Multitask Deep Learning for Automated Detection of Endoleak at Digital Subtraction Angiography during Endovascular Aneurysm Repair

“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop and evaluate a novel multitask deep learning framework for automated detection and localization of endoleaks at aortic digital subtraction angiography (DSA) performed during real-world endovascular aneurysm repair (EVAR) procedures for abdominal aortic aneurysm. Materials and Methods This retrospective study analyzed intraoperative aortic DSA images from EVAR patients (January 2017-December 2021). An expert panel assessed each sequence for endoleaks. Each sequence was processed into three input channels: peak density (PD), time to peak (TTP), and area under the time-density curve (AUC-TD), generating three 2D perfusion maps per patient. These maps served as input into a convolutional neural network (CNN) for binary detection (classification) and localization (regression) of endoleaks through multitask learning. Fivefold cross-validation was performed, with patients split 80:20 into training/testing for each fold. Performance metrics included AUC, F1 score, precision, recall and were compared with human experts. Results The study included 220 patients (181 male; median age, 74 years; IQR, 68–79 years). Endoleaks were visible in 111 out of 220 (50.5%) patients. The model identified and localized endoleaks with an AUC of 0.85 (SD 0.0031), F1 score of 0.78 (SD 0.21), 95% precision, and 73% recall. Compared with the procedural team (94% precision, 63% recall), it had higher values in both metrics, with an F1-score within the human observer range (0.75–0.85). Balancing regression and classification by multitask learning delivered optimal results. The interobserver agreement among human experts was moderate (Fleiss’ Kappa = 0.404). Conclusion A novel, fully automated deep learning method accurately detected and localized endoleaks on DSA imaging from EVAR procedures. ©RSNA, 2025