ArticleJournal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract2022
Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study.
Article in Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictions of postoperative and perioperative complications of laparoscopic cholecystectomy using machine learning algorithms: systematic review.BMC surgery · 2025Pooled it
- Long-term outcomes and quality of life after surgical repair of benign biliary stricture following bile duct injury during cholecystectomy.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2026Article
- Management of Iatrogenic Bile-Duct Injury After Cholecystectomy, 1995-2025: Systematic Review and Meta-Analysis.Life (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Gastrointestinal Surgery: A Systematic Review of Its Role in Laparoscopic and Robotic Surgery.Journal of personalized medicine · 2025Review
- Minimally invasive surgery for post cholecystectomy biliary stricture: current evidence and future perspectives.World journal of gastrointestinal surgery · 2023Review
Corrections and comments
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Authors and funding
36 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIatrogenic bile duct injury (IBDI) is a challenging surgical complication. IBDI management can be guided by artificial intelligence models. Our study identified the factors associated with successful initial repair of IBDI and predicted the success of definitive repair based on patient risk levels.
methodsThis is a retrospective multi-institution cohort of patients with IBDI after cholecystectomy conducted between 1990 and 2020. We implemented a decision tree analysis to determine the factors that contribute to successful initial repair and developed a risk-scoring model based on the Comprehensive Complication Index.
resultsWe analyzed 748 patients across 22 hospitals. Our decision tree model was 82.8% accurate in predicting the success of the initial repair. Non-type E (p < 0.01), treatment in specialized centers (p < 0.01), and surgical repair (p < 0.001) were associated with better prognosis. The risk-scoring model was 82.3% (79.0-85.3%, 95% confidence interval [CI]) and 71.7% (63.8-78.7%, 95% CI) accurate in predicting success in the development and validation cohorts, respectively. Surgical repair, successful initial repair, and repair between 2 and 6 weeks were associated with better outcomes. DISCUSSION: Machine learning algorithms for IBDI are a novel tool may help to improve the decision-making process and guide management of these patients.
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