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Cerebral ischemia d...
Cerebral ischemia detection using artificial intelligence (CIDAI)-A study protocol
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- Block, Linda (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för anestesiologi och intensivvård,Institute of Clinical Sciences, Department of Anesthesiology and Intensive care
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- El-Merhi, Ali (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för anestesiologi och intensivvård,Institute of Clinical Sciences, Department of Anesthesiology and Intensive care
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- Liljencrantz, Jaquette (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för anestesiologi och intensivvård,Institute of Clinical Sciences, Department of Anesthesiology and Intensive care
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- Naredi, Silvana, 1953 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för anestesiologi och intensivvård,Institute of Clinical Sciences, Department of Anesthesiology and Intensive care
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- Staron, Miroslaw, 1977 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU)
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- Odenstedt Hergès, Helena (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för anestesiologi och intensivvård,Institute of Clinical Sciences, Department of Anesthesiology and Intensive care
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(creator_code:org_t)
- 2020-07-02
- 2020
- English.
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In: Acta Anaesthesiologica Scandinavica. - : Wiley. - 0001-5172 .- 1399-6576. ; 64:9, s. 1335-1342
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Abstract
Subject headings
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- Background The onset of cerebral ischemia is difficult to predict in patients with altered consciousness using the methods available. We hypothesize that changes in Heart Rate Variability (HRV), Near-Infrared Spectroscopy (NIRS), and Electroencephalography (EEG) correlated with clinical data and processed by artificial intelligence (AI) can indicate the development of imminent cerebral ischemia and reperfusion, respectively. This study aimed to develop a method that enables detection of imminent cerebral ischemia in unconscious patients, noninvasively and with the support of AI. Methods This prospective observational study will include patients undergoing elective surgery for carotid endarterectomy and patients undergoing acute endovascular embolectomy for cerebral arterial embolism. HRV, NIRS, and EEG measurements and clinical information on patient status will be collected and processed using machine learning. The study will take place at Sahlgrenska University Hospital, Gothenburg, Sweden. Inclusion will start in September 2020, and patients will be included until a robust model can be constructed. By analyzing changes in HRV, EEG, and NIRS measurements in conjunction with cerebral ischemia or cerebral reperfusion, it should be possible to train artificial neural networks to detect patterns of impending cerebral ischemia. The analysis will be performed using machine learning with long short-term memory artificial neural networks combined with convolutional layers to identify patterns consistent with cerebral ischemia and reperfusion. Discussion Early signs of cerebral ischemia could be detected more rapidly by identifying patterns in integrated, continuously collected physiological data processed by AI. Clinicians could then be alerted, and appropriate actions could be taken to improve patient outcomes.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Anestesi och intensivvård (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Anesthesiology and Intensive Care (hsv//eng)
Keyword
- artificial intelligence
- cerebral ischemia
- cerebral reperfusion
- machine learning
- monitoring
- near-infrared spectroscopy
- individual patient data
- transcranial
- doppler
- stroke
- eeg
- Anesthesiology
Publication and Content Type
- ref (subject category)
- art (subject category)
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