Early identification and characterisation of stroke to support prehospital decision-making using artificial intelligence: a scoping review protocol

Hoor Jalo; Mattias Seth; Minna Pikkarainen; Ida Häggström; Katarina Jood; Anna Bakidou; Bengt Arne Sjöqvist; Stefan Candefjord

doi:10.1136/bmjopen-2022-069660

Early identification and characterisation of stroke to support prehospital decision-making using artificial intelligence: a scoping review protocol

BMJ Open. 2023 May 22;13(5):e069660. doi: 10.1136/bmjopen-2022-069660.

Authors

Hoor Jalo¹, Mattias Seth², Minna Pikkarainen³, Ida Häggström², Katarina Jood^{4

5}, Anna Bakidou^{2

6}, Bengt Arne Sjöqvist², Stefan Candefjord²

Affiliations

¹ Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden hoor@chalmers.se.
² Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
³ Department of Occupational Therapy, Prosthetics and Orthotics, Oslo Metropolitan University, Oslo, Norway.
⁴ Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
⁵ Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden.
⁶ PreHospen - Centre for Prehospital Research, University of Borås, Borås, Sweden.

Abstract

Introduction: Stroke is a time-critical condition and one of the leading causes of mortality and disability worldwide. To decrease mortality and improve patient outcome by improving access to optimal treatment, there is an emerging need to improve the accuracy of the methods used to identify and characterise stroke in prehospital settings and emergency departments (EDs). This might be accomplished by developing computerised decision support systems (CDSSs) that are based on artificial intelligence (AI) and potential new data sources such as vital signs, biomarkers and image and video analysis. This scoping review aims to summarise literature on existing methods for early characterisation of stroke by using AI.

Methods and analysis: The review will be performed with respect to the Arksey and O'Malley's model. Peer-reviewed articles about AI-based CDSSs for the characterisation of stroke or new potential data sources for stroke CDSSs, published between January 1995 and April 2023 and written in English, will be included. Studies reporting methods that depend on mobile CT scanning or with no focus on prehospital or ED care will be excluded. Screening will be done in two steps: title and abstract screening followed by full-text screening. Two reviewers will perform the screening process independently, and a third reviewer will be involved in case of disagreement. Final decision will be made based on majority vote. Results will be reported using a descriptive summary and thematic analysis.

Ethics and dissemination: The methodology used in the protocol is based on information publicly available and does not need ethical approval. The results from the review will be submitted for publication in a peer-reviewed journal. The findings will be shared at relevant national and international conferences and meetings in the field of digital health and neurology.

Keywords: Health informatics; Stroke; Telemedicine.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence
Emergency Medical Services*
Humans
Research Design
Review Literature as Topic
Stroke* / diagnosis
Stroke* / therapy