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.