Background and PurposeIn stroke, timely treatment is vital for preserving neurologic function. However, decision-making in neurocritical care is hindered by limited accessibility of neuroimaging and radiological interpretation. We evaluated an artificial intelligence (AI) system for use in conjunction with bedside portable point-of-care (POC)-MRI to automatically measure midline shift (MLS), a quantitative biomarker of stroke severity.Materials and MethodsPOC-MRI (0.064 T) was acquired in a patient cohort (n=94) in the Neurosciences Intensive Care Unit (NICU) of an academic medical center in the follow-up window during treatment for ischemic stroke (IS) and hemorrhagic stroke (HS). A deep-learning architecture was applied to produce AI estimates of midline shift (MLS-AI). Neuroradiologist annotations for MLS were compared to MLS-AI using non-inferiority testing. Regression analysis was used to evaluate associations between MLS-AI and stroke severity (NIHSS) and functional disability (mRS) at imaging time and discharge, and the predictive value of MLS-AI versus clinical outcome was evaluated.ResultsMLS-AI was non-inferior to neuroradiologist estimates of MLS (p1.5 mm was positively predictive of poor discharge outcome in all patients (PPV=70%) and specifically in patients with IS (PPV=77%).ConclusionThe integration of portable POC-MRI and AI provides automatic MLS measurements that were not inferior to time-consuming, manual measurements from expert neuroradiologists, potentially reducing neuroradiological burden for follow-up imaging in acute stroke.
