PDF(Pubmed)
Abstract：
AI-powered segmentation of hip and knee bony anatomy has revolutionized orthopedics, transforming pre-operative planning and post-operative assessment. Despite the remarkable advancements in AI algorithms for medical imaging, the potential for biases inherent within these models remains largely unexplored. This study tackles these concerns by thoroughly re-examining AI-driven segmentation for hip and knee bony anatomy. While advanced imaging modalities like CT and MRI offer comprehensive views, plain radiographs (X-rays) predominate the standard initial clinical assessment due to their widespread availability, low cost, and rapid acquisition. Hence, we focused on plain radiographs to ensure the utilization of our contribution in diverse healthcare settings, including those with limited access to advanced imaging technologies. This work provides insights into the underlying causes of biases in AI-based knee and hip image segmentation through an extensive evaluation, presenting targeted mitigation strategies to alleviate biases related to sex, race, and age, using an automatic segmentation that is fair, impartial, and safe in the context of AI. Our contribution can enhance inclusivity, ethical practices, equity, and an unbiased healthcare environment with advanced clinical outcomes, aiding decision-making and osteoarthritis research. Furthermore, we have made all the codes and datasets publicly and freely accessible to promote open scientific research.
摘要：
AI驱动的髋关节和膝关节骨解剖分割彻底改变了骨科，转变术前计划和术后评估。尽管人工智能算法在医学成像方面取得了显著进步，这些模型中固有的偏见的潜力在很大程度上仍未被探索。这项研究通过彻底重新检查AI驱动的髋关节和膝关节骨解剖分割来解决这些问题。虽然先进的成像模式，如CT和MRI提供全面的观点，普通X光片（X射线）由于其广泛的可用性，在标准的初始临床评估中占主导地位，低成本,快速收购。因此，我们专注于普通射线照片，以确保在不同的医疗保健环境中利用我们的贡献，包括那些对先进成像技术的有限访问。这项工作通过广泛的评估，提供了对基于AI的膝盖和臀部图像分割中偏见的根本原因的见解，提出有针对性的缓解策略，以减轻与性有关的偏见，种族,和年龄,使用公平的自动分割，公正,在AI的背景下也是安全的。我们的贡献可以增强包容性，伦理实践，股本，以及具有先进临床结果的公正的医疗环境，辅助决策和骨关节炎研究。此外，我们已经公开和免费获取所有代码和数据集，以促进开放的科学研究。
