Novel dilation-erosion labeling technique allows for rapid, accurate and adjustable alignment measurements in primary TKA.
Journal:
Computers in biology and medicine
PMID:
39689521
Abstract
BACKGROUND: Optimal implant position and alignment remains a controversial, yet critical topic in primary total knee arthroplasty (TKA). Future study of ideal implant position will require the ability to efficiently measure component positions at scale. Previous algorithms have limited accuracy, do not allow for human oversight and correction in deployment, and require extensive training time and dataset. Therefore, the purpose of this study was to develop and validate a machine learning model that can accurately automate, with surgeon directed adjustment, implant position annotation.