Presentation
Artificial Intelligence in Predictive Modeling of Subarachnoid Hemorrhage
DescriptionIn the last decade, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools capable of augmenting our understanding of acute and devastating neurological disorders. For example, in the management of stroke care, penumbra mapping using advanced AI based software has extended the window of intervention 6 hours to beyond 24 hours. This has shown drastic improvements in the outcomes of this once devastating neurological injury.
However, the promise of AI is not limited to stroke care only. Machine learning algorithms, trained on vast datasets, excel at detecting anomalies in neuroimaging and neurophysiologic studies, such as EEG, relative to the traditional approach of observing with the naked eye and can correlate this information with or simply analyze clinical data with very high fidelity. This essentially allows researchers and clinicians to visualize and interpret patient data in an unparalleled manner and has the potential to yield valuable information to assist in managing some of the deadliest and most disabling conditions where treatment decisions are often under time pressure, hopefully leading to earlier detection of diseases, more timely interventions, improved patient outcomes, and better understanding of patient prognosis.
In this session, we will discuss the role of subarachnoid volumetrics in predictive modeling of Subarachnoid hemorrhage in terms of outcome, mortality and complications including delayed cerebral ischemia, and development of an automated model harnessing artificial intelligence for rapid detection, triage and predictive modeling for these patients.
However, the promise of AI is not limited to stroke care only. Machine learning algorithms, trained on vast datasets, excel at detecting anomalies in neuroimaging and neurophysiologic studies, such as EEG, relative to the traditional approach of observing with the naked eye and can correlate this information with or simply analyze clinical data with very high fidelity. This essentially allows researchers and clinicians to visualize and interpret patient data in an unparalleled manner and has the potential to yield valuable information to assist in managing some of the deadliest and most disabling conditions where treatment decisions are often under time pressure, hopefully leading to earlier detection of diseases, more timely interventions, improved patient outcomes, and better understanding of patient prognosis.
In this session, we will discuss the role of subarachnoid volumetrics in predictive modeling of Subarachnoid hemorrhage in terms of outcome, mortality and complications including delayed cerebral ischemia, and development of an automated model harnessing artificial intelligence for rapid detection, triage and predictive modeling for these patients.
Speaker
Event Type
Breakout Session
TimeTuesday, October 15th2:10pm - 2:30pm PDT
LocationHarbor Ballrooms D-I
Science of Neurocritical Care
General Critical Care
Global Neurocritical Care
Informatics
Multimodal Neuromonitoring (invasive/non-invasive)
Subarachnoid Hemorrhage
Traumatic Brain Injury
Intermediate