Japanese researchers reported that variational autoencoder models can detect coronary artery disease in myocardial perfusion SPECT images more accurately than standard quantitative methods. The approach aims to reduce reliance on labeled normal data by learning features in a way that supports CAD classification within nuclear cardiology workflows. The work was described in a recent publication in the imaging literature.