ArticleAnnals of biomedical engineering2018
Toward Semi-autonomous Cryoablation of Kidney Tumors via Model-Independent Deformable Tissue Manipulation Technique.
Article in Annals of biomedical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 32 citations in OpenAlex.
- Machine learning in the optimization of robotics in the operative field.Current opinion in urology · 2020Review
- Optical Coherence Tomography-Guided Robotic Ophthalmic Microsurgery via Reinforcement Learning from Demonstration.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2020Article
- SCADE: Simultaneous Sensor Calibration and Deformation Estimation of FBG-Equipped Unmodeled Continuum Manipulators.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2020Article
- Inroads Toward Robot-Assisted Internal Fixation of Bone Fractures Using a Bendable Medical Screw and the Curved Drilling Technique.Proceedings of the ... IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics · 2018Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
We present a novel semi-autonomous clinician-in-the-loop strategy to perform the laparoscopic cryoablation of small kidney tumors. To this end, we introduce a model-independent bimanual tissue manipulation technique. In this method, instead of controlling the robot, which inserts and steers the needle in the deformable tissue (DT), the cryoprobe is introduced to the tissue after accurate manipulation of a target point on the DT to the desired predefined insertion location of the probe. This technique can potentially reduce the risk of kidney fracture, which occurs due to the incorrect insertion of the probe within the kidney. The main challenge of this technique, however, is the unknown deformation behavior of the tissue during its manipulation. To tackle this issue, we proposed a novel real-time deformation estimation method and a vision-based optimization framework, which do not require prior knowledge about the tissue deformation and the intrinsic/extrinsic parameters of the vision system. To evaluate the performance of the proposed method using the da Vinci Research Kit, we performed experiments on a deformable phantom and an ex vivo lamb kidney and evaluated our method using novel manipulability measures. Experiments demonstrated successful real-time estimation of the deformation behavior of these DTs while manipulating them to the desired insertion location(s).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.