajagopal, Keerthy Gopalakrishnan, Akhila Sai Sree Cherukuri, John League, Daniela Guerrero Vinsard, Cadman L. Leggett, Coelho-Prabhu Nayantara, Shivaram P. Arunachalam. Endoscopic Image Enhanced Deep Learning Algorithm for Inflammatory Bowel Disease (IBD) Polyp Detection: Feasibility Study2023 IEEE International Conference on Electro Information Technology (eIT) 2023; : 655 doi: 10.1109/eIT57321.2023.10187234
16
Peng-fei Lyu, Yu Wang, Qing-Xiang Meng, Ping-ming Fan, Ke Ma, Sha Xiao, Xun-chen Cao, Guang-Xun Lin, Si-yuan Dong. Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysisFrontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.955668
17
Shiv Bahadur, Prashant Kumar. Deep Learning for Targeted Treatments2022; : 229 doi: 10.1002/9781119857983.ch8
18
Sravani Kommuru, Faith Adekunle, Santiago Niño , Shamsul Arefin, Sai Prudhvi Thalvayapati, Dona Kuriakose, Yasmin Ahmadi, Suprada Vinyak, Zahra Nazir. Role of Artificial Intelligence in the Diagnosis of Gastroesophageal Reflux DiseaseCureus 2024;  doi: 10.7759/cureus.62206
19
Ian I. Lei, Gohar J. Nia, Elizabeth White, Hagen Wenzek, Santi Segui, Angus J. M. Watson, Anastasios Koulaouzidis, Ramesh P. Arasaradnam. Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made SimpleDiagnostics 2023; 13(6): 1038 doi: 10.3390/diagnostics13061038
20
Shuangyang Mo, Cheng Huang, Yingwei Wang, Huaying Zhao, Wenhong Wu, Haixing Jiang, Shanyu Qin. Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumorsFrontiers in Endocrinology 2024; 15 doi: 10.3389/fendo.2024.1383814