AI is increasingly used to automatically outline the structures of the heart on cardiac MRI scans, a step that produces measurements used to guide patient care. This study examined whether one of these AI tools performs equally well across racial and gender groups, using cardiac MRI scans from 5,903 participants in the UK Biobank.
The researchers found no difference in performance between men and women, in a dataset that was balanced by gender. However, they found significant racial bias: the tool was most accurate for White participants and less accurate for all other groups, including Black, Asian, Chinese and mixed-race participants. The authors attribute this to the racial imbalance in the training data, which was mostly made up of White participants.
The researchers then tested three strategies to reduce this bias, including making sure each racial group was equally represented in every batch of training data and training separate models for each group. All three improved fairness compared with the original model, with separate models for each group giving the best results. The authors conclude that simply leaving race out of the model is not enough to ensure fairness, and that this becomes even more important as AI tools are used with less direct oversight from clinicians.
Learn more about this study here: https://doi.org/10.1007/978-3-030-87199-4_39
Reference:
Puyol-Antón, E., Ruijsink, B., Piechnik, S.K., Neubauer, S., Petersen, S.E., Razavi, R., & King, A.P. (2021). Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (Lecture Notes in Computer Science, Vol. 12903, pp. 413–423). Springer.