Trial reportWorld journal of gastroenterology2023
Magnetic resonance imaging-based deep learning model to predict multiple firings in double-stapled colorectal anastomosis.
Trial report in World journal of gastroenterology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review and meta-analysis of preoperative MRI-based pelvimetry in predicting surgical difficulty in rectal cancer: Implications for personalised surgical planning.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2026Pooled it
- The impact of the number of stapler firings on anastomotic leakage in minimally invasive rectal surgery: risk factor or technical marker of complexity? A systematic review, meta‑analysis, and metaregression.Techniques in coloproctology · 2026Pooled it
- Beyond robotic platforms: artificial intelligence and the emergence of intelligent surgical ecosystems in colorectal surgery.Journal of robotic surgery · 2026Review
- Which Method Best Predicts Postoperative Complications: Deep Learning, Machine Learning, or Conventional Logistic Regression?Annals of gastroenterological surgery · 2026Review
- Artificial intelligence in the surgical management for colorectal cancer: current applications and future directions.Frontiers in oncology · 2026Review
- Artificial Intelligence in Gastrointestinal Surgery: A Systematic Review of Its Role in Laparoscopic and Robotic Surgery.Journal of personalized medicine · 2025Review
- Review
- Machine learning and deep learning to improve prevention of anastomotic leak after rectal cancer surgery.World journal of gastrointestinal surgery · 2025Article
- Precision at scale: Machine learning revolutionizing laparoscopic surgery.World journal of clinical oncology · 2024Article
- Nomogram for predicting the surgical difficulty of laparoscopic total mesorectal excision and exploring the technical advantages of robotic surgery.Frontiers in oncology · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMultiple linear stapler firings during double stapling technique (DST) after laparoscopic low anterior resection (LAR) are associated with an increased risk of anastomotic leakage (AL). However, it is difficult to predict preoperatively the need for multiple linear stapler cartridges during DST anastomosis.
aimTo develop a deep learning model to predict multiple firings during DST anastomosis based on pelvic magnetic resonance imaging (MRI).
methodsWe collected 9476 MR images from 328 mid-low rectal cancer patients undergoing LAR with DST anastomosis, which were randomly divided into a training set (
resultsThe prevalence of ≥ 3 linear stapler cartridges was 17.7% (58/328). The prevalence of AL was statistically significantly higher in patients with ≥ 3 cartridges compared to those with ≤ 2 cartridges (25.0%
conclusionMRI-based deep learning model can predict the use of ≥ 3 linear stapler cartridges during DST anastomosis in laparoscopic LAR surgery. This model might help determine the best anastomosis strategy by avoiding DST when there is a high probability of the need for ≥ 3 linear stapler cartridges.
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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.