ArticleThe American surgeon2023
Prediction of Ureteral Injury During Colorectal Surgery Using Machine Learning.
Article in The American surgeon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- The ACPGBI AI taskforce report: A mixed-methods roadmap for AI in colorectal surgery.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2025Article
- Deep learning-based intraoperative visual guidance model for ureter identification in laparoscopic sigmoidectomy.Surgical endoscopy · 2025Article
- 90-day mortality prediction in elective visceral surgery using machine learning: a retrospective multicenter development, validation, and comparison study.International journal of surgery (London, England) · 2025Article
- The Role of Artificial Intelligence in Urogynecology: Current Applications and Future Prospects.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
- Serological Biomarker-Based Machine Learning Models for Predicting the Relapse of Ulcerative Colitis.Journal of inflammation research · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundUreteral injury (UI) is a rare but devastating complication during colorectal surgery. Ureteral stents may reduce UI but carry risks themselves. Risk predictors for UI could help target the use of stents, but previous efforts have relied on logistic regression (LR), shown moderate accuracy, and used intraoperative variables. We sought to use an emerging approach in predictive analytics, machine learning, to create a model for UI.
methodsPatients who underwent colorectal surgery were identified in the National Surgical Quality Improvement Program (NSQIP) database. Patients were split into training, validation, and test sets. The primary outcome was UI. Three machine learning approaches were tested including random forest (RF), gradient boosting (XGB), and neural networks (NN), and compared with traditional LR. Model performance was assessed using area under the curve (AUROC).
resultsThe data set included 262,923 patients, of whom 1519 (.578%) experienced UI. Of the modeling techniques, XGB performed the best, with an AUROC score of .774 (95% CI .742-.807) compared with .698 (95% CI .664-.733) for LR. Random forest and NN performed similarly with scores of .738 and .763, respectively. Type of procedure, work RVUs, indication for surgery, and mechanical bowel prep showed the strongest influence on model predictions.
conclusionsMachine learning-based models significantly outperformed LR and previous models and showed high accuracy in predicting UI during colorectal surgery. With proper validation, they could be used to support decision making regarding the placement of ureteral stents preoperatively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.