A Multilayer and Multimodal-Fusion Architecture for Simultaneous Recognition of Endovascular Manipulations and Assessment of Technical Skills
| Autor: | Zeng-Guang Hou, Zhen-Liang Ni, Yan-Jie Zhou, Xiao-Hu Zhou, Liu Shiqi, Feng Zhenqiu, Xiao-Liang Xie, Gui-Bin Bian, Rui-Qi Li |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Multimodal fusion
Computer science medicine.medical_treatment Machine learning computer.software_genre Clinical success Motion (physics) Percutaneous Coronary Intervention medicine Humans Learning Electrical and Electronic Engineering Technical skills Architecture business.industry Percutaneous coronary intervention Computer Science Applications Human-Computer Interaction Control and Systems Engineering Clinical Competence Artificial intelligence business computer Algorithms Software Clinical skills Information Systems |
| Zdroj: | IEEE Transactions on Cybernetics. 52:2565-2577 |
| ISSN: | 2168-2275 2168-2267 |
| Popis: | 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 single-modal 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. |
| Databáze: | OpenAIRE |
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