Robotized Catheter With Enhanced Distal Targeting for Peripheral Pulmonary Biopsy

Kato, Takahisa; King, Franklin; Takagi, Kiyoshi; Hata, Nobuhiko

doi:10.1109/tmech.2020.3040314

Cited by 11 publications

(2 citation statements)

References 55 publications

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“…The achieved average fiducial registration error was 0.72 mm. Bronchoscopic operations were performed using a snake robot developed in our lab (Dupourqué et al, 2019;Kato et al, 2020) with the OVM6946 bronchoscopic camera (OmniVision, CA, USA). The training sequence was recorded by inserting the bronchoscope into Right Upper Lobe, Right Middle Lobe, Left Upper Lobe and Left Middle Lobe of pig lung specimen #1.…”

Section: Bronchoscopic Operations Using Ex-vivo Specimensmentioning

confidence: 99%

Visually Navigated Bronchoscopy using three cycle-Consistent generative adversarial network for depth estimation

Banach

King

Masaki

et al. 2021

Medical Image Analysis

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Background-Electromagnetically Navigated Bronchoscopy (ENB) is currently the state-of-the art diagnostic and interventional bronchoscopy. CT-to-body divergence is a critical hurdle in ENB, causing navigation error and ultimately limiting the clinical efficacy of diagnosis and treatment. In this study, Visually Navigated Bronchoscopy (VNB) is proposed to address the aforementioned issue of CT-to-body divergence.Materials and Methods-We extended and validated an unsupervised learning method to generate a depth map directly from bronchoscopic images using a Three Cycle-Consistent Generative Adversarial Network (3cGAN) and registering the depth map to preprocedural CTs. We tested the working hypothesis that the proposed VNB can be integrated to the navigated bronchoscopic system based on 3D Slicer, and accurately register bronchoscopic images to pre-procedural CTs to navigate transbronchial biopsies. The quantitative metrics to asses the hypothesis we set was Absolute Tracking Error (ATE) of the tracking and the Target Registration Error (TRE) of the total navigation system. We validated our method on phantoms produced from the pre-procedural CTs of five patients who underwent ENB and on two ex-vivo pig lung specimens.Results-The ATE using 3cGAN was 6.2 +/− 2.9 [mm]. The ATE of 3cGAN was statistically significantly lower than that of cGAN, particularly in the trachea and lobar bronchus (p < 0.001). The TRE of the proposed method had a range of 11.7 to 40.5 [mm]. The TRE computed by 3cGAN was statistically significantly smaller than those computed by cGAN in two of the five cases enrolled (p < 0.05).Conclusion-VNB, using 3cGAN to generate the depth maps was technically and clinically feasible. While the accuracy of tracking by cGAN was acceptable, the TRE warrants further investigation and improvement.

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Section: Bronchoscopic Operations Using Ex-vivo Specimensmentioning

confidence: 99%

Visually Navigated Bronchoscopy using three cycle-Consistent generative adversarial network for depth estimation

Banach

King

Masaki

et al. 2021

Medical Image Analysis

Self Cite

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“…These findings suggest that these platforms can play a transformative role in the future management of pulmonary conditions. In addition to the Monarch and Ion platforms, several other bronchoscope robotic systems are under development by academic institutions or have entered early-stage research to address sensing and control issues for doctors [22][23][24][25][26][27] . Nevertheless, despite its advantages, current telerobotic bronchoscopy faces several challenges, including a steep learning curve and lack of autonomy.…”

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confidence: 99%

AI co-pilot bronchoscope robot

Zhang,

Liu,

Xiang

et al. 2024

Nat Commun

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The unequal distribution of medical resources and scarcity of experienced practitioners confine access to bronchoscopy primarily to well-equipped hospitals in developed regions, contributing to the unavailability of bronchoscopic services in underdeveloped areas. Here, we present an artificial intelligence (AI) co-pilot bronchoscope robot that empowers novice doctors to conduct lung examinations as safely and adeptly as experienced colleagues. The system features a user-friendly, plug-and-play catheter, devised for robot-assisted steering, facilitating access to bronchi beyond the fifth generation in average adult patients. Drawing upon historical bronchoscopic videos and expert imitation, our AI–human shared control algorithm enables novice doctors to achieve safe steering in the lung, mitigating misoperations. Both in vitro and in vivo results underscore that our system equips novice doctors with the skills to perform lung examinations as expertly as seasoned practitioners. This study offers innovative strategies to address the pressing issue of medical resource disparities through AI assistance.

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