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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: 36998424 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
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
5
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