A Multilayer and Multimodal-Fusion Architecture for Simultaneous Recognition of Endovascular Manipulations and Assessment of Technical Skills

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 limi...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2022-04, Vol.52 (4), p.2565-2577
Main Authors: Zhou, Xiao-Hu, Xie, Xiao-Liang, Feng, Zhen-Qiu, Hou, Zeng-Guang, Bian, Gui-Bin, Li, Rui-Qi, Ni, Zhen-Liang, Liu, Shi-Qi, Zhou, Yan-Jie
Format: Article
Language:English
Subjects:
Algorithms
Arteries
Catheters
Clinical Competence
Endovascular manipulations
Humans
Learning
Monolayers
multilayer and multimodal-fusion architecture (MMFA)
Multilayers
Nonhomogeneous media
Percutaneous Coronary Intervention
percutaneous coronary intervention (PCI)
Performance assessment
Performance evaluation
Radio frequency
Recognition
Sensors
Skills
Surgery
technical skill assessment
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Summary: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.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2020.3004653