Artificial intelligence and machine learning for hemorrhagic trauma care

Artificial intelligence (AI), a branch of machine learning (ML) has been increasingly employed in the research of trauma in various aspects. Hemorrhage is the most common cause of trauma-related death. To better elucidate the current role of AI and contribute to future development of ML in trauma ca...

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Bibliographic Details
Published in:Military medical research 2023-02, Vol.10 (1), p.6-6, Article 6
Main Authors: Peng, Henry T, Siddiqui, M Musaab, Rhind, Shawn G, Zhang, Jing, da Luz, Luis Teodoro, Beckett, Andrew
Format: Article
Language:English
Subjects:
Artificial Intelligence
Emergency Medical Services
Hemorrhage
Hemorrhage - diagnosis
Hemorrhage - etiology
Hemorrhage - therapy
Humans
Injury
Machine Learning
Prospective Studies
Retrospective Studies
Review
Trauma
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Summary:Artificial intelligence (AI), a branch of machine learning (ML) has been increasingly employed in the research of trauma in various aspects. Hemorrhage is the most common cause of trauma-related death. To better elucidate the current role of AI and contribute to future development of ML in trauma care, we conducted a review focused on the use of ML in the diagnosis or treatment strategy of traumatic hemorrhage. A literature search was carried out on PubMed and Google scholar. Titles and abstracts were screened and, if deemed appropriate, the full articles were reviewed. We included 89 studies in the review. These studies could be grouped into five areas: (1) prediction of outcomes; (2) risk assessment and injury severity for triage; (3) prediction of transfusions; (4) detection of hemorrhage; and (5) prediction of coagulopathy. Performance analysis of ML in comparison with current standards for trauma care showed that most studies demonstrated the benefits of ML models. However, most studies were retrospective, focused on prediction of mortality, and development of patient outcome scoring systems. Few studies performed model assessment via test datasets obtained from different sources. Prediction models for transfusions and coagulopathy have been developed, but none is in widespread use. AI-enabled ML-driven technology is becoming integral part of the whole course of trauma care. Comparison and application of ML algorithms using different datasets from initial training, testing and validation in prospective and randomized controlled trials are warranted for provision of decision support for individualized patient care as far forward as possible.
ISSN:2054-9369
2095-7467
2054-9369
DOI:10.1186/s40779-023-00444-0