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] |
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URL: | https://www.wjgnet.com/1009-3079/full/v29/i3/536.htm |
编号 | Citing Articles |
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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 surgery. World Journal of Gastrointestinal Surgery 2025; 17(1): 101772 doi: 10.4240/wjgs.v17.i1.101772
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Mohamed Khalifa, Mona Albadawy. Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions. Computer Methods and Programs in Biomedicine Update 2024; 5: 100148 doi: 10.1016/j.cmpbup.2024.100148
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Carlos M Ardila, Daniel González-Arroyave. Precision at scale: Machine learning revolutionizing laparoscopic surgery. World Journal of Clinical Oncology 2024; 15(10): 1256-1263 doi: 10.5306/wjco.v15.i10.1256
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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 surgery. Frontiers in Oncology 2024; 14 doi: 10.3389/fonc.2024.1303686
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