Affiliation:
1. School of Life Science, Beijing Institute of Technology, Beijing 100081, China
2. China Academy of Electronics and Information Technology, Beijing 100041, China
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.
Funder
National Natural Science Foundation of China
Beijing Institute of Technology Research Fund Program for Young Scholars
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference46 articles.
1. Treatment of Complex Aneurysmal Disease with Fenestrated and Branched Stent Grafts;Bicknell;Eur. J. Vasc. Endovasc. Surg.,2009
2. Current and Emerging Robot-Assisted Endovascular Catheterization Technologies: A Review;Payne;Ann. Biomed. Eng.,2013
3. Ultrasound-Assisted Guidance with Force Cues for Intravascular Interventions;Guo;IEEE Trans. Autom. Sci. Eng.,2019
4. Endovascular Repair of Abdominal Aortic Aneurysms: Current Status and Future Directions;Kaufman;Am. J. Roentgenol.,2000
5. Chen, X., Chen, Y., Duan, W., Akinyemi, T.O., Yi, G., Jiang, J., Du, W., and Omisore, O.M. (2022). Design and Evaluation of a Learning-Based Vascular Interventional Surgery Robot. Fibers, 10.
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