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Introduction and accuracy assessment of Nicolab's StrokeViewer in a developing stroke thrombectomy UK service. a
A Karamitros1, L M C Flynn2, A Cox2
1Department of Clinical Neurosciences, 50 Little France Crescent, EH16 4TJ, UK; NHS Lothian, UK.
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.
