Assessment of patients suspected to have an acute stroke at the prehospital level or non-stroke centers may be challenging. We aimed to validate a novel Artificial Intelligence (AI) based application (CVAid) for smartphones able to score neurological deficit in acute stroke patients. Methods: Acute stroke patients, admitted in two different stroke units, and healthy volunteers were studied. A certified stroke neurologist determined NIHSS at bedside. A different user recorded a short neurological exam, using CVAid application. The software processed the video file presenting comprehensive neurological exam per NIHSS test to a remote neurologist on a dedicated tablet. Two different remote neurologists (remote1 and remote2) determined NIHSS reviewing neurological exam in the tablet. CVAid performed also a completely automated AI analysis of facial features, to detect stroke symptoms. Results: A total of 75 patients were included in the study, 64 (85.3%) stroke patients and 11 (14.7%) healthy volunteers. Correlation between bedside and remote1 NIHSS was r=0. 861 (p<0.001). Correlation between remote1 and remote2 NIHSS was r=0.865 (p=0.001). The CVAId facial recognition system showed an accuracy of 87% in stroke symptom detection, sensitivity 95%, Specificity 80%, false negative 5%, false positive 20%. Conclusions: An AI based application efficiently allowed remote neurological evaluation of stroke patients. The automated algorithm was able to accurately triage healthy volunteers from patients suffering a stroke. Feeding the machine learning system with additional patients and development of additional automated analysis beyond facial recognition will increase the system accuracy.
Background and purposeThe National Institutes of Health Stroke Scale (NIHSS) is the most recommended tool for objectively quantifying the impairment caused by a suspected stroke. Nevertheless, it is mainly used by trained neurologists in the emergency department (ED). To bring forward the NIHSS to the pre-hospital setting, a smartphone-based Telestroke system was developed. It captures the full NIHSS by video, transmits it off-line, and enables assessment by a distant stroke physician. We aimed to compare the reliability of an NIHSS score determined by a neurologist from afar, using the platform with a standard NIHSS assessment performed in the emergency departments.MethodsA multi-center prospective study was conducted in two centers (Vall d'Hebron, Barcelona, and Rambam, Israel). Patients admitted to the ED with suspected stroke had a neurological exam based on the NIHSS, while being recorded by the system. A skilled neurologist rated the NIHSS according to the videos offline. The results were compared with the NIHSS score given by a neurologist at the bedside.ResultsA total of 95 patients with suspected stroke were included. The overall intraclass correlation coefficient was 0.936 (0.99 in VdH and 0.84 in Rambam), indicating excellent and good reliability, respectively.ConclusionRemote stroke assessment based on the NIHSS, using videos segments collected by a dedicated platform, installed on a standard smartphone, is a reliable measurement as compared with the bedside evaluation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.