Mitros Zisos, Thamo Balint, Bergeles Christos, da Cruz Lyndon, Dhaliwal Kevin, Khadem Mohsen
Robotics and Vision in Medicine (RViM) Lab, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom.
Front Robot AI. 2021 May 3;8:611866. doi: 10.3389/frobt.2021.611866. eCollection 2021.
In this paper, we design and develop a novel robotic bronchoscope for sampling of the distal lung in mechanically-ventilated (MV) patients in critical care units. Despite the high cost and attributable morbidity and mortality of MV patients with pneumonia which approaches 40%, sampling of the distal lung in MV patients suffering from range of lung diseases such as Covid-19 is not standardised, lacks reproducibility and requires expert operators. We propose a robotic bronchoscope that enables repeatable sampling and guidance to distal lung pathologies by overcoming significant challenges that are encountered whilst performing bronchoscopy in MV patients, namely, limited dexterity, large size of the bronchoscope obstructing ventilation, and poor anatomical registration. We have developed a robotic bronchoscope with 7 Degrees of Freedom (DoFs), an outer diameter of 4.5 mm and inner working channel of 2 mm. The prototype is a push/pull actuated continuum robot capable of dexterous manipulation inside the lung and visualisation/sampling of the distal airways. A prototype of the robot is engineered and a mechanics-based model of the robotic bronchoscope is developed. Furthermore, we develop a novel numerical solver that improves the computational efficiency of the model and facilitates the deployment of the robot. Experiments are performed to verify the design and evaluate accuracy and computational cost of the model. Results demonstrate that the model can predict the shape of the robot in <0.011s with a mean error of 1.76 cm, enabling the future deployment of a robotic bronchoscope in MV patients.
在本文中，我们设计并开发了一种新型机器人支气管镜，用于对重症监护病房中接受机械通气（MV）的患者的远端肺部进行采样。尽管MV患者患肺炎的成本高昂，且发病率和死亡率接近40%，但对于患有诸如新冠病毒-19等一系列肺部疾病的MV患者，其远端肺部的采样并未标准化，缺乏可重复性，且需要专业操作人员。我们提出了一种机器人支气管镜，通过克服在为MV患者进行支气管镜检查时遇到的重大挑战，即灵活性有限、支气管镜尺寸大阻碍通气以及解剖配准不佳，实现对远端肺部病变的可重复采样和引导。我们开发了一种具有7个自由度（DoF）、外径为4.5毫米且内部工作通道为2毫米的机器人支气管镜。该原型是一个推拉驱动的连续体机器人，能够在肺内进行灵巧操作，并对远端气道进行可视化/采样。设计了该机器人的一个原型，并开发了机器人支气管镜的基于力学的模型。此外，我们开发了一种新型数值求解器，提高了模型的计算效率，并便于机器人的部署。进行了实验以验证设计并评估模型的准确性和计算成本。结果表明，该模型能够在<0.011秒内预测机器人的形状，平均误差为1.76厘米，这使得未来能够在MV患者中部署机器人支气管镜。
