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.