Vedula S Swaroop, Malpani Anand, Ahmidi Narges, Khudanpur Sanjeev, Hager Gregory, Chen Chi Chiung Grace
Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.
Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.
J Surg Educ. 2016 May-Jun;73(3):482-9. doi: 10.1016/j.jsurg.2015.11.009. Epub 2016 Feb 16.
Task-level metrics of time and motion efficiency are valid measures of surgical technical skill. Metrics may be computed for segments (maneuvers and gestures) within a task after hierarchical task decomposition. Our objective was to compare task-level and segment (maneuver and gesture)-level metrics for surgical technical skill assessment.
Our analyses include predictive modeling using data from a prospective cohort study. We used a hierarchical semantic vocabulary to segment a simple surgical task of passing a needle across an incision and tying a surgeon's knot into maneuvers and gestures. We computed time, path length, and movements for the task, maneuvers, and gestures using tool motion data. We fit logistic regression models to predict experience-based skill using the quantitative metrics. We compared the area under a receiver operating characteristic curve (AUC) for task-level, maneuver-level, and gesture-level models.
Robotic surgical skills training laboratory.
In total, 4 faculty surgeons with experience in robotic surgery and 14 trainee surgeons with no or minimal experience in robotic surgery.
Experts performed the task in shorter time (49.74s; 95% CI = 43.27-56.21 vs. 81.97; 95% CI = 69.71-94.22), with shorter path length (1.63m; 95% CI = 1.49-1.76 vs. 2.23; 95% CI = 1.91-2.56), and with fewer movements (429.25; 95% CI = 383.80-474.70 vs. 728.69; 95% CI = 631.84-825.54) than novices. Experts differed from novices on metrics for individual maneuvers and gestures. The AUCs were 0.79; 95% CI = 0.62-0.97 for task-level models, 0.78; 95% CI = 0.6-0.96 for maneuver-level models, and 0.7; 95% CI = 0.44-0.97 for gesture-level models. There was no statistically significant difference in AUC between task-level and maneuver-level (p = 0.7) or gesture-level models (p = 0.17).
Maneuver-level and gesture-level metrics are discriminative of surgical skill and can be used to provide targeted feedback to surgical trainees.
时间和动作效率的任务级指标是手术技术技能的有效衡量标准。在对任务进行分层分解后,可以计算任务中各个部分(动作和手势)的指标。我们的目的是比较用于手术技术技能评估的任务级和部分(动作和手势)级指标。
我们的分析包括使用前瞻性队列研究的数据进行预测建模。我们使用分层语义词汇将穿过切口并打一个外科结的简单手术任务分解为动作和手势。我们使用工具运动数据计算任务、动作和手势的时间、路径长度和动作次数。我们拟合逻辑回归模型,使用定量指标预测基于经验的技能。我们比较了任务级、动作级和手势级模型的受试者工作特征曲线下面积(AUC)。
机器人手术技能培训实验室。
共有4名有机器人手术经验的外科教员和14名没有或仅有极少机器人手术经验的实习外科医生。
专家完成任务的时间更短(49.74秒;95%置信区间=43.27 - 56.21对81.97;95%置信区间=69.71 - 94.22),路径长度更短(1.63米;95%置信区间=1.49 - 1.76对2.23;95%置信区间=1.91 - 2.56),动作次数更少(429.25;95%置信区间=383.80 - 474.70对728.69;95%置信区间=631.84 - 825.54)。专家在各个动作和手势的指标上与新手不同。任务级模型的AUC为0.79;95%置信区间=0.62 - 0.97,动作级模型的AUC为0.78;95%置信区间=0.6 - 0.96,手势级模型的AUC为0.7;95%置信区间=0.44 - 0.97。任务级和动作级模型之间(p = 0.7)或手势级模型之间(p = 0.17)的AUC没有统计学上的显著差异。
动作级和手势级指标能够区分手术技能,可用于为外科实习生提供有针对性的反馈。