When technical requirements are high, and patient outcomes are critical,
opportunities for monitoring and improving surgical skills via objective motion
analysis feedback may be particularly beneficial. This narrative review
synthesises work on technical and non-technical surgical skills, collaborative
task performance, and pose estimation to illustrate new opportunities to
advance cardiothoracic surgical performance with innovations from computer
vision and artificial intelligence. These technological innovations are
critically evaluated in terms of the benefits they could offer the
cardiothoracic surgical community, and any barriers to the uptake of the
technology are elaborated upon. Like some other specialities, cardiothoracic
surgery has relatively few opportunities to benefit from tools with data
capture technology embedded within them (as with robotic-assisted laparoscopic
surgery, for example). In such cases, pose estimation techniques that allow for
movement tracking across a conventional operating field without using
specialist equipment or markers offer considerable potential. With video data
from either simulated or real surgical procedures, these tools can (1) provide
insight into the development of expertise and surgical performance over a
surgeon's career, (2) provide feedback to trainee surgeons regarding areas for
improvement, (3) provide the opportunity to investigate what aspects of skill
may be linked to patient outcomes which can (4) inform the aspects of surgical
skill which should be focused on within training or mentoring programmes.
Classifier or assessment algorithms that use artificial intelligence to 'learn'
what expertise is from expert surgical evaluators could further assist
educators in determining if trainees meet competency thresholds.

高技术要求且患者结局至关重要时，通过客观运动分析反馈监测和改进外科技能的机会可能特别有益。本综述通过对技术和非技术外科技能、协作任务表现和姿态估计的研究进行综合，揭示了通过计算机视觉和人工智能的创新推进心胸外科手术性能的新机遇。这些技术创新从纵向上评估其对心胸外科手术界所带来的益处，并详细阐述了采用该技术的障碍。如同其他一些专业一样，心胸外科手术相对较少有机会从嵌入数据采集技术的工具中受益（例如机器人辅助腹腔镜手术）。在这种情况下，允许在常规手术领域中进行运动追踪而不使用专业设备或标记的姿态估计技术具有相当大的潜力。借助来自模拟或真实外科手术过程的视频数据，这些工具可以（1）深入了解外科医生职业生涯中专业知识和手术性能的发展，（2）为实习外科医生提供改进方面的反馈，（3）研究与患者结局有关的技能方面，并（4）在培训或指导计划中确定应重点关注的外科技能方面提供支持。利用人工智能进行 ' 学习 ' 专家外科评估者的专业知识的分类器或评估算法可以进一步帮助教育工作者确定实习生是否达到了胜任水平。

应用计算机视觉和人工智能在心胸外科中提升手术技术性能：一篇叙事性综述

Enhancing Surgical Performance in Cardiothoracic Surgery with  Innovations from Computer Vision and Artificial Intelligence: A Narrative  Review

Surgical skills have a great influence on surgical safety and patients'
well-being. Traditional assessment of surgical skills involves strenuous manual
efforts, which lacks efficiency and repeatability. Therefore, we attempt to
automatically predict how well the surgery is performed using the surgical
video. In this paper, a unified multi-path framework for automatic surgical
skill assessment is proposed, which takes care of multiple composing aspects of
surgical skills, including surgical tool usage, intraoperative event pattern,
and other skill proxies. The dependency relationships among these different
aspects are specially modeled by a path dependency module in the framework. We
conduct extensive experiments on the JIGSAWS dataset of simulated surgical
tasks, and a new clinical dataset of real laparoscopic surgeries. The proposed
framework achieves promising results on both datasets, with the
state-of-the-art on the simulated dataset advanced from 0.71 Spearman's
correlation to 0.80. It is also shown that combining multiple skill aspects
yields better performance than relying on a single aspect.

提出了一种统一的多路径框架，用于通过手术视频自动预测手术的成功程度，该框架考虑了手术技能的多个组成方面，包括手术工具使用、术中事件模式和其他技能代理，并通过路径依赖模块特别建模这些不同方面之间的依赖关系。在模拟手术任务的 JIGSAWS 数据集和新的真实腹腔镜手术的临床数据集上进行了大量实验，提出的框架在两个数据集上均取得了良好的成果，模拟数据集的最先进水平从 0.71 Spearman 相关性提高到了 0.80，同时还表明结合多个技能方面比依赖单个方面获得更好的性能。