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用于硬膜外干预的增强现实增强型机器人系统的设计与评估

Design and Evaluation of Augmented Reality-Enhanced Robotic System for Epidural Interventions.

作者信息

Sayadi Amir, Cecere Renzo, Barralet Jake, Feldman Liane S, Hooshiar Amir

机构信息

Surgical Performance Enhancement and Robotics (SuPER) Centre, Department of Surgery, McGill University, Montreal, QC H3A 0G4, Canada.

出版信息

Sensors (Basel). 2024 Dec 13;24(24):7959. doi: 10.3390/s24247959.

DOI:10.3390/s24247959
PMID:39771696
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11679673/
Abstract

The epidural injection is a medical intervention to inject therapeutics directly into the vicinity of the spinal cord for pain management. Because of its proximity to the spinal cord, imprecise insertion of the needle may result in irreversible damage to the nerves or spinal cord. This study explores enhancing procedural accuracy by integrating a telerobotic system and augmented reality (AR) assistance. Tele-kinesthesia is achieved using a leader-follower integrated system, and stable force feedback is provided using a novel impedance-matching force rendering approach. In this domain, augmented reality employs a magnetic-tracker-based approach for real-time 3D model projection onto the patient's body, aiming to augment the physician's visual field and improve needle insertion accuracy. Preliminary results indicate that our AR-enhanced robotic system may reduce the cognitive load and improve the accuracy of ENI, highlighting the promise of AR technologies in complex medical procedures. However, further studies with larger sample sizes and more diverse clinical settings must comprehensively validate these findings. This work lays the groundwork for future research into integrating AR into medical robotics, potentially transforming clinical practices by enhancing procedural safety and efficiency.

摘要

硬膜外注射是一种将治疗药物直接注射到脊髓附近以进行疼痛管理的医学干预措施。由于其靠近脊髓,针头插入不准确可能会导致神经或脊髓的不可逆损伤。本研究探索通过集成远程机器人系统和增强现实(AR)辅助来提高操作准确性。使用主从集成系统实现远程动觉,并采用新颖的阻抗匹配力渲染方法提供稳定的力反馈。在该领域,增强现实采用基于磁跟踪器的方法将实时3D模型投影到患者身体上,旨在扩大医生的视野并提高针头插入的准确性。初步结果表明,我们的AR增强机器人系统可能会降低认知负荷并提高硬膜外神经阻滞(ENI)的准确性,凸显了AR技术在复杂医疗程序中的前景。然而,需要在更大样本量和更多样化临床环境下进行进一步研究,以全面验证这些发现。这项工作为未来将AR集成到医疗机器人中的研究奠定了基础,有望通过提高操作安全性和效率来改变临床实践。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5630/11679673/341a7d890521/sensors-24-07959-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5630/11679673/0d28a2f0d476/sensors-24-07959-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5630/11679673/da38ac6a3a13/sensors-24-07959-g007a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5630/11679673/341a7d890521/sensors-24-07959-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5630/11679673/0d28a2f0d476/sensors-24-07959-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5630/11679673/77082874e25a/sensors-24-07959-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5630/11679673/7983825cab2b/sensors-24-07959-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5630/11679673/341a7d890521/sensors-24-07959-g008.jpg

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