Journal of neurosurgical anesthesiology · 2026
Smartphone-Based Pupillometry for Noninvasive Detection of Elevated Intracranial Pressure in Nepali Acute Brain Injury Patients: A Pilot Study.
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Pant Aaradhya, Maxin Anthony, Farrokhi Farrokh, Shrestha Gentle S, Sarwal Aarti, Bohara Sandeep, Veeravagu Anand, Sharma Mohan R
Аннотация
BACKGROUND: Intracranial hypertension is a life-threatening complication of acute brain injuries such as traumatic brain injury (TBI), subarachnoid hemorrhage (SAH), or intracerebral hemorrhage (ICH). In low-income and middle-income countries (LMICs), limited resources can delay timely neurocritical interventions. Smartphone-based quantitative pupillometry offers a scalable solution for early detection of elevated intracranial pressure (ICP). Here, we assessed its ability to (1) detect raised optic nerve sheath diameter (ONSD), a noninvasive surrogate for elevated ICP, and (2) classify severe TBI.
METHODS: Thirty-eight Nepali ICU patients with TBI (n=16), SAH (n=10), or ICH (n=12) underwent daily sonographic ONSD and pupillary light reflex (PLR) assessments through the PupilScreen app (Apertur Inc., Seattle, WA) over 7 days. Machine learning classifiers were trained on PLR features to detect elevated ONSD (>6.0 mm). To identify severe TBI (Glasgow Coma Scale [GCS] ≤8 on admission), classifiers were trained on PLR features, ONSD, or both.
RESULTS: For ONSD >6.0 mm, a random forest model achieved an AUC of 0.66, with a sensitivity of 0.31 and specificity of 0.80. For identifying severe TBI, the optimal classifier was a random forest model incorporating ONSD and a subset of PLR metrics, with a sensitivity of 0.93, specificity of 1.00, and AUC of 0.96.
CONCLUSION: In this pilot study, smartphone-based pupillometry showed modest ability for detecting elevated ONSD. However, its high performance in severe TBI classification warrants further evaluation. Larger, multicenter studies evaluating triage utility in prehospital and resource-limited settings are warranted to validate and extend these findings.
METHODS: Thirty-eight Nepali ICU patients with TBI (n=16), SAH (n=10), or ICH (n=12) underwent daily sonographic ONSD and pupillary light reflex (PLR) assessments through the PupilScreen app (Apertur Inc., Seattle, WA) over 7 days. Machine learning classifiers were trained on PLR features to detect elevated ONSD (>6.0 mm). To identify severe TBI (Glasgow Coma Scale [GCS] ≤8 on admission), classifiers were trained on PLR features, ONSD, or both.
RESULTS: For ONSD >6.0 mm, a random forest model achieved an AUC of 0.66, with a sensitivity of 0.31 and specificity of 0.80. For identifying severe TBI, the optimal classifier was a random forest model incorporating ONSD and a subset of PLR metrics, with a sensitivity of 0.93, specificity of 1.00, and AUC of 0.96.
CONCLUSION: In this pilot study, smartphone-based pupillometry showed modest ability for detecting elevated ONSD. However, its high performance in severe TBI classification warrants further evaluation. Larger, multicenter studies evaluating triage utility in prehospital and resource-limited settings are warranted to validate and extend these findings.