Development of novel machine learning algorithms for registration of point clouds and tracking surgical tools

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This partnership has two thrusts to solve two overarching problems of registration and tracking in total knee arthroplasty (TKA). Registration and tracking will determine the location and angle for cutting the femur bone, an action that cannot be undone. As such, both thrusts are of critical importance. Thrust 1 will focus on robust point cloud registration, classification of the accuracy of the registration, and personalized point selection strategy. This thrust will also be carried out in two aims. In the first aim, we first develop novel registration techniques for the mathematically challenging problem of registration. We will then develop novel methods that can automatically assess the accuracy of the registration, so that the procedure can only proceed if registration is accurate. In the second aim, we will develop a novel sampling strategy that is personalized to each bone, so that optimal registration results can be achieved with the selection of few points. In both aims of this thrust, we will develop novel techniques that use both traditional Machine Learning (ML) and Deep Learning (DL) algorithms. Thrust 2 will focus on robust tracking of surgical tools, and automatically detecting if tracking is inaccurate. This thrust will be carried out in two specific aims, wherein the first aim focuses on classical image processing and ML techniques, and the second aim focuses on DL. In addition to improving the tracking accuracy, our proposed method will automatically detect if one of the tools is not being accurately tracked, so that the surgeon can clean the affected tool.This research partnership can lead to development of novel technologies that enable more accurate and reliable localization and registration in TKA, potentially improving the outcome of the surgery. It will also lead to training of six highly qualified personnel (HQP) working in close collaboration with a Canadian company.