Morris Miranda X, Rajesh Aashish, Asaad Malke, Hassan Abbas, Saadoun Rakan, Butler Charles E
12277Duke University School of Medicine, Durham, NC, USA.
101571Duke Pratt School of Engineering, Durham, NC, USA.
Am Surg. 2023 Jan;89(1):36-42. doi: 10.1177/00031348221101490. Epub 2022 May 13.
Deep learning (DL) is a subset of machine learning that is rapidly gaining traction in surgical fields. Its tremendous capacity for powerful data-driven problem-solving has generated computational breakthroughs in many realms, with the fields of medicine and surgery becoming increasingly prominent avenues. Through its multi-layer architecture of interconnected neural networks, DL enables feature extraction and pattern recognition of highly complex and large-volume data. Across various surgical specialties, DL is being applied to optimize both preoperative planning and intraoperative performance in new and innovative ways. Surgeons are now able to integrate deep learning tools into their practice to improve patient safety and outcomes. Through this review, we explore the applications of deep learning in surgery and related subspecialties with an aim to shed light on the practical utilization of this technology in the present and near future.
深度学习(DL)是机器学习的一个子集,正在外科领域迅速获得关注。它在强大的数据驱动问题解决方面的巨大能力在许多领域带来了计算突破,医学和外科领域成为越来越突出的途径。通过其相互连接的神经网络的多层架构,深度学习能够对高度复杂和大量的数据进行特征提取和模式识别。在各个外科专科中,深度学习正以新颖的方式被应用于优化术前规划和术中表现。外科医生现在能够将深度学习工具整合到他们的实践中,以提高患者安全性和治疗效果。通过这篇综述,我们探讨深度学习在外科及相关亚专科中的应用,旨在阐明该技术在当前和不久的将来的实际应用。