ArticleNPJ digital medicine2023
A retrospective study on machine learning-assisted stroke recognition for medical helpline calls.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Stroke recognition and early care pathways following telephone triage in out-of-hours primary care: a register-based study from the Central Denmark Region.Scandinavian journal of primary health care · 2026Article
- Stroke detection in medical emergency calls: a retrospective analysis and exploratory evaluation of an AI decision support model.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Article
- Machine learning models powered by emergency medical services data enhance stroke triage in prehospital settings.Scientific reports · 2026Article
- Current State of the Clinical Applications of Artificial Intelligence in Stroke: A Literature Review.Brain sciences · 2026Review
- Artificial Intelligence-Based Detection of Central Retinal Artery Occlusion Within 4.5 Hours on Standard Fundus Photographs.Journal of the American Heart Association · 2025Observational
- Words to live by: Using medic impressions to identify the need for prehospital lifesaving interventions.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2025Article
- Methods for improving the identification of acute stroke during ambulance calls: A scoping review.PloS one · 2025Article
- Transformative potential of GPT-4o in clinical immunology and allergy: Opportunities and challenges of real-time voice interaction.Asia Pacific allergy · 2024Article
- Article
- Application of Artificial Intelligence in Acute Ischemic Stroke: A Scoping Review.Neurointervention · 2024Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Advanced stroke treatment is time-dependent and, therefore, relies on recognition by call-takers at prehospital telehealth services to ensure fast hospitalisation. This study aims to develop and assess the potential of machine learning in improving prehospital stroke recognition during medical helpline calls. We used calls from 1 January 2015 to 31 December 2020 in Copenhagen to develop a machine learning-based classification pipeline. Calls from 2021 are used for testing. Calls are first transcribed using an automatic speech recognition model and then categorised as stroke or non-stroke using a text classification model. Call-takers achieve a sensitivity of 52.7% (95% confidence interval 49.2-56.4%) with a positive predictive value (PPV) of 17.1% (15.5-18.6%). The machine learning framework performs significantly better (p < 0.0001) with a sensitivity of 63.0% (62.0-64.1%) and a PPV of 24.9% (24.3-25.5%). Thus, a machine learning framework for recognising stroke in prehospital medical helpline calls may become a supportive tool for call-takers, aiding in early and accurate stroke recognition.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.