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- Title
Mesterséges intelligencia alapú országos döntéstámogató rendszer bevezetése a hazai stroke-ellátás javítására.
- Authors
ISTVÁN, SZIKORA; BENCE, MAGYAR; SÁNDOR, TÉGLÁS; GÁBOR, SZUDI; ORSOLYA, SZALMÁS; MÁTÉ, CZENCZ; MÁTÉ, KONDOR; KINGA, POZSÁR; SÁNDOR, NARDAI; LÓRÁND, ERÔSS; CSABA, ÓVÁRY; KRISZTINA, HORVÁTH; JÓZSEF, MOLNÁR FERENC; GYÖRGY, PÁPAI; ÁDÁM, JANCSÓ; ZOLTÁN, SZABÓ; EDVÁRD, BENES; ZOLTÁN, CHADAIDE
- Abstract
INTRODUCTION – Indication for recanalization therapy of acute ischemic stroke is based on imaging procedures. In order to minimize the time loss passing by recognizing the condition and the transfer of images to other facility, we established a stroke imaging network (eStroke network) supported by Artificial Intelligence (AI) in Hungary. Our study aims to present this system. METHODS AND MATERIAL – Organized by the National Institute of Mental Health, Neurology, and Neurosurgery (NIMNN), we included a total of 28 stroke centers, among them 4 thrombectomy centers. An earlier network of the University of Pécs and the widened network parallel with the NIMNN project cover 10 centers thus the service is now available in 38 stroke centers of this country. Stroke CT scans are automatically transmitted via the central teleradiology server to a central image processing server which analyzes the size of the ischemic area (ASPECT score), detects large vessel occlusion and it’s localization, analyzes the quality of collateral circulation and standard CT perfusion parameters using an AI based software (eStroke, Brainomix Ltd.). Results and processed images are sent automatically back to the PACS system of the sending institution and that of the concerning thrombectomy center and become available in anonymous form via cloud by desktop computers or mobile application RESULTS – During the first year of operation, the system has processed 38,060 scans of 16,276 patients. In NIMNN experience by samples of 65 and 152 cases, for drip and ship patients the time from the first alerting of the ambulance service, until arrival at the thrombectomy center was reduced by 38 minutes from 4:18 to 3:40 minutes. CONCLUSION – Building an AI based central stroke imaging network for improving of stroke care’s results is technically feasible. Operation of the eStroke system is capable of reducing patient transportation times, however, further optimization is needed.
- Subjects
HUNGARY; STROKE units; MOBILE apps; WIDE area networks; NATIONAL Institute of Mental Health (U.S.); NEUROSURGERY; ARTIFICIAL intelligence; DIGITAL diagnostic imaging; CLINICAL decision support systems; COMPUTED tomography; DESCRIPTIVE statistics; EMERGENCY medical services; PERFUSION imaging; NEUROLOGY; AMBULANCES; STROKE; QUALITY assurance; THROMBECTOMY; PERFUSION; TRANSPORTATION of patients; TIME; TELERADIOLOGY; PICTURE archiving &; communication systems; EVALUATION
- Publication
Lege Artis Medicine (LAM), 2024, Vol 34, Issue 3, p145
- ISSN
0866-4811
- Publication type
Article
- DOI
10.33616/lam.34.0145