Copyright: ©Author(s) 2026.
Artif Intell Gastroenterol. Aug 8, 2026; 7(2): 118476
Published online Aug 8, 2026. doi: 10.35712/aig.118476
Published online Aug 8, 2026. doi: 10.35712/aig.118476
Figure 1
Flow chart of the literature search.
Figure 2 Artificial intelligence frameworks for indeterminate pancreatobiliary stricture diagnosis.
CA19-9: Carbohydrate antigen 19-9; CNN: Convolutional neural network; EUS: Endoscopic ultrasound; MRCP: Magnetic resonance cholangiopancreatography.
Figure 3 Artificial intelligence assisted pre-endoscopic retrograde cholangiopancreaticography risk stratification based on discriminative performance.
AI: Artificial intelligence; AUROC: Area under the receiver operating characteristic curve; MRCP: Magnetic resonance cholangiopancreatography; EUS: Endoscopic ultrasound; ERCP: Endoscopic retrograde cholangiopancreatography; D-SOC: Digital single-operator cholangioscopy; FNB: Fine needle biopsy.
Figure 4 Endoscopic ultrasound precision cascade.
AI: Artificial intelligence; CNN: Convolutional neural network; D-SOC: Digital single operator cholangioscopy; EUS: Endoscopic ultrasound; ERCP: Endoscopic retrograde cholangiopancreatography.
Figure 5 Artificial intelligence-driven precision diagnostic cascade for biliary strictures.
AI: Artificial intelligence; AUROC: Area under the receiver operating characteristic curve; MRCP: Magnetic resonance cholangiopancreatography; ERCP: Endoscopic retrograde cholangiopancreatography; D-SOC: Digital single-operator cholangioscopy; EUS: Endoscopic ultrasound; FNB: Fine needle biopsy.
- Citation: Majeed AA, Butt AS. Leveraging artificial intelligence to differentiate benign from malignant biliary strictures: A step toward precision diagnosis. Artif Intell Gastroenterol 2026; 7(2): 118476
- URL: https://www.wjgnet.com/2644-3236/full/v7/i2/118476.htm
- DOI: https://dx.doi.org/10.35712/aig.118476