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. 2024 Dec;20(6):e70017.
doi: 10.1002/rcs.70017.

A Respiratory Signal Monitoring Method Based on Dual-Pathway Deep Learning Networks in Image-Guided Robotic-Assisted Intervention System

Xiaodong Wang  1   2 , Jianjun Zhu  2   3 , Yong Wang  4 , Cheng Wang  2 , Peng Chen  2   5 , Pengju Lyu  2 , Jun Xu  1 , Gao-Jun Teng  1   3
Affiliations

A Respiratory Signal Monitoring Method Based on Dual-Pathway Deep Learning Networks in Image-Guided Robotic-Assisted Intervention System

Xiaodong Wang et al. Int J Med Robot. 2024 Dec.

Abstract

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.

Keywords: dual‐pathway deep learning networks; image‐guided robotic‐assisted intervention; respiratory monitoring; respiratory pattern classification.

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