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Martin JW, Barducci L, Scaglioni B, Norton JC, Winters C, Subramanian V, Arezzo A, Obstein KL, Valdastri P. Robotic Autonomy for Magnetic Endoscope Biopsy. IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 2022; 4:599-607. [PMID: 36249558 PMCID: PMC9555223 DOI: 10.1109/tmrb.2022.3187028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Magnetically actuated endoscopes are currently transitioning in to clinical use for procedures such as colonoscopy, presenting numerous benefits over their conventional counterparts. Intelligent and easy-to-use control strategies are an essential part of their clinical effectiveness due to the un-intuitive nature of magnetic field interaction. However, work on developing intelligent control for these devices has mainly been focused on general purpose endoscope navigation. In this work, we investigate the use of autonomous robotic control for magnetic colonoscope intervention via biopsy, another major component of clinical viability. We have developed control strategies with varying levels of robotic autonomy, including semi-autonomous routines for identifying and performing targeted biopsy, as well as random quadrant biopsy. We present and compare the performance of these approaches to magnetic endoscope biopsy against the use of a standard flexible endoscope on bench-top using a colonoscopy training simulator and silicone colon model. The semi-autonomous routines for targeted and random quadrant biopsy were shown to reduce user workload with comparable times to using a standard flexible endoscope.
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Affiliation(s)
| | | | | | | | - Conchubhair Winters
- Leeds Teaching Hospitals NHS Trust, St James’s University Hospital, Leeds, UK
| | | | - Alberto Arezzo
- Department of Surgical Sciences, University of Torino, Turin, Italy
| | - Keith L. Obstein
- STORM Lab USA, Vanderbilt University, Nashville, TN, USA, Vanderbilt University Medical Center, Nashville, TN, USA
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Nguyen KT, Go G, Zhen J, Hoang MC, Kang B, Choi E, Park JO, Kim CS. Locomotion and disaggregation control of paramagnetic nanoclusters using wireless electromagnetic fields for enhanced targeted drug delivery. Sci Rep 2021; 11:15122. [PMID: 34302003 PMCID: PMC8302636 DOI: 10.1038/s41598-021-94446-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 07/12/2021] [Indexed: 11/09/2022] Open
Abstract
Magnetic nanorobots (MNRs) based on paramagnetic nanoparticles/nanoclusters for the targeted therapeutics of anticancer drugs have been highlighted for their efficiency potential. Controlling the locomotion of the MNRs is a key challenge for effective delivery to the target legions. Here, we present a method for controlling paramagnetic nanoclusters through enhanced tumbling and disaggregation motions with a combination of rotating field and gradient field generated by external electromagnets. The mechanism is carried out via an electromagnetic actuation system capable of generating MNR motions with five degrees of freedom in a spherical workspace without singularity. The nanocluster swarm structures can successfully pass through channels to the target region where they can disaggregate. The results show significantly faster response and higher targeting rate by using rotating magnetic and gradient fields. The mean velocities of the enhanced tumbling motion are twice those of the conventional tumbling motion and approximately 130% higher than the gradient pulling motion. The effects of each fundamental factor on the locomotion are investigated for further MNR applications. The locomotion speed of the MNR could be predicted by the proposed mathematical model and agrees well with experimental results. The high access rate and disaggregation performance insights the potentials for targeted drug delivery application.
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Affiliation(s)
- Kim Tien Nguyen
- Korea Institute of Medical Microrobotics (KIMIRo), 43-26 Cheomdangwagi-ro, Buk-gu, Gwangju, Korea
| | - Gwangjun Go
- Korea Institute of Medical Microrobotics (KIMIRo), 43-26 Cheomdangwagi-ro, Buk-gu, Gwangju, Korea
| | - Jin Zhen
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan, China
| | - Manh Cuong Hoang
- Korea Institute of Medical Microrobotics (KIMIRo), 43-26 Cheomdangwagi-ro, Buk-gu, Gwangju, Korea.,School of Mechanical Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju, Korea
| | - Byungjeon Kang
- Korea Institute of Medical Microrobotics (KIMIRo), 43-26 Cheomdangwagi-ro, Buk-gu, Gwangju, Korea.,College of AI Convergence, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju, Korea
| | - Eunpyo Choi
- Korea Institute of Medical Microrobotics (KIMIRo), 43-26 Cheomdangwagi-ro, Buk-gu, Gwangju, Korea.,School of Mechanical Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju, Korea
| | - Jong-Oh Park
- Korea Institute of Medical Microrobotics (KIMIRo), 43-26 Cheomdangwagi-ro, Buk-gu, Gwangju, Korea.
| | - Chang-Sei Kim
- Korea Institute of Medical Microrobotics (KIMIRo), 43-26 Cheomdangwagi-ro, Buk-gu, Gwangju, Korea. .,School of Mechanical Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju, Korea.
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