Machine learning for endoleak detection after endovascular aortic repair

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.

Figure 1

(Top) Overview of the custom data augmentor for removing an endoleak. The CTA slice with a corresponding mask of the endoleak is input into the data augmentor. The data augmentor uses the endoleak mask to remove the endoleak from the CTA slice. (Bottom) Overview of the custom data augmentor for adding an endoleak into an aneurysm sac. The CTA slice with a corresponding mask of the aneurysm sac is input into the data augmentor. The data augmentor uses the aneurysm mask to artificially create a unique and randomly shaped endoleak into the CTA slice.

Figure 2

Overview of the pipeline illustrating use of the custom data augmentor and U-net convolutional neural network (CNN).

Figure 3

Model evaluation.

Figure 4

Receiver operating characteristic curve for CT Slice endoleak detection by the machine learning model.

Figure 5

Raw CT slice (left) followed by human annotated segmentation map (middle) and model predicted endoleak map (right).