Svendsen Morten Bo, Preisler Louise, Hillingsoe Jens Georg, Svendsen Lars Bo, Konge Lars
Morten Bo Svendsen, Lars Konge, Centre for Clinical Education, University of Copenhagen and the Capital Region of Denmark, 2100 Copenhagen, Denmark.
World J Gastrointest Endosc. 2014 May 16;6(5):193-9. doi: 10.4253/wjge.v6.i5.193.
To study technical skills of colonoscopists using a Microsoft Kinect™ for motion analysis to develop a tool to guide colonoscopy education.
Ten experienced endoscopists (gastroenterologists, n = 2; colorectal surgeons, n = 8) and 11 novices participated in the study. A Microsoft Kinect™ recorded the movements of the participants during the insertion of the colonoscope. We used a modified script from Microsoft to record skeletal data. Data were saved and later transferred to MatLab for analysis and the calculation of statistics. The test was performed on a physical model, specifically the "Kagaku Colonoscope Training Model" (Kyoto Kagaku Co. Ltd, Kyoto, Japan). After the introduction to the scope and colonoscopy model, the test was performed. Seven metrics were analyzed to find discriminative motion patterns between the novice and experienced endoscopists: hand distance from gurney, number of times the right hand was used to control the small wheel of the colonoscope, angulation of elbows, position of hands in relation to body posture, angulation of body posture in relation to the anus, mean distance between the hands and percentage of time the hands were approximated to each other.
Four of the seven metrics showed discriminatory ability: mean distance between hands [45 cm for experienced endoscopists (SD 2) vs 37 cm for novice endoscopists (SD 6)], percentage of time in which the two hands were within 25 cm of each other [5% for experienced endoscopists (SD 4) vs 12% for novice endoscopists (SD 9)], the level of the right hand below the sighting line (z-axis) (25 cm for experienced endoscopists vs 36 cm for novice endoscopists, P < 0.05) and the level of the left hand below the z-axis (6 cm for experienced endoscopists vs 15 cm for novice endoscopists, P < 0.05). By plotting the distributions of the percentages for each group, we determined the best discriminatory value between the groups. A pass score was set at the intersection of the distributions, and the consequences of the standard were explored for each test. By using the contrasting group method, we showed a discriminatory value of Z = 1.51 to be the pass/fail value of the data showing discriminatory ability. The pass score allowed all ten experienced endoscopists as well as five novice endoscopists to pass the test.
Identified metrics can be used to discriminate between experienced and novice endoscopists and to provide non-biased feedback. Whether it is possible to use this tool to train novices in a clinical setting requires further study.
研究结肠镜检查医师使用微软Kinect™进行运动分析的技术技能，以开发一种指导结肠镜检查培训的工具。
10名经验丰富的内镜医师（胃肠病学家，2名；结直肠外科医生，8名）和11名新手参与了该研究。微软Kinect™记录了参与者在插入结肠镜过程中的动作。我们使用微软修改后的脚本记录骨骼数据。数据被保存下来，随后传输到MatLab进行分析和统计计算。测试在一个物理模型上进行，具体为“科学结肠镜训练模型”（日本京都科学株式会社，京都）。在介绍了结肠镜和结肠镜检查模型后，进行了测试。分析了七个指标，以找出新手和经验丰富的内镜医师之间有区别的运动模式：手离轮床的距离、右手控制结肠镜小轮的次数、肘部的角度、手相对于身体姿势的位置、身体姿势相对于肛门的角度、双手之间的平均距离以及双手相互靠近的时间百分比。
七个指标中的四个显示出区分能力：双手之间的平均距离[经验丰富的内镜医师为45厘米（标准差2），新手内镜医师为37厘米（标准差6）]、双手在彼此25厘米范围内的时间百分比[经验丰富的内镜医师为5%（标准差4），新手内镜医师为12%（标准差9）]、右手低于视线（z轴）的高度（经验丰富的内镜医师为25厘米，新手内镜医师为36厘米，P<0.05）以及左手低于z轴的高度（经验丰富的内镜医师为6厘米，新手内镜医师为15厘米，P<0.05）。通过绘制每组百分比的分布，我们确定了两组之间的最佳区分值。在分布的交点处设定了及格分数，并对每个测试的标准结果进行了探讨。通过使用对比组方法，我们显示Z=1.51的区分值是具有区分能力的数据的及格/不及格值。及格分数使所有10名经验丰富的内镜医师以及5名新手内镜医师通过了测试。
所确定的指标可用于区分经验丰富的内镜医师和新手内镜医师，并提供无偏差的反馈。是否有可能在临床环境中使用该工具培训新手还需要进一步研究。
