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Cerebral ischemia detection using artificial intelligence (CIDAI)-A study protocol.

Linda Block1,2, Ali El-Merhi1,2, Jaquette Liljencrantz1,2

  • 1Department of Anaesthesiology and Intensive Care, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

Acta Anaesthesiologica Scandinavica
|June 14, 2020
PubMed
Summary

This study proposes using artificial intelligence (AI) to analyze Heart Rate Variability (HRV), Near-Infrared Spectroscopy (NIRS), and Electroencephalography (EEG) data. This approach aims to noninvasively predict imminent cerebral ischemia in unconscious patients.

Keywords:
artificial intelligencecerebral ischemiacerebral reperfusionmachine learningmonitoring

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Area of Science:

  • Neuroscience
  • Medical Technology
  • Artificial Intelligence

Background:

  • Predicting cerebral ischemia in patients with altered consciousness is challenging with current methods.
  • Cerebral ischemia detection requires noninvasive, AI-supported techniques for improved patient outcomes.

Purpose of the Study:

  • To develop an AI-driven method for noninvasive detection of imminent cerebral ischemia in unconscious patients.
  • To utilize integrated physiological data for early identification of cerebral ischemia and reperfusion.

Main Methods:

  • Prospective observational study involving carotid endarterectomy and acute endovascular embolectomy patients.
  • Collection and machine learning analysis of Heart Rate Variability (HRV), Near-Infrared Spectroscopy (NIRS), and Electroencephalography (EEG) data.
  • Training artificial neural networks (long short-term memory with convolutional layers) to detect patterns of cerebral ischemia and reperfusion.

Main Results:

  • The study aims to establish a robust AI model by analyzing integrated physiological and clinical data.
  • Patterns indicative of impending cerebral ischemia and reperfusion will be identified through machine learning.

Conclusions:

  • AI analysis of integrated physiological data (HRV, NIRS, EEG) can enable rapid detection of early cerebral ischemia signs.
  • This approach facilitates timely clinical alerts and interventions, potentially improving patient outcomes.