Augmented Reality and Robotic-Assistance for Percutaneous Nephrolithotomy

• Published in: IEEE robotics and automation letters Vol. 5; no. 3; pp. 4556 - 4563
• Main Authors: Ferraguti, Federica, Minelli, Marco, Farsoni, Saverio, Bazzani, Stefano, Bonfe, Marcello, Vandanjon, Alexandre, Puliatti, Stefano, Bianchi, Giampaolo, Secchi, Cristian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-07-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Augmented reality; Calculi; human performance augmentation; Learning curves; medical robots and systems; Needles; Robots; Solid modeling; Surgeons; Surgery; Surgical robotics: laparoscopy; Three-dimensional displays; Trajectory
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2020.3002216

Percutaneous nephrolithotomy (PCNL) is considered the gold standard for the treatment of patients with renal stones larger than 20 mm in diameter. The success and treatment outcomes of the surgery are very well known to be highly dependent on the precision and accuracy of the puncture step, since it must allow to reach the stone with a precise and direct path. Thus, performing the renal access during PCNL is the most crucial and challenging step of the procedure with the steepest learning curve. In this letter, we propose an innovative solution, based on an AR application combined with a robotic system, that can assist both an expert surgeon in improving the performance of the surgical operation and a novel surgeon in strongly reducing his/her learning curve. The proposed system is validated on a setup including a KUKA LWR 4+ robot and the Microsoft HoloLens as augmented reality headset, through experiments performed by a sample of 11 users.