Using Contact Forces and Robot Arm Accelerations to Automatically Rate Surgeon Skill at Peg Transfer
Using Contact Forces and Robot Arm Accelerations to Automatically Rate Surgeon Skill at Peg Transfer
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DOI：
10.1109/tbme.2016.2634861
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发表时间：
2017-09-01
影响因子：
4.6
通讯作者：
Kuchenbecker, Katherine J.
中科院分区：
文献类型：
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作者：
Brown, Jeremy D.;O'Brien, Conor E.;Kuchenbecker, Katherine J.
目的：大多数学员开始学习机器人微创手术是通过与临床机器人进行无生命的练习任务，如直觉外科达芬奇。专家外科医生通常被要求使用标准化的五分制量表来评估这些表现，但这样的评分既耗时又乏味，而且有些主观。本文提出了一种仅分析机器人与任务材料的接触力、机器人仪器和相机的宽带加速度以及任务完成时间的自动技能评估系统。方法：我们招募了N = 38名不同技能的机器人手术参与者，使用带有我们的智能任务板的达芬奇标准机器人进行三次peg转移试验。校准后，三个人在机器人技能全球评估评估（GEARS）结构化评估工具的五个领域对这些试验进行评分，为回归和分类机器学习算法提供基本事实标签，该算法根据记录的力、加速度和时间信号预测GEARS分数。结果：两种机器学习方法在保留的测试集上产生的分数与人类评分者的分数非常一致，即使不考虑力信息。此外，回归预测GEARS得分比分类预测更准确、更有效。结论：外科医生在机器人钉移植方面的技能可以通过使用机器人外部的力、加速度和时间传感器收集的特征进行回归来可靠地评估。意义：我们期望在手术机器人的无生命任务练习中提供这些自动技能评级，从而改善受训者的学习。
Objective: Most trainees begin learning robotic minimally invasive surgery by performing inanimate practice tasks with clinical robots such as the Intuitive Surgical da Vinci. Expert surgeons are commonly asked to evaluate these performances using standardized five-point rating scales, but doing such ratings is time consuming, tedious, and somewhat subjective. This paper presents an automatic skill evaluation system that analyzes only the contact force with the task materials, the broad-bandwidth accelerations of the robotic instruments and camera, and the task completion time. Methods: We recruited N = 38 participants of varying skill in robotic surgery to perform three trials of peg transfer with a da Vinci Standard robot instrumented with our Smart Task Board. After calibration, three individuals rated these trials on five domains of the Global Evaluative Assessment of Robotic Skill (GEARS) structured assessment tool, providing ground-truth labels for regression and classification machine learning algorithms that predict GEARS scores based on the recorded force, acceleration, and time signals. Results: Both machine learning approaches produced scores on the reserved testing sets that were in good to excellent agreement with the human raters, even when the force information was not considered. Furthermore, regression predicted GEARS scores more accurately and efficiently than classification. Conclusion: A surgeon's skill at robotic peg transfer can be reliably rated via regression using features gathered from force, acceleration, and time sensors external to the robot. Significance: We expect improved trainee learning as a result of providing these automatic skill ratings during inanimate task practice on a surgical robot.