BACKGROUND The neurological exam is pivotal in assessing patients with neurological conditions but has severe limitations: it can vary between examiners, it may not discern subtle or subacute changes, and because it is intermittent can delay recognition of new deficits. Even in the Neuroscience Intensive Care Unit (NSICU), neurological exams are conducted only hourly. The majority of ICUs/hospitals lack subspecialized neurocritical care services, exacerbating this neurologic monitoring gap. We hypothesized that pose AI, a machine learning approach to track patient position, could provide a continuous and relevant method of neurological monitoring.

METHODS We retrospectively collected video segments from patients in the NSICU and the Epilepsy Monitoring Unit (EMU) who underwent video-EEG at a large, urban hospital between July, 2024 to January, 2025. We externally validated two leading pose AI models, ViTPose and Meta Sapiens. We then developed a robust movement index and evaluated its correlation with two measures of consciousness obtained through hourly physical exams, the Glasgow Coma Scale (GCS) and Richmond Agitation Sedation Scale (RASS).

RESULTS We collected 998,520 video minutes from 119 NSICU and EMU patients. ViTPose demonstrated superior performance to Sapiens across multiple metrics, so we used ViTPose to calculate a computer vision movement index (λMI). We observed higher movement with increasing GCS (GCS 3–8 λMI=0.52, GCS 9–13 λMI=0.70, GCS 14 λMI=3.52, GCS 15 λMI=10.99, P=0.01), a 21-fold increase from the lowest to highest tranche. We also observed 10-fold higher movement in awake/agitated patients (RASS>-1 λMI=6.59) compared to those who were asleep/sedated (RASS≤-1 λMI=0.67, P=0.005). Taken together, we developed a novel computer vision movement index and demonstrated expected correlations with GCS and RASS scores in NSICU patients.

CONCLUSION We show that pose AI can provide minimally invasive, continuous and clinically relevant neuro-monitoring in critically ill patients. Neurological conditions account for the highest global disease burden and pose AI may be a low-cost, explainable, and scalable AI solution to address this pressing need for neuro-telemetry.

The authors have declared no competing interest.

This study was funded by NIH Postdoctoral TL1 Clinical & Translational Research Career Development Award to Rui Feng and Thrasher Foundation Award to Felix Richter.

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