IRIMAS, University of Haute-Alsace, Mulhouse, France.
Faculty of Information Technology, Monash University, Melbourne, Australia.
Int J Comput Assist Radiol Surg. 2018 May;13(5):629-636. doi: 10.1007/s11548-018-1713-y. Epub 2018 Mar 3.
Surgery is one of the riskiest and most important medical acts that are performed today. The need to improve patient outcomes and surgeon training, and to reduce the costs of surgery, has motivated the equipment of operating rooms with sensors that record surgical interventions. The richness and complexity of the data that are collected call for new methods to support computer-assisted surgery. The aim of this paper is to support the monitoring of junior surgeons learning their surgical skill sets.
Our method is fully automatic and takes as input a series of surgical interventions each represented by a low-level recording of all activities performed by the surgeon during the intervention (e.g., cut the skin with a scalpel). Our method produces a curve describing the process of standardization of the behavior of junior surgeons. Given the fact that junior surgeons receive constant feedback from senior surgeons during surgery, these curves can be directly interpreted as learning curves.
Our method is assessed using the behavior of a junior surgeon in anterior cervical discectomy and fusion surgery over his first three years after residency. They revealed the ability of the method to accurately represent the surgical skill evolution. We also showed that the learning curves can be computed by phases allowing a finer evaluation of the skill progression.
Preliminary results suggest that our approach constitutes a useful addition to surgical training monitoring.
手术是当今进行的风险最高、最重要的医疗行为之一。为了提高患者的治疗效果和外科医生的培训水平，降低手术成本，人们已经开始在手术室中配备记录手术干预的传感器。所收集的数据丰富而复杂，这就需要新的方法来支持计算机辅助手术。本文旨在支持对初级外科医生学习手术技能的监测。
我们的方法是全自动的，它将输入一系列手术干预，每个干预都由外科医生在干预过程中执行的所有活动的低级记录表示（例如，用手术刀切开皮肤）。我们的方法生成一条描述初级外科医生行为标准化过程的曲线。由于初级外科医生在手术过程中会不断从资深外科医生那里得到反馈，因此这些曲线可以直接解释为学习曲线。
我们的方法使用初级外科医生在 residency 后前三年的颈椎前路椎间盘切除和融合手术中的行为进行评估。结果表明，该方法能够准确地表示手术技能的演变。我们还表明，可以通过分阶段计算学习曲线，从而更精细地评估技能进展。
初步结果表明，我们的方法是手术培训监测的有益补充。
