DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning
This addresses the problem of missed AAA diagnoses in clinical settings, offering a potential tool to reduce mortality, though it is incremental as it applies existing deep learning methods to a specific medical imaging task.
The researchers tackled the detection of abdominal aortic aneurysms (AAAs), which cause over 10,000 deaths annually in the U.S. and are often missed by radiologists, by developing DeepAAA, a deep learning model that achieved high sensitivity and specificity (e.g., 0.91/0.95 and 0.85/1.0) on clinical CT scans.
We propose a deep learning-based technique for detection and quantification of abdominal aortic aneurysms (AAAs). The condition, which leads to more than 10,000 deaths per year in the United States, is asymptomatic, often detected incidentally, and often missed by radiologists. Our model architecture is a modified 3D U-Net combined with ellipse fitting that performs aorta segmentation and AAA detection. The study uses 321 abdominal-pelvic CT examinations performed by Massachusetts General Hospital Department of Radiology for training and validation. The model is then further tested for generalizability on a separate set of 57 examinations with differing patient demographics and acquisition characteristics than the original dataset. DeepAAA achieves high performance on both sets of data (sensitivity/specificity 0.91/0.95 and 0.85 / 1.0 respectively), on contrast and non-contrast CT scans and works with image volumes with varying numbers of images. We find that DeepAAA exceeds literature-reported performance of radiologists on incidental AAA detection. It is expected that the model can serve as an effective background detector in routine CT examinations to prevent incidental AAAs from being missed.