2025 Poster Presentations
P340: USING AUTOMATED IMAGE PROCESSING FOR CLOT DETECTION DURING CEREBRAL ANGIOGRAPHY
Heather C Grimaudo, MD1; Ayberk Acar, BS2; Dewei Hu, MS2; Rohan Chitale, MD3; Ipek Oguz, BS, MS, PhD4; 1University of South Florida; 2Vanderbilt Institue for Surgery and Engineering; 3Vanderbilt University Medical Center; 4Vanderbilt University School of Engineering
Background: Thromboembolic events are a known risk of neuroangiographic procedures, and prompt detection depends on the proceduralist’s advanced knowledge of cerebral vascular anatomy and the dynamic timing of contrast transit. With ongoing advances in surgical adjuncts and software, subtask automation with artificial intelligence (AI) models helps to reduce technical errors and make neurosurgical procedures safer. This study serves as proof-of-concept for a preliminary AI model for automatic thrombus recognition during an angiographic procedure.
Methods: An automated image processing and visualization pipeline was developed for angiographic image analysis using the DICOM files. First, faulty angiographic images were eliminated using a deep learning classifier, and then thresholding and stacking vessel maps were created. At the final stage of imaging processing, pre- and post-thrombectomy vessel maps were registered for visualization and clot detection.
Results: Eight vessel maps were created using the above-stated imaging processing techniques to identify a vessel occlusion on angiographic imaging.
Conclusions: AI-based software algorithms present an enormous underdeveloped opportunity to improve prompt identification of thromboembolic events during neuroangiographic procedures, which offers a greater safety benefit to the patient. This study presents a preliminary AI model for clot detection, which required significant manual imaging processing – a model to build on for more robust AI-based image processing and clot detection in the future. Ultimately, this software is intended to work in synergy with the neurointerventionalist and current intraoperative monitoring practices to enhance simple and complex neuroangiographic procedural outcomes.
