Xiaodong Wang, Jianjun Zhu, Yong Wang, Cheng Wang, Peng Chen, Pengju Lyu, Jun Xu, Gao-Jun Teng
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A Respiratory Signal Monitoring Method Based on Dual-Pathway Deep Learning Networks in Image-Guided Robotic-Assisted Intervention System
Background
Percutaneous puncture procedures, guided by image-guided robotic-assisted intervention (IGRI) systems, are susceptible to disruptions in patients' respiratory rhythm due to factors such as pain and psychological distress.
Methods
We developed an IGRI system with a coded structured light camera and a binocular camera. Our system incorporates dual-pathway deep learning networks, combining convolutional long short-term memory (ConvLSTM) and point long short-term memory (PointLSTM) modules for real-time respiratory signal monitoring.
Results
Our in-house dataset experiments demonstrate the superior performance of the proposed network in accuracy, precision, recall and F1 compared to separate use of PointLSTM and ConvLSTM for respiratory pattern classification.
Conclusion
In our IGRI system, a respiratory signal monitoring module was constructed with a binocular camera and dual-pathway deep learning networks. The integrated respiratory monitoring module provides a basis for the application of respiratory gating technology to IGRI systems and enhances surgical safety by security mechanisms.
期刊介绍：
The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.
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