Artificial intelligence for fluoroscopic cholangiogram interpretation during endoscopic retrograde cholangiopancreatography

Table 1 Artificial intelligence applications relevant to fluoroscopic cholangiogram interpretation and peri-procedural endoscopic retrograde cholangiopancreatography imaging

Clinical task	Imaging input	AI approach	Best reported performance	Dataset and validation	Readiness
Malignant vs benign stricture characterization[4]	Fluoroscopic cholangiogram	Classification CNN + saliency mapping	AUROC 0.89 (internal); 0.72-0.76 (external)	251 patients, 3 centers; internal CV + 2 external cohorts	Experimental-externally validated
CBD stone and duct segmentation[5]	Fluoroscopic cholangiogram	Semantic segmentation (D-LinkNet/U-Net)	mIoU 86.4% (duct), 68.4% (stone)	1,954 cholangiograms, 3 hospitals; internal validation	Experimental-multicenter
Stone-extraction difficulty scoring[5]	Fluoroscopic cholangiogram	Segmentation-derived scoring scale	Score ≥ 2 → 36% vs 86% complete clearance	As above	Experimental-multicenter
Biliary stent-length selection[15]	Fluoroscopic cholangiogram	Segmentation and geometric measurement	104 of 121 selections within 1 cm; mean absolute error 0.81 cm; dose-area product reduced by about 202 mGy.cm2	794 images from 431 patients; model development and validation	Experimental-assistive use only
Ampulla localization and cannulation-difficulty classification[14]	White-light papillary image	Classification/detection CNN	Ampulla mIoU 64.1%; difficulty recall 719% (easy)/61.1% (difficult)	531/451 patients, single center; internal	Experimental-proof-of-concept
Real-time papilla and cannula navigation[9]	White-light papillary image	Swin-transformer detector (4STDH)	mAP 93.2%; outputs cannula distance/direction	1840 images; single dataset + video test	Experimental-proof-of-concept
Post-ERCP pancreatitis prediction[10]	White-light papillary image	Papilla radiomics + machine learning	AUC: 0.825-0.857	2372 patients, 2 centers; multicohort validation	Experimental-multicohort

AI: Artificial intelligence; AUC: Area under the curve; AUROC: Area under the receiver-operating-characteristic curve; CBD: Common bile duct; CNN: Convolutional neural network; CV: Cross-validation; ERCP: Endoscopic retrograde cholangiopancreatography; mAP: Mean average precision; mIoU: Mean intersection-over-union.