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TRUVACE RECORD VERSION
record: TRV-2026-0679
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-07T06:27:45.993391Z
status: published
lens: g_space
sector: health
headline: The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: pooled sensitivity and specificity were 95% (95% CI: 85-98) and 88% (95% CI: 76-94) | Artificial intelligence-based machine learning of cholangioscopy images appears to be a promising modality for the diagnosis of indeterminant and malignant biliary strictures
problem_reading: (none)
problem_evidence: (none)
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.
limitation: Evidence base is limited to five studies with 675 lesions, indicating early-stage evaluation rather than broad clinical validation.
tag: Evidence-backed gain
key_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.
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.
sources:
- peer_reviewed | Journal of Clinical Gastroenterology | https://doi.org/10.1097/mcg.0000000000002148 | 2026-08-06
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