Francesco Celotto, Quoc R Bao, Giulia Capelli, Gaya Spolverato, Andrew A Gumbs
{"title":"Machine learning and deep learning to improve prevention of anastomotic leak after rectal cancer surgery.","authors":"Francesco Celotto, Quoc R Bao, Giulia Capelli, Gaya Spolverato, Andrew A Gumbs","doi":"10.4240/wjgs.v17.i1.101772","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":23759,"journal":{"name":"World Journal of Gastrointestinal Surgery","volume":"17 1","pages":"101772"},"PeriodicalIF":1.8000,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11757192/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Journal of Gastrointestinal Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.4240/wjgs.v17.i1.101772","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0
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.