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

ABSTRACT:

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

SUBMITTER: Khosravi B 

PROVIDER: S-EPMC11456877 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature