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Cited by in CrossRef
For: Berbís MA, Paulano Godino F, Royuela del Val J, Alcalá Mata L, Luna A. Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver. World J Gastroenterol 2023; 29(9): 1427-1445 [PMID: PMC10044858 DOI: 10.3748/wjg.v29.i9.1427]
URL: https://www.wjgnet.com/1007-9327/full/v29/i9/1427.htm
Number Citing Articles
1
Jiaju Yin, Tianrui Cui, Yi Yang, Tian-Ling Ren. Sensing of Digestive Enzymes—Diagnosis and Monitoring of PancreatitisChemosensors 2023; 11(9): 469 doi: 10.3390/chemosensors11090469
2
Reabal Najjar. Redefining Radiology: A Review of Artificial Intelligence Integration in Medical ImagingDiagnostics 2023; 13(17): 2760 doi: 10.3390/diagnostics13172760
3
Hardik Patel, Theodoros Zanos, D. Brock Hewitt. Deep Learning Applications in Pancreatic CancerCancers 2024; 16(2): 436 doi: 10.3390/cancers16020436
4
Ashley Bond, Kevin Mccay, Simon Lal. Artificial intelligence & clinical nutrition: What the future might have in storeClinical Nutrition ESPEN 2023; 57: 542 doi: 10.1016/j.clnesp.2023.07.082
5
Federica Flammia, Roberta Fusco, Sonia Triggiani, Giuseppe Pellegrino, Alfonso Reginelli, Igino Simonetti, Piero Trovato, Sergio Venanzio Setola, Giuseppe Petralia, Antonella Petrillo, Francesco Izzo, Vincenza Granata. Risk Assessment and Radiomics Analysis in Magnetic Resonance Imaging of Pancreatic Intraductal Papillary Mucinous Neoplasms (IPMN)Cancer Control 2024; 31 doi: 10.1177/10732748241263644
6
Mark R. Loper, Mina S. Makary. Evolving and Novel Applications of Artificial Intelligence in Abdominal ImagingTomography 2024; 10(11): 1814 doi: 10.3390/tomography10110133
7
Elena Ramírez-Maldonado, Sandra López Gordo, Rosa Jorba. Acute and Chronic Pancreatitis [Working Title]2025;  doi: 10.5772/intechopen.1008988
8
Julia Arribas Anta, Juan Moreno-Vedia, Javier García López, Miguel Angel Rios-Vives, Josep Munuera, Júlia Rodríguez-Comas. Artificial intelligence for detection and characterization of focal hepatic lesions: a reviewAbdominal Radiology 2024;  doi: 10.1007/s00261-024-04597-x
9
Nicoleta Podină, Elena Codruța Gheorghe, Alina Constantin, Irina Cazacu, Vlad Croitoru, Cristian Gheorghe, Daniel Vasile Balaban, Mariana Jinga, Cristian George Țieranu, Adrian Săftoiu. Artificial Intelligence in Pancreatic Imaging: A Systematic ReviewUnited European Gastroenterology Journal 2025;  doi: 10.1002/ueg2.12723
10
Surenth Nalliah, Esben Bolvig Mark, Søren Schou Olesen, Tine Maria Hansen, Jens Brøndum Frøkjær. Current Trends and Developments in Radiologic Assessment of Chronic PancreatitisCurrent Treatment Options in Gastroenterology 2024; 22(4): 302 doi: 10.1007/s11938-024-00447-3
11
Kai Liu, Qing Li, Xingxing Wang, Caixia Fu, Haitao Sun, Caizhong Chen, Mengsu Zeng. Feasibility of deep learning-reconstructed thin-slice single-breath-hold HASTE for detecting pancreatic lesions: A comparison with two conventional T2-weighted imaging sequencesResearch in Diagnostic and Interventional Imaging 2024; 9: 100038 doi: 10.1016/j.redii.2023.100038
12
Yasunari Matsuzaka, Ryu Yashiro. The Diagnostic Classification of the Pathological Image Using Computer VisionAlgorithms 2025; 18(2): 96 doi: 10.3390/a18020096
13
Nicholas Bull, Janindu Goonawardena, Lina Hua, Dee Lim, King Tung Cheung, Vivek Ramachandran, Adrian Fox, Sayed Hassen. Measurement of the distal bile duct density on computed tomography can differentiate choledocholithiasis from a control populationANZ Journal of Surgery 2024; 94(12): 2195 doi: 10.1111/ans.19189