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TruaceTracing the truth around AIMonday, August 17, 2026
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Health·G Space·Evidence-backed gain·Published 2026-08-07

The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis

Abstract: Background Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated chol…

TRV-2026-0679Peer-reviewedPermanent record — cite & verify
The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis

"The Endoscopy Suite" by Sam Blackman is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/.

The quick read

By August 2026, researchers published a systematic review and meta-analysis of AI combined with digital cholangioscopy for indeterminate and malignant biliary strictures. The analysis pooled five studies totaling 675 lesions and 2,685,674 images, finding pooled sensitivity of 95%, specificity of 88%, and SROC accuracy of 97% for AI-assisted diagnosis.

Accurate diagnosis of biliary strictures is clinically important because current ERCP and cholangioscopic sampling remain suboptimal, and improved computer-vision support could reduce missed malignancies. The findings remain preliminary given the small number of studies and lack of reported prospective clinical outcomes, implementation data, or harms.

Main points
  • Systematic review and meta-analysis included 5 studies covering 675 lesions and 2,685,674 cholangioscopic images.
  • Four of five studies used deep learning with a convoluted neural network processing 30 to 60 frames per second.
  • Bivariate model was used to compute pooled sensitivity, specificity, likelihood ratio, diagnostic odds ratio, and SROC curve.
  • Sensitivity analysis of CNN-only studies (538 patients) showed 95% sensitivity, 88% specificity, and 97% SROC accuracy.
Gain

AI-based machine learning applied to cholangioscopy images achieved high pooled diagnostic performance for indeterminate and malignant biliary strictures, with 95% sensitivity and 88% specificity.

The rundown

The review searched per PRISMA and MOOSE guidelines and applied Cochrane Diagnostic Test Accuracy methodology with a bivariate model to pool results across studies.

Most systems analyzed were CNN-based deep learning models operating at 30 to 60 frames per second, evaluated on a combined dataset of over 2.6 million cholangioscopic images from 675 lesions.

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