A Multilayer and Multimodal-Fusion Architecture for Simultaneous Recognition of Endovascular Manipulations and Assessment of Technical Skills | |
Xiaohu,Zhou3; Xiaoliang.Xie3; Zhenqiu,Feng3; Zengguang,Hou1,2,3; Guibin,Bian3; Ruiqi,Li1; Zhenliang,Ni1; Shiqi,Liu3; Yan-Jie Zhou1 | |
刊名 | IEEE TRANSACTIONS ON CYBERNETICS |
2020-07 | |
卷号 | PP期号:99页码:1-13 |
关键词 | Endovascular manipulations multilayer and multimodal-fusion architecture (MMFA) percutaneous coronary intervention (PCI), technical skill assessment |
英文摘要 | The clinical success of the percutaneous coronary intervention (PCI) is highly dependent on endovascular manipulation skills and dexterous manipulation strategies of interventionalists. However, the analysis of endovascular manipulations and related discussion for technical skill assessment are limited. In this study, a multilayer and multimodal-fusion architecture is proposed to recognize six typical endovascular manipulations. The synchronously acquired multimodal motion signals from ten subjects are used as the inputs of the architecture independently. Six classification-based and two rule-based fusion algorithms are evaluated for performance comparisons. The recognition metrics under the determined architecture are further used to assess technical skills. The experimental results indicate that the proposed architecture can achieve the overall accuracy of 96.41%, much higher than that of a single-layer recognition architecture (92.85%). In addition, the multimodal fusion brings significant performance improvement in comparison with singlemodal schemes. Furthermore, the K-means-based skill assessment can obtain an accuracy of 95% to cluster the attempts made by different skill-level groups. These hopeful results indicate the great possibility of the architecture to facilitate clinical skill assessment and skill learning. |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/41470] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
作者单位 | 1.the School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 2.the Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China 3.the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
推荐引用方式 GB/T 7714 | Xiaohu,Zhou,Xiaoliang.Xie,Zhenqiu,Feng,et al. A Multilayer and Multimodal-Fusion Architecture for Simultaneous Recognition of Endovascular Manipulations and Assessment of Technical Skills[J]. IEEE TRANSACTIONS ON CYBERNETICS,2020,PP(99):1-13. |
APA | Xiaohu,Zhou.,Xiaoliang.Xie.,Zhenqiu,Feng.,Zengguang,Hou.,Guibin,Bian.,...&Yan-Jie Zhou.(2020).A Multilayer and Multimodal-Fusion Architecture for Simultaneous Recognition of Endovascular Manipulations and Assessment of Technical Skills.IEEE TRANSACTIONS ON CYBERNETICS,PP(99),1-13. |
MLA | Xiaohu,Zhou,et al."A Multilayer and Multimodal-Fusion Architecture for Simultaneous Recognition of Endovascular Manipulations and Assessment of Technical Skills".IEEE TRANSACTIONS ON CYBERNETICS PP.99(2020):1-13. |
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