ArticleFrontiers in robotics and AI2019
Design and Integration of Electrical Bio-impedance Sensing in Surgical Robotic Tools for Tissue Identification and Display.
Article in Frontiers in robotics and AI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Advances in Computer Vision That Are Enabling More Autonomous Actions in Surgery: A Systematic Review of the Literature.Sensors (Basel, Switzerland) · 2022Pooled it
- Multi-source information fusion using CNN-LSTM-Attention for bone layer recognition in robotic orthopedic grinding.Medical & biological engineering & computing · 2026Article
- Revolutionising Breast Surgery: A Comprehensive Review of Robotic Innovations in Breast Surgery and Reconstruction.Cureus · 2024Review
- Electrical Impedance Characterization ofJournal of electrical bioimpedance · 2021Article
- Design of a Drop-in EBI Sensor Probe for Abnormal Tissue Detection in Minimally Invasive Surgery.Journal of electrical bioimpedance · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of intra-operative sensors into surgical robots is a hot research topic since this can significantly facilitate complex surgical procedures by enhancing surgical awareness with real-time tissue information. However, currently available intra-operative sensing technologies are mainly based on image processing and force feedback, which normally require heavy computation or complicated hardware modifications of existing surgical tools. This paper presents the design and integration of electrical bio-impedance sensing into a commercial surgical robot tool, leading to the creation of a novel smart instrument that allows the identification of tissues by simply touching them. In addition, an advanced user interface is designed to provide guidance during the use of the system and to allow augmented-reality visualization of the tissue identification results. The proposed system imposes minor hardware modifications to an existing surgical tool, but adds the capability to provide a wealth of data about the tissue being manipulated. This has great potential to allow the surgeon (or an autonomous robotic system) to better understand the surgical environment. To evaluate the system, a series of
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Registered trials
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