ArticleFrontiers in neuroscience2022
Mobile Smartphone-Based Digital Pupillometry Curves in the Diagnosis of Traumatic Brain Injury.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 22 citations in OpenAlex.
- The Ocular Signature of TBI: A Narrative Review of Structural Changes and Potential Candidate Biomarkers.Life (Basel, Switzerland) · 2026Review
- Digital Innovations for Clinical Assessment in Acquired Brain Injury: Scoping Review.Journal of medical Internet research · 2025Article
- Low-Cost Strategies for the Development of Neurocritical Care in Resource-Limited Settings.Neurocritical care · 2025Review
- Smartphone-based pupillometry in resource-limited settings: an 'eye-catching' prospect.Brain & spine · 2025Article
- Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion.Diagnostics (Basel, Switzerland) · 2024Article
- Use of artificial intelligence to support prehospital traumatic injury care: A scoping review.Journal of the American College of Emergency Physicians open · 2024Article
- From real world data to real world evidence to improve outcomes in neuro-ophthalmology.Eye (London, England) · 2024Review
- Smartphone Pupillometry and Machine Learning for Detection of Acute Mild Traumatic Brain Injury: Cohort Study.JMIR neurotechnology · 2024Article
- Developing a Theoretically Informed Implementation Model for Telemedicine-Delivered Medication for Opioid Use Disorder: Qualitative Study With Key Informants.JMIR mental health · 2023Article
- Interdevice Agreement between a Smartphone and a Commercial Pupillometer.International journal of applied & basic medical researchArticle
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The pupillary light reflex (PLR) and the pupillary diameter over time (the PLR curve) is an important biomarker of neurological disease, especially in the diagnosis of traumatic brain injury (TBI). We investigated whether PLR curves generated by a novel smartphone pupillometer application could be easily and accurately interpreted to aid in the diagnosis of TBI. Methods: A total of 120 PLR curves from 42 healthy subjects and six patients with TBI were generated by PupilScreen. Eleven clinician raters, including one group of physicians and one group of neurocritical care nurses, classified 48 randomly selected normal and abnormal PLR curves without prior training or instruction. Rater accuracy, sensitivity, specificity, and interrater reliability were calculated. Results: Clinician raters demonstrated 93% accuracy, 94% sensitivity, 92% specificity, 92% positive predictive value, and 93% negative predictive value in identifying normal and abnormal PLR curves. There was high within-group reliability ( Conclusion: The PupilScreen smartphone application-based pupillometer produced PLR curves for clinical provider interpretation that led to accurate classification of normal and abnormal PLR data. Interrater reliability was greater than previous studies of manual pupillometry. This technology may be a good alternative to the use of subjective manual penlight pupillometry or digital pupillometry.
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