Celotto Francesco, Bao Quoc R, Capelli Giulia, Spolverato Gaya, Gumbs Andrew A
Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, Padova 35128, Veneto, Italy.
Department of Surgery, Azienda Socio Sanitaria Territoriale Bergamo Est, Bergamo 24068, Lombardy, Italy.
World J Gastrointest Surg. 2025 Jan 27;17(1):101772. doi: 10.4240/wjgs.v17.i1.101772.
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.
吻合口漏（AL）是直肠癌手术后的一种严重并发症，对生活质量和肿瘤学结局均产生不利影响。人工智能（AI）的最新进展，尤其是机器学习和深度学习，为预测和预防AL提供了有前景的途径。这些技术可以分析大量临床数据集，以识别术前和围手术期的风险因素，如营养不良、身体成分和放射学特征。与传统统计方法相比，基于AI的模型已显示出卓越的预测能力，有可能指导临床决策并改善患者结局。此外，AI可以为外科医生提供有关血供和解剖分离平面的术中反馈，将术中并发症的风险降至最低，并降低发生AL的可能性。
