The article is a scoping review of the literature on the use of decision support systems based on artificial neural networks in emergency surgery. The authors present modern literature data on the effectiveness of artificial neural networks for predicting, diagnosing and treating abdominal emergency conditions: acute appendicitis, acute pancreatitis, acute cholecystitis, perforated gastric or duodenal ulcer, acute intestinal obstruction, and strangulated hernia. The intelligent systems developed at present allow a surgeon in an emergency setting, not only to check his own diagnostic and prognostic assumptions, but also to use artificial intelligence in complex urgent clinical cases. The authors summarize the main limitations for the implementation of artificial neural networks in surgery and medicine in general. These limitations are the lack of transparency in the decision-making process; insufficient quality educational medical data; lack of qualified personnel; high cost of projects; and the complexity of secure storage of medical information data. The development and implementation of decision support systems based on artificial neural networks is a promising direction for improving the forecasting, diagnosis and treatment of emergency surgical diseases and their complications.

中文翻译：

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紧急手术中基于人工神经网络的决策支持系统 WSES 项目。

这篇文章是对基于人工神经网络的决策支持系统在急诊手术中的应用的文献综述。作者介绍了人工神经网络在预测、诊断和治疗腹部紧急情况方面的有效性的现代文献数据：急性阑尾炎、急性胰腺炎、急性胆囊炎、穿孔的胃或十二指肠溃疡、急性肠梗阻和绞窄性疝。目前开发的智能系统允许外科医生在紧急情况下，不仅可以检查自己的诊断和预后假设，还可以在复杂的紧急临床病例中使用人工智能。作者总结了在外科和医学中实施人工神经网络的主要限制。这些限制是决策过程缺乏透明度；质量教育医疗数据不足；缺乏合格的人员；项目成本高；以及医疗信息数据安全存储的复杂性。基于人工神经网络的决策支持系统的开发和实现是提高急诊外科疾病及其并发症的预测、诊断和治疗的一个有前途的方向。