"Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania.
Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania.
Updates Surg. 2022 Apr;74(2):417-429. doi: 10.1007/s13304-022-01255-z. Epub 2022 Mar 2.
Artificial intelligence (AI), including machine learning (ML), is being slowly incorporated in medical practice, to provide a more precise and personalized approach. Pancreatic surgery is an evolving field, which offers the only curative option for patients with pancreatic cancer. Increasing amounts of data are available in medicine: AI and ML can help incorporate large amounts of information in clinical practice. We conducted a systematic review, based on PRISMA criteria, of studies that explored the use of AI or ML algorithms in pancreatic surgery. To our knowledge, this is the first systematic review on this topic. Twenty-five eligible studies were included in this review; 12 studies with implications in the preoperative diagnosis, while 13 studies had implications in patient evolution. Preoperative diagnosis, such as predicting the malignancy of IPMNs, differential diagnosis between pancreatic cystic lesions, classification of different pancreatic tumours, and establishment of the correct management for each of these lesions, can be facilitated through different AI or ML algorithms. Postoperative evolution can also be predicted, and some studies reported prediction models for complications, including postoperative pancreatic fistula, while other studies have analysed the implications for prognosis evaluation (from predicting a textbook outcome, the risk of metastasis or relapse, or the mortality rate and survival). One study discussed the possibility of predicting an intraoperative complication-massive intraoperative bleeding. Artificial intelligence and machine learning models have promising applications in pancreatic surgery, in the preoperative period (high-accuracy diagnosis) and postoperative setting (prognosis evaluation and complication prediction), and the intraoperative applications have been less explored.
人工智能（AI），包括机器学习（ML），正在慢慢被整合到医学实践中，以提供更精确和个性化的方法。胰腺手术是一个不断发展的领域，为胰腺癌患者提供了唯一的治愈选择。医学领域有越来越多的数据可用：AI 和 ML 可以帮助将大量信息纳入临床实践。我们根据 PRISMA 标准，对探索 AI 或 ML 算法在胰腺外科中的应用的研究进行了系统回顾。据我们所知，这是该主题的第一个系统综述。这项综述共纳入了 25 项符合条件的研究；其中 12 项研究对术前诊断有影响，而 13 项研究对患者的演变有影响。术前诊断，如预测 IPMN 的恶性程度、胰腺囊性病变的鉴别诊断、不同胰腺肿瘤的分类，以及为这些病变制定正确的治疗方案，都可以通过不同的 AI 或 ML 算法来实现。术后的演变也可以预测，一些研究报告了并发症的预测模型，包括术后胰瘘，而其他研究则分析了对预后评估的影响（从预测教科书式的结果、转移或复发的风险，或死亡率和生存率）。一项研究讨论了预测术中并发症——大量术中出血的可能性。人工智能和机器学习模型在胰腺外科中有很好的应用前景，包括术前（高精度诊断）和术后（预后评估和并发症预测），而术中应用的研究还比较少。
