Artificial intelligence in endoscopic ultrasound-guided biliary drainage: Emerging applications, technical challenges, and future directions

Table 1 Potential applications of artificial intelligence in endoscopic ultrasound-guided biliary drainage

Stage of care	AI application	Data input	Potential clinical benefit	Current limitation	Ref.
Patient selection	Prediction of ERCP failure and suitability for EUS-BD	Clinical history, laboratory values, prior ERCP findings, CT/MRI findings	Earlier selection of EUS-BD and avoidance of repeated failed ERCP attempts	Requires large, validated therapeutic EUS datasets	[19,20]
Pre-procedural planning	Selection of drainage route, including CDS, HGS, antegrade stenting, or rendezvous drainage	CT, MRI/MRCP, EUS images, obstruction level, ductal dilatation, duodenal patency	Individualized selection of the safest and most feasible drainage strategy	Limited direct evidence in EUS-BD-specific populations	[20-23]
Anatomical recognition	Identification of bile duct, vessels, tumor, and gastrointestinal wall	Real-time EUS images, EUS video, Doppler EUS, cross-sectional imaging	Improved target recognition and safer access planning	Most current AI-EUS studies remain diagnostic or proof-of-concept	[24,25]
Route and trajectory guidance	Suggestion of puncture window and needle trajectory	EUS video, Doppler signal, duct-wall distance, device position	Reduced risk of bleeding, bile leak, and failed access	Requires real-time integration into EUS processors and devices	[26,27]
Device and stent deployment support	Recognition of guidewire, dilator, and stent position during deployment	EUS images, fluoroscopy, procedural video, device-tracking data	More precise stent placement and reduced maldeployment risk	Not yet established in routine clinical practice	[26,27]
Safety optimization	Prediction of bile leak, bleeding, cholangitis, stent migration, stent dysfunction, and reintervention	Clinical variables, coagulation profile, ascites, duct diameter, imaging and procedural data	Better consent, risk mitigation, and post-procedure monitoring	Needs prospective validation using clinically meaningful endpoints	[17,23]
Training and quality assurance	Landmark recognition, procedural phase detection, and objective performance feedback	EUS recordings, procedure videos, annotated landmarks, procedural metrics	Standardized training, competency assessment, and reduced operator variability	Requires expert-labelled datasets and standardized competency metrics	[18,24,25]
Post-procedural follow-up	Prediction of clinical failure, stent dysfunction, or need for reintervention	Symptoms, bilirubin trend, liver enzymes, imaging, prior procedural details	Earlier detection of treatment failure and timely reintervention	Minimal evidence currently available specifically for EUS-BD	[17,23,27]

AI: Artificial intelligence; BD: Biliary drainage; CDS: Choledochoduodenostomy; ERCP: Endoscopic retrograde cholangiopancreatography; EUS: Endoscopic ultrasound; HGS: Hepaticogastrostomy; MRI: Magnetic resonance imaging; MRCP: Magnetic resonance cholangiopancreatography.