Learning Retention of Thoracic Pedicle Screw Placement Using a High-Resolution Augmented Reality Simulator With Haptic Feedback

医学 触觉技术 胸椎 考试(生物学) 模拟 虚拟现实 医学物理学 计算机科学 外科 人工智能 生物 古生物学
作者
Cristián Luciano,Pat Banerjee,Brad Bellotte,G Michael Oh,Michael Lemole,Fady T. Charbel,Ben Roitberg
出处
期刊:Operative Neurosurgery [Lippincott Williams & Wilkins]
卷期号:69: ons14-ons19 被引量:105
标识
DOI:10.1227/neu.0b013e31821954ed
摘要

We evaluated the use of a part-task simulator with 3D and haptic feedback as a training tool for a common neurosurgical procedure--placement of thoracic pedicle screws.To evaluate the learning retention of thoracic pedicle screw placement on a high-performance augmented reality and haptic technology workstation.Fifty-one fellows and residents performed thoracic pedicle screw placement on the simulator. The virtual screws were drilled into a virtual patient's thoracic spine derived from a computed tomography data set of a real patient.With a 12.5% failure rate, a 2-proportion z test yielded P = .08. For performance accuracy, an aggregate Euclidean distance deviation from entry landmark on the pedicle and a similar deviation from the target landmark in the vertebral body yielded P = .04 from a 2-sample t test in which the rejected null hypothesis assumes no improvement in performance accuracy from the practice to the test sessions, and the alternative hypothesis assumes an improvement.The performance accuracy on the simulator was comparable to the accuracy reported in literature on recent retrospective evaluation of such placements. The failure rates indicated a minor drop from practice to test sessions, and also indicated a trend (P = .08) toward learning retention resulting in improvement from practice to test sessions. The performance accuracy showed a 15% mean score improvement and more than a 50% reduction in standard deviation from practice to test. It showed evidence (P = .04) of performance accuracy improvement from practice to test session.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
刚刚
kreatal发布了新的文献求助10
1秒前
1秒前
美满的画板完成签到 ,获得积分10
3秒前
科研通AI6.2应助如常采纳,获得10
3秒前
淡然发布了新的文献求助10
3秒前
3秒前
4秒前
4秒前
arniu2008发布了新的文献求助10
6秒前
6秒前
lizeyu发布了新的文献求助10
8秒前
8秒前
坦率凌波关注了科研通微信公众号
8秒前
旰旰旰完成签到,获得积分10
8秒前
9秒前
9秒前
米香脆发布了新的文献求助10
9秒前
9秒前
Pupil发布了新的文献求助10
9秒前
秘书处堂完成签到,获得积分10
10秒前
Boyce发布了新的文献求助10
10秒前
11秒前
11秒前
所所应助小yy采纳,获得10
12秒前
wxd发布了新的文献求助10
12秒前
是曦凉啊发布了新的文献求助10
12秒前
翁雁丝发布了新的文献求助10
13秒前
LChen完成签到,获得积分10
13秒前
完美诗兰发布了新的文献求助10
14秒前
14秒前
华仔应助科研通管家采纳,获得10
14秒前
14秒前
Gauss应助科研通管家采纳,获得30
14秒前
orixero应助科研通管家采纳,获得30
14秒前
14秒前
Copyright应助科研通管家采纳,获得10
14秒前
14秒前
15秒前
高分求助中
GL 2 A method for assessing the in-place cleanability of food processing equipment, Fourth Edition, December 2023 3000
Annie Ernaux: De la perte au corps glorieux 600
Writing Systems 500
类器官构建与应用:从基础到前沿 500
Optical Coating Design with the Essential Macleod 400
A revision of Limenitis helmanni and its related species (Nymphalidae) from Central and South China 400
Moore's Clinically Oriented Anatomy 10th Edition 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6797447
求助须知:如何正确求助?哪些是违规求助? 8516873
关于积分的说明 18138273
捐赠科研通 6112039
什么是DOI,文献DOI怎么找? 3024854
邀请新用户注册赠送积分活动 2001439
关于科研通互助平台的介绍 1992842