Cerebral ischemia detection using artificial intelligence (CIDAI)—A study protocol. Issue 9 (2nd July 2020) - Block, Linda; El‐Merhi, Ali; Liljencrantz, Jaquette; Naredi, Silvana; Staron, Miroslaw; Odenstedt Hergès, Helena
Abstract : 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.