Abstract
Diagnosis of endoleak following endovascular aortic repair (EVAR) relies on manual review of multi-slice CT angiography (CTA) by physicians which is a tedious and time-consuming process that is susceptible to error. We evaluate the use of a deep neural network for the detection of endoleak on CTA for post-EVAR patients using a novel data efficient training approach. 50 CTAs and 20 CTAs with and without endoleak respectively were identified based on gold standard interpretation by a cardiovascular subspecialty radiologist. The Endoleak Augmentor, a custom designed augmentation method, provided robust training for the machine learning (ML) model. Predicted segmentation maps underwent post-processing to determine the presence of endoleak. The model was tested against 3 blinded general radiologists and 1 blinded subspecialist using a held-out subset (10 positive endoleak CTAs, 10 control CTAs). Model accuracy, precision and recall for endoleak diagnosis were 95%, 90% and 100% relative to reference subspecialist interpretation (AUC = 0.99). Accuracy, precision and recall was 70/70/70% for generalist1, 50/50/90% for generalist2, and 90/83/100% for generalist3. The blinded subspecialist had concordant interpretations for all test cases compared with the reference. In conclusion, our ML-based approach has similar performance for endoleak diagnosis relative to subspecialists and superior performance compared with generalists.
Highlights
IntroductionAdvanced data augmentation techniques have been shown to provide substantial model performances increases[26]
The presence or absence of endoleak in each patient was determined based on the corresponding clinical Computed tomography angiography (CTA) radiology report dictated by a cardiovascular imaging subspecialty trained diagnostic radiologist
A recent study by Hahn et al evaluated the use of a deep learning method for endoleak identification[24]
Summary
Advanced data augmentation techniques have been shown to provide substantial model performances increases[26]. Studies have shown the feasibility of getting state-of-the-art performance by augmenting just one image[27]. Data augmentation techniques such as random image rotations and introduction of nonlinear deformations have been shown effective in improving medical segmentation model accuracies[28]. Other augmentation methods involving adding or subtracting regions of images have not been commonly used for natural images because the outcomes appear artificial. We present a novel data augmentation method using segmentation maps to augment CT slices by adding and removing regions of the CT slice containing an endoleak
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