Abstract
Timely identification of large-vessel occlusion (LVO) in ischemic stroke is essential for optimizing prehospital triage and enabling rapid mobilization of thrombectomy-capable teams. Traditional LVO screening tools are often lengthy and reliant on neurological examination skills that may be inaccessible to nonspecialists.To assess the ability of large language models (LLMs) to detect LVO using only free-text summaries, with or without National Institutes of Health Stroke Scale (NIHSS) data, in a national teleneurology service.We conducted a retrospective analysis of 2,887 suspected stroke cases across 21 spoke hospitals within a national TeleStroke network. Neurologist-authored case summaries were processed using natural language processing techniques, including text embedding and supervised machine learning classification. Contextual LLMs (BERTimbau, BioBERTpt, GPorTuguese-2) were evaluated with five algorithms. The Bootstrap method was employed to mitigate class imbalance, with performance averaging over 100 iterations.Of 1,060 cases included in the final dataset, 143 had confirmed proximal occlusions. Median Alberta Stroke Program Early CT Score (ASPECTS) was 9 and mean National Institutes of Health Stroke Scale (NIHSS) was 5.4 ± 2. AdaBoost paired with BioBERT yielded the highest accuracy (89.82%), precision (98.37%), and AUC (89.86%). Incorporating NIHSS as a numerical feature improved recall (87.60% with multilayer perceptron) and F1-score (89.05% with Dense Neural Network). BioBERT consistently outperformed other models, regardless of NIHSS inclusion.The LLM-based models demonstrated strong performance in identifying LVO using routine clinical narratives. These findings support the integration of NLP and ML in TeleStroke systems and underscore the need for further validation across larger, multilingual datasets to ensure generalizability and clinical applicability.