Suppr超能文献

一种基于图像信息的血管内介入技术操作技能客观评估方法。

An Image Information-Based Objective Assessment Method of Technical Manipulation Skills for Intravascular Interventions.

机构信息

School of Life Science, Beijing Institute of Technology, Beijing 100081, China.

China Academy of Electronics and Information Technology, Beijing 100041, China.

出版信息

Sensors (Basel). 2023 Apr 16;23(8):4031. doi: 10.3390/s23084031.

Abstract

The clinical success of vascular interventional surgery relies heavily on a surgeon's catheter/guidewire manipulation skills and strategies. An objective and accurate assessment method plays a critical role in evaluating the surgeon's technical manipulation skill level. Most of the existing evaluation methods incorporate the use of information technology to find more objective assessment models based on various metrics. However, in these models, sensors are often attached to the surgeon's hands or to interventional devices for data collection, which constrains the surgeon's operational movements or exerts an influence on the motion trajectory of interventional devices. In this paper, an image information-based assessment method is proposed for the evaluation of the surgeon's manipulation skills without the requirement of attaching sensors to the surgeon or catheters/guidewires. Surgeons are allowed to use their natural bedside manipulation skills during the data collection process. Their manipulation features during different catheterization tasks are derived from the motion analysis of the catheter/guidewire in video sequences. Notably, data relating to the number of speed peaks, slope variations, and the number of collisions are included in the assessment. Furthermore, the contact forces, resulting from interactions between the catheter/guidewire and the vascular model, are sensed by a 6-DoF F/T sensor. A support vector machine (SVM) classification framework is developed to discriminate the surgeon's catheterization skill levels. The experimental results demonstrate that the proposed SVM-based assessment method can obtain an accuracy of 97.02% to distinguish between the expert and novice manipulations, which is higher than that of other existing research achievements. The proposed method has great potential to facilitate skill assessment and training of novice surgeons in vascular interventional surgery.

摘要

血管介入手术的临床成功在很大程度上依赖于外科医生的导管/导丝操作技巧和策略。客观准确的评估方法对于评估外科医生的技术操作技能水平起着至关重要的作用。现有的评估方法大多结合了信息技术,以基于各种指标寻找更客观的评估模型。然而,在这些模型中,传感器通常被附加到外科医生的手或介入设备上以进行数据采集,这限制了外科医生的操作运动或对介入设备的运动轨迹产生影响。在本文中,提出了一种基于图像信息的评估方法,用于评估外科医生的操作技能,而无需将传感器附加到外科医生或导管/导丝上。在数据采集过程中,允许外科医生使用其自然床边操作技能。他们在不同导管插入任务中的操作特征是从视频序列中导管/导丝的运动分析中得出的。值得注意的是,评估中包括与速度峰值数量、斜率变化和碰撞数量有关的数据。此外,导管/导丝与血管模型之间的相互作用产生的接触力由 6 自由度 F/T 传感器感知。开发了支持向量机 (SVM) 分类框架来区分外科医生的导管插入技能水平。实验结果表明,所提出的基于 SVM 的评估方法可以获得 97.02%的准确性来区分专家和新手操作,这高于其他现有研究成果。该方法有望促进血管介入手术中新手外科医生的技能评估和培训。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2146/10144356/923e7f776dbf/sensors-23-04031-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验