Department of Psychology, Northumbria University, Newcastle-upon-Tyne, UK.
Department of Computer Science, Durham University, Durham, UK.
J Cardiothorac Surg. 2024 Feb 14;19(1):94. doi: 10.1186/s13019-024-02558-5.
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 is possible 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. With collaborative efforts between surgical teams, medical institutions, computer scientists and researchers to ensure this technology is developed with usability and ethics in mind, the developed feedback tools could improve cardiothoracic surgical practice in a data-driven way.
当技术要求较高且患者预后至关重要时，通过客观运动分析反馈来监测和提高手术技能的机会可能特别有益。本文通过技术和非技术手术技能、协作任务绩效以及姿势估计方面的工作综合说明，展示了计算机视觉和人工智能创新如何为心胸外科手术性能带来新的提升机会。这些技术创新从为心胸外科社区带来的益处以及技术采用的障碍等方面进行了批判性评估。与其他一些专业一样，心胸外科手术从嵌入数据采集技术的工具中获益的机会相对较少（例如，机器人辅助腹腔镜手术就是如此）。在这种情况下，允许在不使用专业设备或标记物的情况下在常规手术区域内进行运动跟踪的姿势估计技术具有很大的潜力。使用来自模拟或真实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绩效的发展；(2) 为受训外科医生提供有关改进领域的反馈；(3) 提供机会研究哪些技能方面可能与患者预后相关，从而 (4) 为培训或指导计划中应关注的外科技能方面提供信息。使用人工智能“学习”专家外科评估员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受训者是否达到能力阈值。通过外科团队、医疗机构、计算机科学家和研究人员之间的协作努力，确保以可用性和道德为前提开发这种技术，开发的反馈工具可以以数据驱动的方式改进心胸外科实践。
