| For: | Li DL, Zhu L, Liu SL, Wang ZB, Liu JN, Zhou XM, Hu JL, Liu RQ. Machine learning-based radiomic nomogram from unenhanced computed tomography and clinical data predicts bowel resection in incarcerated inguinal hernia. World J Gastrointest Surg 2025; 17(6): 106155 [PMID: 40584479 DOI: 10.4240/wjgs.v17.i6.106155] |
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| URL: | https://www.wjgnet.com/1948-9366/full/v17/i6/106155.htm |
| Number | Citing Articles |
| 1 |
Yongzheng Xie, Haiyun Deng, Zhaohui Shi, Meng Wang, Binjie Wang, Changbo Li, Li Zheng, Nikhat Kaura. Predicting Strangulation Risk in Acute Irreducible Inguinal Hernias: Development and Validation of an Integrated Model Using Inflammatory Markers and Imaging Features. International Journal of Clinical Practice 2026; 2026(1) doi: 10.1155/ijcp/4920030
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| 2 |
Yang Liu, Tian-Hao Xie, Yan Fu, Si-Ning Ha, Xiao-Shi Jin, Xiang-Xiang Ren. Application of machine learning and deep learning in the diagnosis and treatment of inguinal hernia: a narrative review. Frontiers in Medicine 2026; 13 doi: 10.3389/fmed.2026.1743178
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