Artificial intelligence in gastroenterology practice: Current perspectives and emerging prospects

Table 6 Studies evaluating applications of artificial intelligence in biliary diseases

Ref.	Clinical application	Data type	AI method	Sample size	Study design, center design, and validation design	Major performance metric	Principal limitation
Zhang et al[94]	Choledocholithiasis prediction	Preoperative clinical predictors	ModelArts	1199 patients	Retrospective, single-center, internal validation	Accuracy 0.97, recall 0.97, precision 0.971	Retrospective design may limit generalizability to real-world settings
Sugimoto et al[96]	Distal bile duct obstruction, benign vs malignant	Clinical laboratory biomarkers	LightGBM	206 patients, 64 malignant, 142 benign, 16 excluded for missing values	Retrospective, single-center, internal validation with 10-fold cross-validation	AUROC 0.908, sensitivity 83.1%, specificity 87.2%, AUROC significantly higher than individual biomarkers	Small sample size, exclusion of markers such as CA19-9 because of missing data
Saraiva et al[99]	Biliary stricture, benign vs malignant	Digital single-operator cholangioscopy images	CNN	85 patients, 11855 images	Retrospective pilot study, single-center, internal validation with 5-fold cross-validation	Accuracy 94.9%, sensitivity 94.7%, specificity 92.1%, AUC 0.988	Small patient cohort, static images rather than full videos
Saraiva et al[100]	Malignant biliary stricture and morphologic characterization	Digital single-operator cholangioscopy images	ResNet-based CNN	129 DSOC examinations, 84994 images	Retrospective, multicenter, internal validation	Overall accuracy 82.9%, AUROC 0.92, tumor vessel detection AUC 0.98, papillary projection detection AUC 0.96	Tested on a single DSOC platform, technological maturity remains preclinical
Brenner et al[101]	Post-ERCP pancreatitis prediction	Patient-level data from 12 RCTs	Gradient-boosted machine	7389 patients for development, 135 patients for pilot	Retrospective development and prospective pilot, multicenter development and single-center pilot, internal and prospective validation	AUC 0.70 (internal cross-validation), AUC 0.74 (prospective pilot)	Low PPV of 15%, trial data may not reflect real-world populations
Archibugi et al[102]	Post-ERCP pancreatitis prediction	Clinical preprocedural and procedural factors	Gradient boosting	1150 patients	Prospective multicenter international cohort, proof-of-concept, internal validation	AUC 0.70 (cross-validation), AUC 0.671 (internal test set)	Moderate predictive accuracy in the final test set
Jin et al[103]	Post-ERCP cholangitis prediction in malignant biliary obstruction	Clinical laboratory and procedural data	XGBoost	1421 patients, derivation cohort 1026 (718 training, 308 testing), external validation 395	Retrospective, multicenter, internal and external validation	AUC 0.7670 (internal test), AUC 0.7270 (external validation cohort)	Retrospective design, external validation cohort restricted to the same geographic region
Chen et al[104]	Post-ERCP pancreatitis prediction	Papilla radiomics from endoscopic images and clinical data	DL and ML algorithms	2372 patients	Retrospective multicohort, multicenter, internal validation	AUC 0.825-0.857 (internal validation and testing cohorts)	Retrospective enrollment, focus on surface morphology rather than internal structural factors
Kim et al[105]	Ampulla detection and cannulation difficulty prediction	Endoscopic images	CNN with U-Net and ResNet	531 patients for difficulty, 451 for detection	Retrospective, multicenter, internal validation with 5-fold cross-validation	mIoU 64.1% for detection, recall 61.1% for difficult cases	Low recall for difficult cases, lack of radiologic data to capture internal ampullary structure

AI: Artificial intelligence; AUROC: Area under the receiver operating characteristic curve; CA19-9: Carbohydrate antigen 19-9; CNN: Convolutional neural network; AUC: Area under the curve; DSOC: Digital single-operator cholangioscopy; ERCP: Endoscopic retrograde cholangiopancreatography; XGBoost: Extreme gradient boosting; DL: Deep learning; ML: Machine learning; mIoU: Mean intersection over union; LightGBM: Light Gradient Boosting Machine; PPV: Positive predictive value; RCT: Randomized controlled trial; ResNet: Residual neural network; U-Net: U-shaped convolutional neural network.