Zago Mauro, Sforza Chiarella, Mariani Diego, Marconi Matteo, Biloslavo Alan, Greca Antonio La, Kurihara Hayato, Casamassima Andrea, Bozzo Samantha, Caputo Francesco, Galli Manuela, Zago Matteo
Department of Surgery, Minimally Invasive Surgery Unit, Policlinico San Pietro, Via Forlanini 15, Ponte San Pietro, 24036, Bergamo, Italy.
Department of Biomedical Sciences for Health, Università degli Studi di Milano, via Mangiagalli 31, 20133, Milan, Italy.
Eur J Trauma Emerg Surg. 2020 Dec;46(6):1421-1428. doi: 10.1007/s00068-019-01112-6. Epub 2019 Mar 15.
Increasing pressure pushes towards the objective competence assessment of clinical operators. Hand motion analysis (HMA) was introduced to measure surgical and clinical procedures; its recent application to FAST examinations leaves unsolved issues. This study aimed at determining optimal HMA parameters to discriminate between operators' skill levels, and which FAST tasks are experience-dependent.
Ten experienced (EG) and 13 beginner (BG) sonographers performed a FAST examination on one female and one male model. A motion capture system returned the duration, working volume, number of movements (absolute and time normalized), and hand path length (absolute and time normalized) of each view.
BG took more time in completing specific views, with a higher working volume (p = 0.003) and longer hands path (p < 0.001). The number of movements was lower in the EG (p < 0.001) and differed between views (p = 0.014). No significant Group/Model differences were found for the normalized number of movements. The LUQ view required a higher number of movements (p < 0.001).
HMA identified kinematic parameters discriminating between proficiency level and critical subtasks in the FAST examination. These findings could be the base for a focused HMA-based evaluation of performances following a proctored training period. There is room to incorporate HMA into simulation metrics and evidence-based credentialing standards for clinical ultrasound applications.
不断增加的压力促使朝着临床操作人员的客观能力评估发展。引入手部运动分析（HMA）来测量手术和临床操作；其最近在FAST检查中的应用仍存在未解决的问题。本研究旨在确定最佳的HMA参数以区分操作人员的技能水平，以及哪些FAST任务依赖于经验。
10名经验丰富的超声检查人员（EG）和13名初学者（BG）对一名女性和一名男性模型进行了FAST检查。一个动作捕捉系统记录了每个视图的持续时间、工作体积、动作数量（绝对数量和时间归一化数量）以及手部路径长度（绝对长度和时间归一化长度）。
BG完成特定视图花费的时间更长，工作体积更大（p = 0.003），手部路径更长（p < 0.001）。EG的动作数量更少（p < 0.001），并且不同视图之间存在差异（p = 0.014）。动作数量的归一化值在组/模型之间未发现显著差异。左上腹（LUQ）视图需要更多的动作（p < 0.001）。
HMA确定了在FAST检查中区分熟练程度水平和关键子任务的运动学参数。这些发现可以作为在监督培训期后基于HMA进行针对性性能评估的基础。有空间将HMA纳入临床超声应用的模拟指标和循证认证标准中。
