论文标题
基于视频的形成性和总结性评估手术任务使用深度学习
Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning
论文作者
论文摘要
为了确保令人满意的临床结果，手术技能评估必须是客观，时间效率且优先自动化的 - 目前尚无目前可实现。基于视频的评估（VBA）正在术中和模拟设置中部署，以评估技术技能的执行。但是，VBA仍然是手动和时间密集型的，容易出现主观解释和评估者间的可靠性差。在此，我们提出了一个深度学习（DL）模型，可以自动，客观地根据视频提要和低风险形成性评估对手术技能执行进行高风险的总结性评估，以指导手术技能获取。使用与手术性能相关的视觉特征的热图生成形成性评估。因此，DL模型为从视频中对手术任务进行定量和可重复评估的方式铺平了道路，并有可能在手术培训，认证和证书中进行广泛传播。
To ensure satisfactory clinical outcomes, surgical skill assessment must be objective, time-efficient, and preferentially automated - none of which is currently achievable. Video-based assessment (VBA) is being deployed in intraoperative and simulation settings to evaluate technical skill execution. However, VBA remains manually- and time-intensive and prone to subjective interpretation and poor inter-rater reliability. Herein, we propose a deep learning (DL) model that can automatically and objectively provide a high-stakes summative assessment of surgical skill execution based on video feeds and low-stakes formative assessment to guide surgical skill acquisition. Formative assessment is generated using heatmaps of visual features that correlate with surgical performance. Hence, the DL model paves the way to the quantitative and reproducible evaluation of surgical tasks from videos with the potential for broad dissemination in surgical training, certification, and credentialing.
