机器学习,深度学习和的手术. 我们是否在推动腹部核心健康的极限? 一个定性系统审查
D L Lima1, J Kasakewitch2, D Q Nguyen3
1Department of Surgery, Montefiore Medical Center, New York, NY, USA. dilaurentino@gmail.com.
概括
机器学习和人工智能在手术中表现有前途,可以预测患者的结果并识别并发症. 本综述综合了关于这些先进工具的最新发现,用于腹壁重建.
科学领域:
- 手术创新 在外科创新.
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 门手术,包括腹壁重建,在预测患者的结果和识别导致术后并发症的因素方面面临挑战.
- 越来越多地探索了先进的计算工具的整合,以增强外科决策和患者护理.
研究的目的:
- 系统地审查和评估机器学习 (ML) 和人工智能 (AI) 在手术中的应用.
- 综合当前关于使用ML和深度学习模型 (DLMs) 进行结局预测和修复并发症识别的研究.
主要方法:
- 在遵守PRISMA指南的基础上进行系统审查.
- 使用ROBINS-I和Rob 2工具对包含的研究进行定性评估.
- 总结了包括协议,研究问题,搜索策略,资格,数据提取,研究设计和偏见风险在内的关键项目.
主要成果:
- 包括在2020年至2023年间发表的13篇文章,重点关注在瘤手术中的ML / AI.
- 研究涵盖了 inguinal,ventral 和切口,使用各种ML / DLM.
- 关键应用包括手术步骤的识别,使用基于图像的DLM预测结果,以及确定术后并发症的因素.
结论:
- 机器学习在手术中为腹壁重建提供了一个有前途的工具.
- ML有助于预测外科手术结果,并识别与术后并发症相关的因素.
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