From Bench to Bedside: The First Live Robotic Surgery on the dVRK to Enable Remote Telesurgery with Motion Scaling
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Abstract (arXiv)
Innovations from surgical robotic research rarely translates to live surgery due to the significant difference between the lab and a live environment. Live environments require considerations that are often overlooked during early stages of research such as surgical staff, surgical procedure, and the challenges of working with live tissue. One such example is the da Vinci Research Kit (dVRK) which is used by over 40 robotics research groups and represents an open-sourced version of the da Vinci Surgical System. Despite dVRK being available for nearly a decade and the ideal candidate for translating research to practice on over 5,000 da Vinci Systems used in hospitals around the world, not one live surgery has been conducted with it. In this paper, we address the challenges, considerations, and solutions for translating surgical robotic research from bench-to-bedside. This is explained from the perspective of a remote telesurgery scenario where motion scaling solutions previously experimented in a lab setting are translated to a live pig surgery. This study presents results from the first ever use of a dVRK in a live animal and discusses how the surgical robotics community can approach translating their research to practice.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Sep 2021.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 1.
4 papers.
- 2022Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Trackingcited by 19
- 2024Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitivescited by 3
- 2023SuPerPM: A Surgical Perception Framework Based on Deep Point Matching Learned from Physical Constrained Simulation Datacited by 2
- 2023SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimationcited by 1
- 2024Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitives8 Mar 2024
- 2023SuPerPM: A Surgical Perception Framework Based on Deep Point Matching Learned from Physical Constrained Simulation Data25 Sep 2023
- 2023SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation31 Mar 2023
- 2022Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking29 Oct 2022