assessment using video data and to showcase the effectiveness of the proposed
approach in evaluating surgeon proficiency, its potential for targeted training
interventions, and quality assurance in surgical departments. The pipeline
incorporates a representation flow convolutional neural network and a novel
tree-based Gaussian process classifier, which is robust to noise, while being
computationally efficient. Additionally, new kernels are introduced to enhance
accuracy. The performance of the pipeline is evaluated using the JIGSAWS
dataset. Comparative analysis with existing literature reveals significant
improvement in accuracy and betterment in computation cost. The proposed
pipeline contributes to computational efficiency and accuracy improvement in
surgical skill assessment using video data. Results of our study based on
comments of our colleague surgeons show that the proposed method has the
potential to facilitate skill improvement among surgery fellows and enhance
patient safety through targeted training interventions and quality assurance in
surgical departments.

使用视频数据进行评估，展示了所提出方法在评估外科医生熟练程度、针对性培训干预和外科部门质量保证方面的有效性，该流水线结合了表示流卷积神经网络和一种新颖的基于树的高斯过程分类器，它具有抗噪性和计算效率。此外，引入了新的核来提高准确性，该流水线的性能使用 JIGSAWS 数据集进行评估，与现有文献进行比较分析显示出准确性的显著改善和计算成本的改善，该提议的流程为使用视频数据进行外科技能评估的计算效率和准确性的提升做出了贡献，在同事外科医生的评价中，我们的研究结果表明该方法有助于促进外科住院医师的技能提高，并通过针对性培训干预和外科部门质量保证来增强患者安全。