Introduction and accuracy assessment of Nicolab's StrokeViewer in a developing stroke thrombectomy UK service. a service development/improvement project.

Aim: The aim of this study was to evaluate the implementation of artificial intelligence (AI) software in a quaternary stroke centre as well as assess the accuracy and efficacy of StrokeViewer software in large vessel occlusion detection and its potential impact on radiological workflow.

Materials And Methods: Data were collected during two separate three-month periods comparing the accuracy rate of StrokeViewer in detection of large vessel occlusion to that of a junior registrar. During the first three months, 37 cases were identified and during the second leg, 47. The second leg of the study was performed due to a high number of technical failures during the first one and in an attempt to improve those via communication with the manufacturer and co-operation between allied healthcare professionals. Statistical analysis was performed using SPSS software.

Results: Technical failure rate was 25% in the first leg and reduced to 17% in the second leg, showing a trend to statistical significance. Specificity and sensitivity of StrokeViewer were similar in the two legs of the study, measuring 91% and 93% initially and 94% and 93% finally, respectively. Efficacy was comparable to that of the junior registrar with StrokeViewer, demonstrating 92% accuracy during the first leg vs 95% by the junior registrar and 93% in the second leg vs 98% by the junior registrar. These did not show statistical significance.

Conclusion: This is a real-life analysis of StrokeViewer efficacy and its potential failures, showing a reduction in failure rate, accuracy rate of a junior registrar, and sensitivity and specificity values close to the advertised ones.