Analyzing Racial Differences in Imaging Joint Replacement Registries Using Generative Artificial Intelligence: Advancing Orthopaedic Data Equity

作者：Bardia Khosravi, Pouria Rouzrokh, Bradley J. Erickson, Hillary W. Garner, Doris E. Wenger, Michael J. Taunton, Cody C. Wyles · 发表于：Arthroplasty Today · 年份：2024 · DOI：10.1016/j.artd.2024.101503 · 被引用次数：15 · 研究领域：Artificial Intelligence in Healthcare and Education、Machine Learning in Healthcare、Total Knee Arthroplasty Outcomes

Background: Discrepancies in medical data sets can perpetuate bias, especially when training deep learning models, potentially leading to biased outcomes in clinical applications. Understanding these biases is crucial for the development of equitable healthcare technologies. This study employs generative deep learning technology to explore and understand radiographic differences based on race among patients undergoing total hip arthroplasty. Methods: Utilizing a large institutional registry, we retrospectively analyzed pelvic radiographs from total hip arthroplasty patients, characterized by demographics and image features. Denoising diffusion probabilistic models generated radiographs conditioned on demographic and imaging characteristics. Fréchet Inception Distance assessed the generated image quality, showing the diversity and realism of the generated images. Sixty transition videos were generated that showed transforming White pelvises to their closest African American counterparts and vice versa while controlling for patients' sex, age, and body mass index. Two expert surgeons and 2 radiologists carefully studied these videos to understand the systematic differences that are present in the 2 races' radiographs. Results: Our data set included 480,407 pelvic radiographs, with a predominance of White patients over African Americans. The generative denoising diffusion probabilistic model created high-quality images and reached an Fréchet Inception Distance of 6.8. Experts id...