Key result
CNN model achieves ~96% accuracy in identifying operator actions during robot-assisted catheterization.
Why the study?
Training fellows for safe robotic PCIs requires expert supervision and high-fidelity systems, but current systems have limited proficiency for real-time hand motion recognition.
A novel CNN-based model using multimodal sensor data accurately recognizes interventionalist hand motions, potentially aiding training and evaluation in robot-assisted PCI.
May support AI motion tracking for PCI training evaluation; leaves open validation in larger prospective cohorts.
The development of procedural competencies by interventional cardiology fellows for minimally invasive treatment of cardiac diseases depends on their effective training and evaluation. Learning tool manipulation for safe robotic PCIs requires expert supervision and use of high-fidelity systems, however, with limited proficiency for real-time hand motion recognition. Therefore, this study proposes a deep learning-based model for identifying operators’ actions. The proposed model is based on the convolutional neural network (CNN) that consists of 1-D convolutional layers for automatic feature map extraction, downsampling, and fully connected layers (FCLs) for inference. The developed models were evaluated using a multimodal dataset collected from sensory glove, electromagnetic (EM), and surface electromyography (sEMG) sensors and real-time angiograms in the course of in vivo catheterization trials performed by nine subjects (two experts and seven novices) using a custom-built robotic catheter system (RCS). The results indicate that the model achieves a 92%–96% accuracy in identifying five actions across four clusters compared with a recognition performance of 83% when recognizing all six actions. Furthermore, we compared the proposed model performance with the existing studies, and the analyses show that our model has a 2%–3% higher accuracy for five-action recognition. Therefore, the proposed model could be employed for real-time hand motion recognition in robot-assisted percutaneous coronary intervention (R-PCI) trials.
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Akinyemi et al. (2023) studied Robot-assisted percutaneous coronary intervention (n=9). Convolutional neural network (CNN) model vs. Existing models was evaluated on Action recognition accuracy. A convolutional neural network model achieved 92%–96% accuracy in identifying five operator actions during robot-assisted catheterization, demonstrating 2%–3% higher accuracy than existing models.
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