The Woven EndoBridge (WEB) device is widely used for intracranial bifurcation aneurysms, highlighting the need for real-world safety monitoring. The FDA’s MAUDE database provides valuable postmarket data, and AI-driven natural language processing (NLP) enables deeper analysis of its unstructured reports to uncover hidden safety patterns.

474 reports met inclusion criteria. Median patient age was 62 years. Most reports (99.6%) originated from the manufacturer, with 50.6% classified as injury, 43.9% as malfunction, and 5.5% as death. Common aneurysm sites were anterior communicating (38%), middle cerebral (26.7%), and basilar arteries (12.9%). The WEB SL 17 model accounted for 54.7% of cases and most device-related issues, including difficult or delayed separation (34.6%) and separation failure (20.1%). Among 342 patient problems, thrombosis (15%), aneurysm remnant (12.6%), and hemorrhage (9.4%) predominated. NLP identified three themes: device deployment and detachment mechanics, clinical outcomes, and manufacturer investigations, linking mechanical failures with potential manufacturing factors.

AI-powered NLP enhances postmarket surveillance by enabling scalable, nuanced safety signal detection, supporting regulatory monitoring and device improvement. Real-world WEB device adverse events are more diverse than those in trials, underscoring the need for improved imaging, operator training, and integration of real-world data to optimize safety.