AI is increasingly used to automatically outline the structures of the heart on cardiac MRI scans, a step that produces the measurements doctors use to guide patient care. One of the main causes of bias in these tools is imbalance in the data used to train them. This study is described by its authors as the first systematic investigation of how imbalance by race and sex in training data affects bias in this type of AI.
The researchers trained several AI models on cardiac MRI scans, varying the proportion of people of different races and sexes in the training data each time. They found no significant bias in the experiment on sex, but significant bias in the two experiments on race.
The authors conclude that the results highlight the need to ensure that different demographic groups are adequately represented in health datasets.
Learn more about this study here: https://doi.org/10.1007/978-3-031-23443-9_22
Reference:
Lee, T., Puyol-Antón, E., Ruijsink, B., Shi, M., & King, A.P. (2022). A Systematic Study of Race and Sex Bias in CNN-Based Cardiac MR Segmentation. In Statistical Atlases and Computational Models of the Heart (STACOM 2022) (Lecture Notes in Computer Science, Vol. 13593, pp. 233–244). Springer.