부산대학교 도서관

Background: Patient selection for reperfusion therapies requires significant expertise in neuroimaging. Increasingly, machine learning-based analysis is used for faster and standardized patient selection. However, there is little information on how such software influences real-world patient management. Aims: We evaluated changes in thrombolysis and thrombectomy delivery following implementation of automated analysis at a high volume primary stroke centre. Methods: We retrospectively collected data on consecutive stroke patients admitted to a large university stroke centre from two identical 7-month periods in 2017 and 2018 between which the e-Stroke Suite (Brainomix, Oxford, UK) was implemented to analyse non-contrast CT and CT angiography results. Delivery of stroke care was otherwise unchanged. Patients were transferred to a hub for thrombectomy. We collected the number of patients receiving intravenous thrombolysis and/or thrombectomy, the time to treatment; and outcome at 90 days for thrombectomy. Results: 399 patients from 2017 and 398 from 2018 were included in the study. From 2017 to 2018, thrombolysis rates increased from 11.5% to 18.1% with a similar trend for thrombectomy (2.8-4.8%). There was a trend towards shorter door-to-needle times (44-42 min) and CT-to-groin puncture times (174-145 min). There was a non-significant trend towards improved outcomes with thrombectomy. Qualitatively, physician feedback suggested that e-Stroke Suite increased decision-making confidence and improved patient flow. Conclusions: Use of artificial intelligence decision support in a hyperacute stroke pathway facilitates decision-making and can improve rate and time of reperfusion therapies in a hub-and-spoke system of care. Keywords: Stroke, Thrombolysis, Thrombectomy, Machine learning, e-ASPECTS
Author(s): Bence Gunda [a]; Ain Neuhaus [b]; Ildikó Sipos [a]; Rita Stang [a]; Péter Pál Böjti [a]; Tímea Takács [a]; Dániel Bereczki [a]; Balázs Kis [c]; István Szikora [c]; George [...]