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Image-Guided Surgical Robotic System for Percutaneous Reduction of Joint Fractures

Overview of attention for article published in Annals of Biomedical Engineering, August 2017
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Title
Image-Guided Surgical Robotic System for Percutaneous Reduction of Joint Fractures
Published in
Annals of Biomedical Engineering, August 2017
DOI 10.1007/s10439-017-1901-x
Pubmed ID
Authors

Giulio Dagnino, Ioannis Georgilas, Samir Morad, Peter Gibbons, Payam Tarassoli, Roger Atkins, Sanja Dogramadzi

Abstract

Complex joint fractures often require an open surgical procedure, which is associated with extensive soft tissue damages and longer hospitalization and rehabilitation time. Percutaneous techniques can potentially mitigate these risks but their application to joint fractures is limited by the current sub-optimal 2D intra-operative imaging (fluoroscopy) and by the high forces involved in the fragment manipulation (due to the presence of soft tissue, e.g., muscles) which might result in fracture malreduction. Integration of robotic assistance and 3D image guidance can potentially overcome these issues. The authors propose an image-guided surgical robotic system for the percutaneous treatment of knee joint fractures, i.e., the robot-assisted fracture surgery (RAFS) system. It allows simultaneous manipulation of two bone fragments, safer robot-bone fixation system, and a traction performing robotic manipulator. This system has led to a novel clinical workflow and has been tested both in laboratory and in clinically relevant cadaveric trials. The RAFS system was tested on 9 cadaver specimens and was able to reduce 7 out of 9 distal femur fractures (T- and Y-shape 33-C1) with acceptable accuracy (≈1 mm, ≈5°), demonstrating its applicability to fix knee joint fractures. This study paved the way to develop novel technologies for percutaneous treatment of complex fractures including hip, ankle, and shoulder, thus representing a step toward minimally-invasive fracture surgeries.

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Geographical breakdown

Country Count As %
Unknown 71 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 18%
Student > Master 11 15%
Student > Bachelor 8 11%
Student > Postgraduate 7 10%
Student > Doctoral Student 3 4%
Other 11 15%
Unknown 18 25%
Readers by discipline Count As %
Medicine and Dentistry 18 25%
Nursing and Health Professions 9 13%
Engineering 9 13%
Computer Science 5 7%
Biochemistry, Genetics and Molecular Biology 2 3%
Other 3 4%
Unknown 25 35%