Usingmotioncapturetoassesscolonoscopyexperiencelevel

• 作者: Hillingsoe JG Konge L Preisler L Svendsen LB Svendsen MB
• 来源期刊: 世界胃肠内镜杂志：英文版（电子版）
• 学科: 医学
• 关键词: COLONOSCOPY 评价 模拟 运动俘获 运动分析
• 年，卷（期）: 2014,（5）
• 页码范围: 53-59
• 页数: 7页
• 分类号: R

摘要

AIM: To study technical skills of colonoscopists using a Microsoft Kinect? for motion analysis to develop a tool to guide colonoscopy education.RESULTS: 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 wasused 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.RESULTS: 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