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Clinical stratification of rupture risk is limited to criteria based on geometry (diameter) which is not always accurate. We propose an image transformer approach applying neural networks for focused attention on abdominal aortic aneurysms (AAAs), which doesn’t require explicit segmentation, for predicting rupture risk, starting with CT angiography images. Our image dataset consisted of 16 cases with high rupture risk and 14 cases with low rupture risk. Our study reveals that 3D ResNet classifiers trained with neural embeddings from a 3D U-Net trained on images of any one rupture risk class produced an accuracy of 90% (83% sensitivity, 100% specificity). Our representation learning pipeline, AAA-Net, could be adapted to reduce the amount of time and clinical expertise required to identify AAA rupture risk, enabling efficient and automated aneurysm monitoring.
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