An HMM-based recognition framework for endovascular manipulations

Abstract

Robotic surgical systems are becoming increasingly popular for the treatment of cardiovascular diseases. However, most of them have been designed without considering techniques and skills of natural surgical manipulations, which are key factors to clinical success of percutaneous coronary intervention. This paper proposes an HMM-based framework to recognize six typical endovascular manipulations for surgical skill analysis. A simulative surgical platform is built for endovascular manipulations assessed by five subjects (1 expert and 4 novices). The performances of the proposed framework are evaluated by three experimental schemes with the optimal model parameters. The results show that endovascular manipulations are recognized with high accuracy and reliable performance. Furthermore, the acceptable results can also be applied to the design of next generation vascular interventional robots.