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Acute aortic syndrome (AAS) is a group of life threatening conditions of theaorta. We have developed an end-to-end automatic approach to detect AAS incomputed tomography (CT) images. Our approach consists of two steps. At first,we extract N cross sections along the segmented aorta centerline for each CTscan. These cross sections are stacked together to form a new volume which isthen classified using two different classifiers, a 3D convolutional neuralnetwork (3D CNN) and a multiple instance learning (MIL). We trained, validated,and compared two models on 2291 contrast CT volumes. We tested on a set asidecohort of 230 normal and 50 positive CT volumes. Our models detected AAS withan Area under Receiver Operating Characteristic curve (AUC) of 0.965 and 0.985using 3DCNN and MIL, respectively.

Deep Learning based detection of Acute Aortic Syndrome in contrast CT images