Objective: This study creates linear and generalized additive models (GAMs) of video-recorded two-dimensional hand motion (synonymously referred to as hand movements or hand kinematics) to predict expert-rated performance along a series of surgical motion scales. Background: Surgical performance assessments are costly and time consuming. Automatically quantifying hand motion may offload some burden of surgical coaching and intervention by automatically collecting features of psychomotor performance. Methods: Five experts rated anonymized video clips of benchtop suturing and tying tasks (n = 219) along four visual-analog (0–10) performance scales: fluidity of motion, motion economy, tissue handling, and hand coordination. Custom software tracked both participant hands across successive video frames and populated a robust feature set to train a series of predictive models to reproduce the expert ratings. Results: A GAM (which accounts for nonlinear effects) predicted fluidity of motion ratings with slope = 0.71, intercept = 1.98, and ${{\boldsymbol{R}}^2}$ = 0.77 for clinicians of different experience levels. Fluidity of motion and motion economy models outperformed those created to predict hand coordination and tissue handling ratings. Conclusions: Hand motion tracking may not address all contextual features of surgical tasks. Future work will explore how well simulation-based models extrapolate to more dynamic settings of the operating room.

中文翻译：

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沿 OSATS 派生运动量表的专家评级和无标记手部跟踪的比较

客观的： 这项研究创建了视频录制的二维手部运动（同义词称为手部运动或手部运动学）的线性和广义加性模型 (GAM)，以预测一系列手术运动量表的专家级性能。 背景：手术性能评估既昂贵又耗时。通过自动收集心理运动表现的特征，自动量化手部运动可能会减轻一些外科指导和干预的负担。方法：五位专家对台式缝合和打结任务的匿名视频剪辑进行了评级（n= 219) 沿着四个视觉模拟 (0-10) 性能量表：运动的流动性、运动经济性、组织处理和手部协调。定制软件在连续的视频帧中跟踪参与者的双手，并填充一个强大的功能集来训练一系列预测模型来重现专家评级。结果：GAM（考虑非线性效应）预测运动评级的流动性，斜率 = 0.71，截距 = 1.98，并且 ${{\boldsymbol{R}}^2}$= 0.77 对于不同经验水平的临床医生。运动的流动性和运动经济性模型优于那些用于预测手部协调和组织处理等级的模型。结论：手部运动跟踪可能无法解决手术任务的所有上下文特征。未来的工作将探索基于模拟的模型如何外推到手术室的更多动态设置。