Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2026
Classification of Biliary Strictures Using Real-Time Cholangioscopy Artificial Intelligence: The SMART-AI Trial.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Marya Neil B, Powers Patrick D, Marcello Matthew, Rau Prashanth, Nasser-Ghodsi Navine, Marshall Christopher, Zivny Jaroslav, AbiMansour Jad P, Chandrasekhara Vinay
Аннотация
BACKGROUND & AIMS: Sampling techniques have poor accuracy for classifying biliary strictures as benign or malignant. Previously, a cholangioscopy artificial intelligence (AI) outperformed sampling techniques based solely on analysis of previously recorded cholangioscopy footage. The aim of this single-center, prospective trial was to compare the performance of a real-time cholangioscopy AI to both sampling techniques and human observers for the task of biliary stricture classification.
METHODS: A cholangioscopy AI computer connected directly to a cholangioscope console. The computer analyzed the cholangioscopy video stream during procedures for suspected biliary strictures. The primary outcome of the study was comparison of the performance of cholangioscopy AI to sampling techniques-brush cytology and transpapillary forceps biopsy-for biliary stricture classification. Secondary outcomes included comparison of the AI classification performance to that of 14 human observers (separated into junior-level and experienced-level cohorts) who reviewed the cholangioscopy footage.
RESULTS: A total of 41 patients were enrolled in the trial and had biliary strictures analyzed by cholangioscopy AI. For the classification of strictures, the AI had greater classification accuracy than standard sampling techniques (87.8% vs 67.4%; P = .043). Additionally, the cholangioscopy AI was significantly more accurate for biliary stricture classification than both junior-level (87.8% vs 61.5%; P = .001) and experienced endoscopists (87.8% vs 63.1%; P = .011).
CONCLUSIONS: This trial demonstrates that sampling techniques and human assessment of biliary strictures are flawed and there may be a benefit to the use of a cholangioscopy AI system to aid in biliary stricture classification.
CLINICALTRIALS: gov, Number: NCT05779436.
METHODS: A cholangioscopy AI computer connected directly to a cholangioscope console. The computer analyzed the cholangioscopy video stream during procedures for suspected biliary strictures. The primary outcome of the study was comparison of the performance of cholangioscopy AI to sampling techniques-brush cytology and transpapillary forceps biopsy-for biliary stricture classification. Secondary outcomes included comparison of the AI classification performance to that of 14 human observers (separated into junior-level and experienced-level cohorts) who reviewed the cholangioscopy footage.
RESULTS: A total of 41 patients were enrolled in the trial and had biliary strictures analyzed by cholangioscopy AI. For the classification of strictures, the AI had greater classification accuracy than standard sampling techniques (87.8% vs 67.4%; P = .043). Additionally, the cholangioscopy AI was significantly more accurate for biliary stricture classification than both junior-level (87.8% vs 61.5%; P = .001) and experienced endoscopists (87.8% vs 63.1%; P = .011).
CONCLUSIONS: This trial demonstrates that sampling techniques and human assessment of biliary strictures are flawed and there may be a benefit to the use of a cholangioscopy AI system to aid in biliary stricture classification.
CLINICALTRIALS: gov, Number: NCT05779436.