A breakthrough in technology will mean hypoxic-ischemic brain injury will be better assessed in survivors of cardiac arrest.
University of Chicago researchers have developed a technique using deep learning, a form of artificial intelligence (AI), which can support better insight.
The goal of the multi-department research was to see if machine-learning could help clinicians at the hospital better assess hypoxic-ischemic brain injury (HIBI), which can occur when the brain does not receive enough oxygen during cardiac arrest.
The extent of this damage depends on several variables, including the baseline characteristics of the brain and its vascular supply, duration of oxygen deprivation, and cessation of blood flow.
“While the neurological injury that follows cardiac arrest is largely a function of HIBI, the process of determining a patient’s projected long-term neurological function is a multifaceted endeavour that involves multiple clinical and diagnostic tools,” explains Professor Fernando Goldenberg, a professor of neurology and neurosurgery, as well as the co-director of the comprehensive stroke center and director of neuroscience critical care at UChicago Medicine.
“In addition to bedside clinical exam, head CT (HCT) is often the earliest and most readily available imaging tool.”
In their work, the researchers hypothesised that the progression of HIBI could be identified in scans completed on average within the first three hours after the heart resumes normal activity.
To test this, the team used machine learning, specifically, a deep transfer learning approach to predict from the first normal-appearing HCT scan whether or not HIBI would progress.
The deep learning technique, for which there is a patent-pending, automatically assessed the first HCT scan to identify the progression of HIBI.
This is important as currently there is no imaging-based method/analyses to identify early on whether or not a patient will exhibit HIBI, and while more data is needed to further confirm the efficacy of the AI-based method, the results to date are “very promising.”
“The findings in patients’ first HCT may be too subtle to be picked up by the human eye,” said Maryellen Giger, who has been conducting research on computer-aided decades for three decades.
“However, a computer looking at the complete image may be able to determine between those patients who will progress and eventually show evidence of HIBI and those who will not.”
According to the researchers, the AI system can help in the process of prognostication in survivors of cardiac arrest “by identifying patients who may differentially benefit from early interventions – a step along precision medicine in this patient population. If prospectively validated, it could also allow for the neuroprognostic process to start sooner than the current standard timeline,” said Ali Mansour, an assistant professor of neurology and neurosurgery
Additionally, the AI algorithm is expected to be easily integrated into various commercially available image analysis software packages that are already deployed in clinical settings.
