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Improved Stroke Care in a Primary Stroke Centre Using AI-Decision Support.

Bence Gunda1, Ain Neuhaus2, Ildikó Sipos1

  • 1Department of Neurology, Semmelweis University, Budapest, Hungary.

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Summary
This summary is machine-generated.

Automated artificial intelligence analysis of neuroimaging improved stroke reperfusion therapies. This AI tool enhanced decision-making, increasing thrombolysis and thrombectomy rates and reducing treatment times in a hub-and-spoke system.

Keywords:
Machine learningStrokeThrombectomyThrombolysise-ASPECTS

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Area of Science:

  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Reperfusion therapy selection in stroke patients demands neuroimaging expertise.
  • Machine learning aids in faster, standardized patient selection for stroke therapies.
  • Limited data exists on the real-world impact of AI software on stroke patient management.

Purpose of the Study:

  • To evaluate changes in thrombolysis and thrombectomy delivery after implementing automated neuroimaging analysis.
  • To assess the influence of AI-driven decision support on hyperacute stroke care pathways.

Main Methods:

  • Retrospective analysis of stroke patients from two 7-month periods (2017 and 2018) at a high-volume stroke center.
  • Implementation of e-Stroke Suite for automated analysis of CT and CT angiography results between the study periods.
  • Data collection included reperfusion therapy rates, treatment times, and 90-day outcomes.

Main Results:

  • Thrombolysis rates increased from 11.5% to 18.1%; thrombectomy rates rose from 2.8% to 4.8%.
  • Trends indicated shorter door-to-needle and CT-to-groin puncture times.
  • Physician feedback highlighted increased confidence and improved patient flow with AI support.

Conclusions:

  • AI decision support in hyperacute stroke pathways enhances clinical decision-making.
  • Implementation of AI tools can improve the rate and timeliness of reperfusion therapies.
  • AI facilitates efficient patient management within a hub-and-spoke stroke care model.