An artificial intelligence difficulty scoring system for stone removal during ERCP: a prospective validation

Abstract

Background A computer-assisted (CAD) system was developed to assess, score, and classify the technical difficulty of common bile duct (CBD) stone removal during endoscopic retrograde cholangiopancreatography (ERCP). The efficacy of the CAD system was subsequently assessed through a multicenter, prospective, observational study. Method All patients who met the inclusion criteria were included. Based on cholangiogram images, the CAD system analyzed the level of difficulty of stone removal and classified it into “difficult” and “easy” groups. Subsequently, differences in clinical endpoints, including attempts at stone extraction, stone extraction time, total operation time, and stone clearance rates were compared between the two groups. Results 173 patients with CBD stones from three hospitals were included in the study. The group classified as difficult by CAD had more extraction attempts (7.20 vs. 4.20, P < 0.001), more frequent machine lithotripsy (30.4 % vs. 7.1 %, P < 0.001), longer stone extraction time (16.59 vs. 7.69 minutes, P < 0.001), lower single-session stone clearance rate (73.9 % vs. 94.5 %, P < 0.001), and lower total stone clearance rate (89.1 % vs. 97.6 %, P = 0.019) compared with the group classified as easy by CAD. Conclusion The CAD system effectively assessed and classified the degree of technical difficulty in endoscopic stone extraction during ERCP. In addition, it automatically provided a quantitative evaluation of CBD and stones, which in turn could help endoscopists to apply suitable procedures and interventional methods to minimize the possible risks associated with endoscopic stone removal.