Automated Detection of Endoleaks in CT Angiography Using a Single Deep Learning Model

Detecting endoleaks on post-EVAR CT scans, especially in the presence of artifacts caused by embolic agents like Onyx, is a challenging but critical task. In this study, we propose a deep learning-based method for endoleak detection using a single object detection model.

A retrospective dataset comprising 249 CT scans was used, divided into training (129 scans) and test (120 scans) datasets. Ground truth annotations were obtained through 3D manual segmentation, and a preprocessing pipeline was applied. The model was trained on slices extracted from the training dataset and validated using standard metrics, including sensitivity, specificity, and AUC.

The model achieved high performance at both the per-slice and per-series levels. Per-slice detection yielded a ROC AUC of 96%, with a sensitivity of 91%, specificity of 93%, PPV of 55%, and NPV of 99%. At the per-series level, sensitivity and specificity were 93% and 95%, respectively. Notably, our method introduces a per-slice detection metric, which was not reported in previous studies, and uses a single model, making it computationally efficient and easy to train.

This study demonstrates the feasibility of using deep learning for endoleak detection, offering a robust and efficient tool for clinical practice. Future work could focus on expanding the dataset and integrating additional predictive capabilities, such as endoleak classification and localization.