Advances in Computer Assisted Mosaic Arthroplasty

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Cartilage degeneration is a widespread problem which occurs predominantly in the knee, ankle, and shoulder. For younger, active patients, early cartilage replacement can result in a much improved quality of life and can delay or eliminate the eventual need for prosthetic joint replacement, with substantial savings to the Canadian healthcare system. Mosaic arthroplasty is a surgical procedure in which plugs of cartilage and bone are transplanted from non-load-bearing areas of the joint to the site of the cartilage defect. We have shown in previous research that computer-assisted mosaic arthroplasty (CAMA) results in better clinical outcomes than conventional mosaic arthroplasty. The proposed research, which will train four graduate and three undergraduate students, will develop computer algorithms and user interfaces to improve the delivery of CAMA in three areas: 1. Almost all computer-assisted operations involve a patient CT or MRI scan, followed by a pre-operative planning stage, followed by the operation. We will collapse the scanning and planning stages of CAMA into the operation itself: The surgeon will intraoperatively scan the cartilage surface with a tracked arthroscope probe, will use a planning algorithm to develop a surgical plan, and will execute the plan using computer tracking and guidance. This can reduce waiting times and can reduce patient exposure to ionizing radiation. 2. For arthroscopic CAMA, we will provide the surgeon with an augmented reality guidance display, where the surgical plan is superimposed on the live arthroscope video, shown on a display beside the patient. This will reduce the cognitive burden on the surgeon and may make the success of the operation more likely. 3. One method of CAMA tool guidance uses patient-specific guides, which are manufactured on a 3D printer; their bottom side fits tightly to the patient's cartilage surface and their top side contains holes to guide the surgical tools. We will develop a computer algorithm that designs the guides automatically, encoding the operator's hidden heuristic knowledge and saving substantial operator time over the current manual method.