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Artificial intelligence and machine learning for hemorrhagic trauma care.

Henry T Peng1, M Musaab Siddiqui2, Shawn G Rhind2

  • 1Defence Research and Development Canada, Toronto Research Centre, Toronto, ON, M3K 2C9, Canada. henry.peng@drdc-rddc.gc.ca.

Military Medical Research
|February 16, 2023
PubMed
Summary

Artificial intelligence (AI) and machine learning (ML) show promise in diagnosing and treating traumatic hemorrhage. Further research with diverse datasets and prospective trials is needed to integrate these AI tools into clinical practice.

Keywords:
Artificial intelligenceHemorrhageInjuryMachine learningTrauma

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Area of Science:

  • Medical Informatics
  • Trauma Surgery
  • Machine Learning

Background:

  • Hemorrhage is a leading cause of trauma mortality.
  • Artificial intelligence (AI) and machine learning (ML) are increasingly used in trauma research.
  • Understanding the current role of ML in traumatic hemorrhage care is crucial for future development.

Purpose of the Study:

  • To review the application of ML in the diagnosis and treatment strategies for traumatic hemorrhage.
  • To identify key areas where ML is being utilized in trauma care.
  • To assess the benefits and limitations of current ML models in managing traumatic hemorrhage.

Main Methods:

  • A comprehensive literature search was conducted on PubMed and Google Scholar.
  • Studies were screened based on titles and abstracts, with full articles reviewed for relevance.
  • Eighty-nine studies were included and categorized into five main areas of application.

Main Results:

  • ML models demonstrated benefits in predicting outcomes, risk assessment, triage, transfusion needs, hemorrhage detection, and coagulopathy.
  • Most studies were retrospective and focused on mortality prediction and outcome scoring.
  • Few studies validated models on external datasets, and current ML tools are not widely used in practice.

Conclusions:

  • AI-driven ML technology is becoming integral to trauma care.
  • Prospective, randomized controlled trials with diverse datasets are needed for robust validation.
  • Decision support for individualized patient care through ML requires further development and implementation.