Point-of-Care MRI with Artificial Intelligence to Measure Midline Shift in Acute Stroke Follow-Up

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 (p<1e-5). MLS-AI measurements were associated with stroke severity (NIHSS) near the time of imaging in all patients (p<0.005) and within the IS subgroup (p=0.005). In multivariate analysis, larger MLS-AI at the time of imaging was associated with significantly worse outcome at the time of discharge in all patients and in the IS subgroup (p<0.05). POC-MRI with MLS-AI >1.5 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.