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

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 patients undergoing EVAR (January 2017–December 2021). An expert panel assessed each sequence for endoleaks. Each sequence was processed into three input channels: peak density, time to peak, and area under the time-density curve, generating three two-dimensional perfusion maps per patient. These maps served as input into a convolutional neural network for binary detection (classification) and localization (regression) of endoleaks through multitask learning. Fivefold cross-validation was performed, with patients split 80:20 into training and testing datasets for each fold. Performance metrics included area under the receiver operating characteristic curve, F1 score, precision, and recall and were compared with human experts. Results: The study included 220 patients (median age, 74 years [IQR, 68–79]; 181 male). Endoleaks were visible in 111 of 220 (50.5%) patients. The model identified and localized endoleaks with an area under the receiver operating characteristic curve of 0.85 ± 0.0031 (SD), F1 score of 0.78 ± 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 κ = 0.404). Conclusion: A novel, fully automated deep learning method accurately detected and localized endoleaks at DSA imaging from EVAR procedures.