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December 14, 2017

Virtual Incision gets funding for miniaturised surgical robots

US-based Virtual Incision has secured an $18m Series B funding for the development of miniaturised surgical robots.

US-based Virtual Incision has secured an $18m Series B funding for the development of miniaturised surgical robots.

Led by Sinopharm Capital and Bluestem Capital, the funding round involved additional investors such as PrairieGold Venture Partners.

Virtual Incision intends to use the funds to support the 510(k) premarket notification submission for its next-generation miniaturised robotically assisted surgical device (RASD) to the US Food and Drug Administration (FDA).

Virtual Incision president and CEO John Murphy said: “Closing this round of financing launches the development of our miniaturised surgical robot toward our next key milestones, including submission to the FDA for market clearance.”

The firm is developing the RASD to allow less-invasion during general surgery abdominal procedures that are currently performed using large, open incisions.

"Our robot is designed to be moved from suite to suite as needed, providing the surgical staff with much more flexibility when it comes to the tools being used."

RASD is designed as a small, dexterous and self-contained surgical robot that enables insertion through an umbilical incision in the abdomen.

The technology is said to leverage existing tools and techniques and does not require a special operating room or infrastructure.

Virtual Incision co-founder and chief technology officer Shane Farritor said: “The large footprint and dedicated operating suite required to house multi-port or single-port mainframe robots can limit access, especially in smaller hospitals that have only one robotic surgery system.

“In contrast, our robot is designed to be moved from suite to suite as needed, providing the surgical staff with much more flexibility when it comes to the tools being used during a procedure.”

The firm’s robotic platform employs artificial intelligence and machine learning technologies to track and guide instrument usage.

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