从目光到熟练度:深度学习驱动的预测新手在腹腔镜训练中的表现,使用AOI依赖的指标
Aseel F Khanfar1,2, Sanaz Motamedi1, Shawn D Safford3
1Department of Industrial Engineering, The Pennsylvania State University, University Park, PA, USA.
Surgical endoscopy
|December 5, 2025
概括
这项研究引入了从眼睛跟踪和运动分析的新指标,以客观地评估实习生的腹腔镜外科技能. 这些指标可以区分技能水平和预测视觉行为,个性化外科训练.
科学领域:
- 手术教育技术 技术手术教育
- 医疗模拟 医疗模拟
- 计算机视觉在外科手术中的应用
背景情况:
- 目前的腹腔镜外科培训缺乏个性化的反和客观的技能评估.
- 现有的模拟器往往忽略了儿科解剖学和先进的学员监测.
- 眼睛跟踪和运动分析在外科培训中提供了客观,实时反的潜力.
研究的目的:
- 开发和验证客观指标,以区分和预测外科手术技能水平.
- 从腹腔镜盒训练员任务中提取取取决于感兴趣区域 (AOI) 和运动指标.
- 评估机器学习模型在预测学员视觉行为的有效性.
主要方法:
- 医学学生和住院医生在成人和儿科腹腔镜盒训练器上执行任务.
- 计算机视觉-深度学习 (CV-DL) 集成的眼睛跟踪数据来提取AOI固定率和工具速度.
- 用K-means集群和机器学习模型 (随机森林,SVM,ANN,决策树) 来进行技能分类和视觉行为预测.
主要成果:
- 提取的指标成功地将新手分为高和中低技能水平 (p <0.05).
- 随机森林模型在预测视觉行为方面表现出最高的准确性,确定固定率和工具速度作为关键预测指标.
- 新手视觉注意力模式在儿童和成人盒子教练中一致 (p > 0.05).
结论:
- 来自眼睛跟踪和运动分析的客观指标可以有效地分类和预测新手手术技能水平和视觉行为.
- 这些发现支持开发定制培训计划,以提高外科手术的性能.
- 该方法有望在外科模拟中提供客观的实时反.
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