ArticleWorld journal of gastrointestinal surgery2025
Machine learning and deep learning to improve prevention of anastomotic leak after rectal cancer surgery.
Article in World journal of gastrointestinal surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Metabolic dynamic score and machine learning: a novel approach to predicting pathological complete response in rectal cancer after neoadjuvant chemoradiotherapy.Frontiers in oncology · 2026Article
- The gut microbiome in colorectal anastomotic leakage: from mechanisms to precision.Frontiers in medicine · 2026Review
- Mechanomedicine in digestive surgery: a theranostic framework integrating mechanical diagnostics and therapeutic modulation across the perioperative continuum.Theranostics · 2026Review
- Taming colonic anastomotic leakage: Wisdom from the ancient Chinese legend of Yu the Great.World journal of gastrointestinal surgery · 2025Article
- From static thresholds to dynamic trends: Reassessing serum calcium in anastomotic leakage prediction.World journal of gastrointestinal surgery · 2025Article
- Accuracy and Reliability of Artificial Intelligence in Surgical Decision-Making: A Literature Review.Cureus · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anastomotic leakage (AL) is a significant complication following rectal cancer surgery, adversely affecting both quality of life and oncological outcomes. Recent advancements in artificial intelligence (AI), particularly machine learning and deep learning, offer promising avenues for predicting and preventing AL. These technologies can analyze extensive clinical datasets to identify preoperative and perioperative risk factors such as malnutrition, body composition, and radiological features. AI-based models have demonstrated superior predictive power compared to traditional statistical methods, potentially guiding clinical decision-making and improving patient outcomes. Additionally, AI can provide surgeons with intraoperative feedback on blood supply and anatomical dissection planes, minimizing the risk of intraoperative complications and reducing the likelihood of AL development.
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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.