Copyright
©The Author(s) 2025.
World J Gastroenterol. Dec 28, 2025; 31(48): 112683
Published online Dec 28, 2025. doi: 10.3748/wjg.v31.i48.112683
Published online Dec 28, 2025. doi: 10.3748/wjg.v31.i48.112683
Figure 1 Flow chart of YOLOv7 model construction and validation.
AI: Artificial intelligence.
Figure 2 Comparison of different methods in identifying rectal neuroendocrine tumors.
ROC: Receiver operating characteristic; AUC: Area under the curve; CI: Confidence interval.
Figure 3 Total right.
rNET: Rectal neuroendocrine tumor.
Figure 4 Error both, artificial intelligence, and endoscopist.
A: Error both; B: Error-artificial intelligence; C: Error-endoscopist. rNET: Rectal neuroendocrine tumor.
- Citation: Liu K, Wang ZY, Yi LZ, Li F, He SH, Zhang XG, Lai CX, Li ZJ, Qiu L, Zhang RY, Wu W, Lin Y, Yang H, Liu GM, Guan QS, Zhao ZF, Cheng LM, Dai J, Bai Y, Xie F, Zhang MN, Chen SZ, Zhong XF. Artificial intelligence-assisted diagnosis of rectal neuroendocrine tumors during white-light endoscopy. World J Gastroenterol 2025; 31(48): 112683
- URL: https://www.wjgnet.com/1007-9327/full/v31/i48/112683.htm
- DOI: https://dx.doi.org/10.3748/wjg.v31.i48.112683