Surgical Skill Assessment Based on Dynamic Warping Manipulations - Zhou, Xiao-Hu; Xie, Xiao-Liang; Liu, Shi-Qi; Feng, Zhen-Qiu; Gui, Mei-Jiang; Wang, Jin-Li; Li, Hao; Xiang, Tian-Yu; Bian, Gui-Bin; Hou, Zeng-Guang (IEEE transactions on medical robotics and bionics, 2022)
Percutaneous coronary intervention (PCI) has become a popular treatment for coronary artery disease. Highly dexterous skills are necessary to procedure success. However, few effective methods can be applied to PCI skill assessment. In this study, ten interventional cardiologists (four experts and six novices) were recruited. In vivo studies were performed via delivering a medical guidewire into distal left circumflex artery (target vessel I) and obtuse marginal artery (target vessel II) of a porcine model. Regarded as a type of manipulation data, the guidewire motion is simultaneously acquired with an electromagnetic (EM) sensor attached to guidewire tail. To address the deficiency of conventional dynamic time warping (DTW) limited to two-sequence matching, a novel warping algorithm is proposed to match multiple manipulation data. Then the intra-similarity is calculated to evaluate consistencies among each subject's different manipulations, while the inter-similarity is further analyzed to find skill differences among different subjects. Extensive statistical analysis demonstrates that the proposed algorithm can effectively distinguish between the manipulations made by different skill-level subjects with significant differences on the target vessel I ( P = 3.25× 10^{-4} ) and II ( P = 7.30× 10^{-3} ). These promising results show the proposed technique's great potential to facilitate skill assessment in clinical practice and skill learning in surgical robotics.