Robotic Arthroscopy: The Uncertainty in Internal Knee Joint Measurement

• Main Authors: Mario Strydom, Ross Crawford, Jonathan Roberts, Anjali Jaiprakash
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Computer vision; joint motion stereo; image segmentation; optical tracking; error analysis; measurement uncertainty
• Online Access: https://ieeexplore.ieee.org/document/8902057/

We report a study that developed algorithms to measure the dimension and uncertainty range of free space inside the knee joint for the purpose of minimally invasive surgery. During knee arthroscopy, the patient's leg position is continuously adjusted to create the space for surgical instruments inside the joint. Surgeons 'feel' the force they apply to the leg and estimate the joint space from a 2D video. In many cases, they overestimate the instrument gap, resulting in damaging to the knee joint by pushing instruments through a gap that is too small. We used cadaveric experiments to inform the noise induced by the sensors and image processing steps, to derive an error point-cloud in a simulated environment. From the point-cloud, we calculate the instrument gap range inside the knee joint. For a selected surgical instrument gap size, the measurement algorithm is accurate to less than a millimetre. However, measurement errors introduce an uncertainty of 14%. The performance of our algorithms demonstrates the use of a single-lens arthroscope to measure the instrument gap to provide feedback to a surgeon or enable control of a robotic leg manipulator.