Learning Skill Characteristics From Manipulations

Percutaneous coronary intervention (PCI) has increasingly become the main treatment for coronary artery disease. The procedure requires high experienced skills and dexterous manipulations. However, there are few techniques to model PCI skill so far. In this study, a learning framework with local and...

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Vydané v:IEEE transaction on neural networks and learning systems Ročník 34; číslo 12; s. 9727 - 9741
Hlavní autori: Zhou, Xiao-Hu, Xie, Xiao-Liang, Liu, Shi-Qi, Ni, Zhen-Liang, Zhou, Yan-Jie, Li, Rui-Qi, Gui, Mei-Jiang, Fan, Chen-Chen, Feng, Zhen-Qiu, Bian, Gui-Bin, Hou, Zeng-Guang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Animal models
Animals
Arteries
Cardiovascular disease
Coronary arteriosclerosis
Coronary artery disease
Ensemble learning
Feature extraction
Fiber optics
Heart diseases
Humans
In vivo
In vivo methods and tests
in vivo porcine studies
Learning
Machine learning
Measurement
Neural Networks, Computer
Percutaneous Coronary Intervention
Robotics
Sensor phenomena and characterization
Sensors
skill characteristics
Surgery
Swine
Task analysis
wavelet packet decomposition (WPD)
Wavelet transforms
Wrist
ISSN:2162-237X, 2162-2388, 2162-2388
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Popis
Shrnutí:Percutaneous coronary intervention (PCI) has increasingly become the main treatment for coronary artery disease. The procedure requires high experienced skills and dexterous manipulations. However, there are few techniques to model PCI skill so far. In this study, a learning framework with local and ensemble learning is proposed to learn skill characteristics of different skill-level subjects from their PCI manipulations. Ten interventional cardiologists (four experts and six novices) were recruited to deliver a medical guidewire to two target arteries on a porcine model for in vivo studies. Simultaneously, translation and twist manipulations of thumb, forefinger, and wrist are acquired with electromagnetic (EM) and fiber-optic bend (FOB) sensors, respectively. These behavior data are then processed with wavelet packet decomposition (WPD) under 1-10 levels for feature extraction. The feature vectors are further fed into three candidate individual classifiers in the local learning layer. Furthermore, the local learning results from different manipulation behaviors are fused in the ensemble learning layer with three rule-based ensemble learning algorithms. In subject-dependent skill characteristics learning, the ensemble learning can achieve 100% accuracy, significantly outperforming the best local result (90%). Furthermore, ensemble learning can also maintain 73% accuracy in subject-independent schemes. These promising results demonstrate the great potential of the proposed method to facilitate skill learning in surgical robotics and skill assessment in clinical practice.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3160159