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Cited by in CrossRef
For: Akbulut S, Kucukakcali Z, Colak C. Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications. World J Gastroenterol 2025; 31(43): 112000 [PMID: 41358178 DOI: 10.3748/wjg.v31.i43.112000]
URL: https://www.wjgnet.com/2307-8960/full/v31/i43/112000.htm
Number Citing Articles
1
Margarita L. Martinez-Fierro, Jose G. Gonzalez-Rodarte, Sodel Vazquez-Reyes, Manuel Gonzalez-Plascencia, Idalia Garza-Veloz, Perla Velasco-Elizondo, Sidere M. Zorrilla-Alfaro, Jaime Y. Burciaga-Paez, Gonzalo Ibarra-Bañuelos, Luis A. Flores-Chaires, Alejandro Mauricio-Gonzalez. Usefulness of Laboratory-Based Machine Learning for Detection and Severity Classification of Acute Appendicitis in a Resource-Limited Healthcare SettingDiagnostics 2026; 16(7): 1090 doi: 10.3390/diagnostics16071090
2
Erkan Karacan, H. Mehmet Kayili. Machine learning-based diagnostic modeling for differentiating lymphoid hyperplasia from acute appendicitis using laboratory biomarkersThe American Journal of Surgery 2026; 256: 116925 doi: 10.1016/j.amjsurg.2026.116925
3
Xuhong Zhang, Xiaoyu Wang, Hong Zhu, Huiwen Yu, Lin Fu, Wanyi Shu, Xihang Ye, Zhixin Lim, Saboor Saeed, Ha Linh Nguyen, Julius Pistoor, Dazhong Rong, Jianbo Lai, Zheng Wang, Yang He, Yueming Wang, Ye Shen, Yudong Zhou, Shaohua Hu, Jianping Tong. Eye–brain axis: Ocular and visual pathophysiology as driver and therapeutic target across the mood disorder trajectoryProgress in Retinal and Eye Research 2026; 112: 101467 doi: 10.1016/j.preteyeres.2026.101467
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