In medical imaging, artificial intelligence (AI) is increasingly being used to automate routine tasks. However, these algorithms can exhibit and exacerbate biases which lead to disparate performances between protected groups. We investigate the impact of model choice on how imbalances in subject sex and race in training datasets affect AI-based cine cardiac magnetic resonance image segmentation. We evaluate three convolutional neural network-based models and one vision transformer model. We find significant sex bias in three of the four models and racial bias in all of the models. However, the severity and nature of the bias varies between the models, highlighting the importance of model choice when attempting to train fair AI-based segmentation models for medical imaging tasks.

翻译：在医学影像领域，人工智能正日益广泛地用于自动化常规任务。然而，这些算法可能显现并加剧偏差，导致不同受保护群体间的性能差异。本文探究模型选择如何影响训练数据集中受试者性别与种族的不平衡对基于AI的电影磁共振心脏图像分割的作用。我们评估了三种基于卷积神经网络的模型和一种视觉Transformer模型。研究发现，四种模型中有三种存在显著的性别偏差，而所有模型均存在种族偏差。然而，不同模型间偏差的严重程度和性质有所不同，这凸显了在医学影像任务中训练公平的AI分割模型时模型选择的重要性。