59639 Lighting-Invariant Computational Pupillometry in the Neuro-ICU: Evaluation of the PuRe Pupillometer

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INTRODUCTION: Quantitative pupillometry enhances neurologic assessment but is still hampered by ambient-light sensitivity and limited on-device processing. The PuReTM Pupillometer (Solvemed inc.) off-loads deep-learning algorithms to the neural and graphical processing units of a smartphone, generating a lighting-invariant Pupil Reactivity (PuReTM) score. METHODS: We performed a prospective, single-center observational study in a municipal neuro-ICU. Twelve adults (75% male; median age 59 y) with stroke, traumatic brain injury, hydrocephalus, brain tumor, or intracranial hypertension underwent 1,331 pupillometry recordings (median 44 per patient) under dim (4 lux) to bright (1,200 lux) conditions while receiving standard sedatives/analgesics. The study received institutional ethics committee approval (#419/24) from the Poznan University of Medical Sciences. Frame-wise pupil diameters and PuRe scores were computed on-device; analyses used Python (v3.11). RESULTS: Accuracy: Frame-to-frame diameter error was σ = 0.025 mm across iris colours, surpassing reported infrared-pupillometer precision. Lighting invariance: Conventional PLR parameters differed significantly between dim and bright light (p < 0.001), whereas PuRe scores did not (p = 0.449). Neurologic severity: PuRe was lower during severe neurologic status (GCS ≤ 8; median 1.85 ± 1.39) vs non-severe (median 4.11 ± 0.76; p < 0.001); ROC AUC = 0.94, sensitivity 84.3%, specificity 90.2%. Outcome prediction: Non-survivors showed markedly reduced PuRe (median 0.00 ± 1.15) compared with survivors (median 2.82 ± 1.62; p < 0.001). CONCLUSIONS: The PuRe Pupillometer delivers sub-0.03 mm precision and lighting-invariant reactivity metrics directly on a mobile device. PuRe strongly discriminates neurologic severity and predicts survival, supporting its integration into critical-care workflows as a fast, non-invasive, and resource-efficient neurologic monitor.

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