AI can accurately diagnose coronary artery disease from myocardial perfusion imaging, a type of scan that shows blood flow to the heart muscle. However, these models are often trained on high-risk patients who were referred for invasive tests, which can lead them to overestimate the probability of disease in other patients. This study tested ways of training AI so that the probabilities it estimates match the real probability of disease more closely.
Deep learning models were trained on 828 patients from three sites and then tested on a separate group of 511 patients, including 179 with a low likelihood of disease. The best results came from a model trained with additional examples of patients without coronary artery disease and with little or no sign of reduced blood flow on the scan. It produced more accurate probability estimates than the model trained without these additional cases, and the improvement was most pronounced in women. It was also better at identifying disease than standard measures taken from the scans.
The authors conclude that adding more low-risk patients during training can improve how accurately AI estimates the probability of disease. This matters especially for lower-risk patients and for women, for whom overestimated probabilities could affect decisions about further tests and treatment.
Learn more about this study here: https://doi.org/10.1007/s00259-022-05972-w
Reference:
Miller, R.J.H., Singh, A., Otaki, Y., Tamarappoo, B.K., Kavanagh, P., Parekh, T., Hu, L.-H., Gransar, H., Sharir, T., Einstein, A.J., Fish, M.B., Ruddy, T.D., Kaufmann, P.A., Sinusas, A.J., Miller, E.J., Bateman, T.M., Dorbala, S., Di Carli, M.F., Liang, J.X., … Slomka, P.J. (2023). Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images. European Journal of Nuclear Medicine and Molecular Imaging, 50(2), 387–397.