Neil Antonson, Brandt Buckner, Beau Konigsberg, Curtis Hartman, Kevin Garvin, Beau Kildow
BACKGROUND: The anticipated growth of total hip arthroplasty (THA) will result in an increased need for revision THA. Pre-operative planning, including identifying current implants, is critical for successful revision surgery. Artificial intelligence (AI) is promising for aiding clinical decision-making, including hip implant identification. However, previous studies have limitations such as small datasets, dissimilar stem designs, limited scalability, and the need for AI expertise. To address these limitations, we developed a novel technique to generate large datasets, tested radiographically similar stems, and demonstrated scalability utilizing a no-code machine learning solution...
February 7, 2024: Journal of Arthroplasty