Abstract WP262: Impact of an AI-Based Vessel Occlusion Detection Algorithm on Occlusion Identification in a Real-World Stroke Workflow

Purpose: The purpose of this study was to evaluate the effectiveness of implementation of an FDA-cleared artificial intelligence (AI) solution (Aidoc, Tel Aviv, Israel) for detecting intracranial vessel occlusions (VO) in a real-world stroke center. Materials and Methods: This study retrospectively analyzed all stroke cases performed at a single site academic medical center between December 2020 and April 2022 where a CT angiogram of the head and neck was part of the initial workup. An AI algorithm was used to analyze these head CT angiograms, and a natural language processing (NLP) tool was employed to classify the resulting radiology reports. Cases flagged as positive or discordant by the NLP tool were then reviewed by a radiologist to establish the true diagnosis (ground truth). Finally, diagnostic performance metrics were calculated. Results: A total of 1,000 CTA exams were analyzed, of which 33 were excluded due to findings of neck vessel occlusions. The AI algorithm demonstrated an overall sensitivity of 87.2% and specificity of 97.1%. The positive predictive value was 87.6%, and the negative predictive value was 96.9% as shown in Table 1. From a sub analysis of 179 patient cases with at least one occlusion present, 233 total vessel occlusions (VO) were individually identified and reviewed: 185 (79%) were anterior circulation occlusions (ICA, MCA, ACA segments) with an AI sensitivity of 87.6% for correctly detected anterior circulation vesel occlusions; and 48 (21%) were posterior circulation vessel occlusions (basilar, vertebral, PCA segments) with an AI sensitivity of 79.2% for correctly detected posterior vessel occlusions as shown in Table 2. Conclusion: The algorithm demonstrated strong diagnostic performance, supporting the potential value of AI integration into radiology workflows for evaluating both anterior and posterior circulation stroke occlusions. Clinical Relevance Statement: Artificial intelligence solutions that provide triage and alerts for suspected vessel occlusions can meaningfully augment stroke care workflows. This study reinforces the role of AI in improving VO detection.