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Cited by in CrossRef
For: Cai ZH, Zhang Q, Fu ZW, Fingerhut A, Tan JW, Zang L, Dong F, Li SC, Wang SL, Ma JJ. Magnetic resonance imaging-based deep learning model to predict multiple firings in double-stapled colorectal anastomosis. World J Gastroenterol 2023; 29(3): 536-548 [PMID: 36688017 DOI: 10.3748/wjg.v29.i3.536]
URL: https://www.wjgnet.com/1007-9327/full/v29/i3/536.htm
Number Citing Articles
1
Ludovica Gorini, Roberto de la Plaza Llamas, Daniel Alejandro Díaz Candelas, Rodrigo Arellano González, Wenzhong Sun, Jaime García Friginal, María Fra López, Ignacio Antonio Gemio del Rey. Artificial Intelligence in Gastrointestinal Surgery: A Systematic Review of Its Role in Laparoscopic and Robotic SurgeryJournal of Personalized Medicine 2025; 15(11): 562 doi: 10.3390/jpm15110562
2
Francesco Celotto, Quoc R Bao, Giulia Capelli, Gaya Spolverato, Andrew A Gumbs. Machine learning and deep learning to improve prevention of anastomotic leak after rectal cancer surgeryWorld Journal of Gastrointestinal Surgery 2025; 17(1): 101772 doi: 10.4240/wjgs.v17.i1.101772
3
Mohamed Khalifa, Mona Albadawy. Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential FunctionsComputer Methods and Programs in Biomedicine Update 2024; 5: 100148 doi: 10.1016/j.cmpbup.2024.100148
4
Carlos M Ardila, Daniel González-Arroyave. Precision at scale: Machine learning revolutionizing laparoscopic surgeryWorld Journal of Clinical Oncology 2024; 15(10): 1256-1263 doi: 10.5306/wjco.v15.i10.1256
5
Ahmad Alshammari, Ali Boabbas, Bader Nassar , Amal Shaikhah. The Role of Artificial Intelligence in General Surgery: A Systematic Review and Meta-Analysis of Machine Learning Applications in Colorectal Cancer Treatment OutcomesCureus 2025;  doi: 10.7759/cureus.96919
6
Fangliang Guo, Cong Xia, Zongheng Wang, Ruiqi Wang, Jianfeng Gao, Yue Meng, Jiahao Pan, Qianshi Zhang, Shuangyi Ren. Nomogram for predicting the surgical difficulty of laparoscopic total mesorectal excision and exploring the technical advantages of robotic surgeryFrontiers in Oncology 2024; 14 doi: 10.3389/fonc.2024.1303686
7
Ryosuke Fukuyo, Masanori Tokunaga, Hiroyuki Yamamoto, Hideki Ueno, Yusuke Kinugasa. Which Method Best Predicts Postoperative Complications: Deep Learning, Machine Learning, or Conventional Logistic Regression?Annals of Gastroenterological Surgery 2025;  doi: 10.1002/ags3.70145