As the volume of primary total knee arthroplasties (TKA) continues to escalate worldwide, containing resource consumption is becoming a critical objective for modern healthcare systems. This is especially true as advanced workflowsâincluding computer navigation, robotics, custom implants, and patient-specific instrumentationâshift from a luxury to a baseline standard of care. To drive their accuracy, these advanced technologies typically mandate preoperative 3D imaging via a computed tomography (CT) scan.
Unfortunately, traditional 3D workflows impose a severe triple-threat: they add substantial operational cost, require additional clinic visits, and expose the patient to unnecessary ion radiation. We evaluated whether a novel machine learning algorithm could bypass these hurdles by converting standard, inexpensive two-dimensional plain radiographs into 3D bone reconstructions of equivalent diagnostic accuracy.
