Khosravi Bardia, Rouzrokh Pouria, Erickson Bradley J, Garner Hillary W, Wenger Doris E, Taunton Michael J, Wyles Cody C
Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA.
Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Arthroplast Today. 2024 Sep 23;29:101503. doi: 10.1016/j.artd.2024.101503. eCollection 2024 Oct.
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.
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.
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 identified 6 characteristics differentiating races, including interacetabular distance, osteoarthritis degree, obturator foramina shape, femoral neck-shaft angle, pelvic ring shape, and femoral cortical thickness.
This study demonstrates the potential of generative models for understanding disparities in medical imaging data sets. By visualizing race-based differences, this method aids in identifying bias in downstream tasks, fostering the development of fairer healthcare practices.
医疗数据集的差异会使偏差长期存在，尤其是在训练深度学习模型时，这可能导致临床应用中的结果出现偏差。了解这些偏差对于公平医疗技术的发展至关重要。本研究采用生成式深度学习技术，探索并理解全髋关节置换术患者基于种族的影像学差异。
利用一个大型机构登记处，我们回顾性分析了全髋关节置换术患者的骨盆X光片，以人口统计学和图像特征为特征。去噪扩散概率模型根据人口统计学和成像特征生成X光片。弗雷歇因ception距离评估生成图像的质量，显示生成图像的多样性和真实感。生成了60个过渡视频，展示了将白人骨盆转变为最接近的非裔美国人骨盆，反之亦然，同时控制患者的性别、年龄和体重指数。两名专家外科医生和两名放射科医生仔细研究了这些视频，以了解两个种族X光片中存在的系统差异。
我们的数据集包括480407张骨盆X光片，白人患者比非裔美国人占比更多。生成式去噪扩散概率模型创建了高质量图像，弗雷歇因ception距离达到6.8。专家们确定了6个区分种族的特征，包括髋臼间距离、骨关节炎程度、闭孔形状、股骨颈干角、骨盆环形状和股骨皮质厚度。
本研究证明了生成模型在理解医学影像数据集差异方面的潜力。通过可视化基于种族的差异，这种方法有助于识别下游任务中的偏差，促进更公平医疗实践的发展。
