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

Background and Purpose In 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 Methods POC-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. Results MLS-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 (p1.5 mm was positively predictive of poor discharge outcome in all patients (PPV=70%) and specifically in patients with IS (PPV=77%). Conclusion The 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.