Surgical Artificial Intelligence and Innovation Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
ICube, University of Strasbourg, CNRS, IHU Strasbourg, France; Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
Surgery. 2021 May;169(5):1253-1256. doi: 10.1016/j.surg.2020.10.039. Epub 2020 Dec 1.
The fields of computer vision (CV) and artificial intelligence (AI) have undergone rapid advancements in the past decade, many of which have been applied to the analysis of intraoperative video. These advances are driven by wide-spread application of deep learning, which leverages multiple layers of neural networks to teach computers complex tasks. Prior to these advances, applications of AI in the operating room were limited by our relative inability to train computers to accurately understand images with traditional machine learning (ML) techniques. The development and refining of deep neural networks that can now accurately identify objects in images and remember past surgical events has sparked a surge in the applications of CV to analyze intraoperative video and has allowed for the accurate identification of surgical phases (steps) and instruments across a variety of procedures. In some cases, CV can even identify operative phases with accuracy similar to surgeons. Future research will likely expand on this foundation of surgical knowledge using larger video datasets and improved algorithms with greater accuracy and interpretability to create clinically useful AI models that gain widespread adoption and augment the surgeon's ability to provide safer care for patients everywhere.
在过去的十年中，计算机视觉 (CV) 和人工智能 (AI) 领域取得了快速发展，其中许多技术已应用于术中视频分析。这些进展的推动因素是深度学习的广泛应用，深度学习利用多层神经网络来教计算机完成复杂任务。在此之前，由于我们相对无法使用传统机器学习 (ML) 技术训练计算机准确理解图像，因此 AI 在手术室中的应用受到限制。现在，能够准确识别图像中的物体并记住过去手术事件的深度神经网络的开发和改进，激发了 CV 分析术中视频的应用热潮，并能够准确识别各种手术步骤和手术器械。在某些情况下，CV 甚至可以与外科医生一样准确地识别手术阶段。未来的研究可能会使用更大的视频数据集和更准确、更具可解释性的改进算法，在这个手术知识的基础上进一步扩展，以创建具有广泛应用并增强外科医生为各地患者提供更安全护理能力的临床有用的 AI 模型。
