Machine learning approach for ambient-light-corrected parameters and the Pupil Reactivity (PuRe) score in smartphone-based pupillometry

Bogucki, A; John, I; Zinkiewicz, Ł; Jachura, M; Jaworski, D; Suwała, K; Chrost, H; Wlodarski, M; Kałużny, J; Campbell, D; Bakken, P; Pandya, S; Chrapkiewicz, R; Manohar, SG

Journal article

Machine learning approach for ambient-light-corrected parameters and the Pupil Reactivity (PuRe) score in smartphone-based pupillometry

Abstract:: IntroductionGlaucoma is a leading cause of blindness, often progressing asymptomatically until significant vision loss occurs. Early detection is crucial for preventing irreversible damage. The pupillary light reflex (PLR) has proven useful in glaucoma diagnosis, and mobile technologies like the AI-based smartphone pupillometer (AI Pupillometer) offer a promising solution for accessible screening. This study assesses the reliability of the AI Pupillometer in detecting glaucoma.MethodsIn Experiment 1, 20 healthy participants were assessed using both the AI Pupillometer and the NPi-200 device to evaluate equivalence in measuring PLR. Each eye underwent three trials. Experiment 2 included 46 participants, 24 with primary open-angle glaucoma (POAG) and 22 healthy controls. PLR measurements from the AI Pupillometer were correlated with structural and functional ocular parameters. An additional study expanded the sample to 387 participants (103 glaucoma patients, 284 controls), focusing on differential pupillometry parameters to minimize ambient light interference.ResultsIn Experiment 1, the AI Pupillometer demonstrated strong correlations with the NPi-200 in key parameters like initial pupil size (r = 0.700), constricted pupil size (r = 0.755), and constriction velocity (r = 0.541), confirming its reliability. In Experiment 2, although no statistically significant differences in light-corrected PLR parameters were found between groups, glaucoma patients had a marginally higher constricted pupil size (p = 0.1632). Significant correlations were observed between pupillometry and advanced ocular imaging results, notably between constriction amplitude and visual field loss. The additional study revealed significant differences in constriction amplitude (p = 0.014) and relative pupil size change (p = 0.0072) between glaucoma patients and controls, reinforcing the AI Pupillometer’s diagnostic potential.ConclusionThis study confirms the AI Pupillometer as a reliable, accessible tool for glaucoma screening. Mobile diagnostics could enhance early detection, improving outcomes for glaucoma patients

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Bogucki, A., John, I., Zinkiewicz, Ł., Jachura, M., Jaworski, D., Suwała, K., Chrost, H., Wlodarski, M., Kałużny, J., Campbell, D., Bakken, P., Pandya, S., Chrapkiewicz, R., & Manohar, S. G. (2024). Machine learning approach for ambient-light-corrected parameters and the Pupil Reactivity (PuRe) score in smartphone-based pupillometry. Frontiers in Neurology, 15, 1363190–1363190.

MLA Style

Bogucki, A, et al. “Machine Learning Approach for Ambient-Light-Corrected Parameters and the Pupil Reactivity (PuRe) Score in Smartphone-Based Pupillometry.” Frontiers in Neurology, vol. 15, 2024, pp. 1363190–90.

Chicago Style

Bogucki, A, I John, Ł Zinkiewicz, et al. 2024. “Machine Learning Approach for Ambient-Light-Corrected Parameters and the Pupil Reactivity (PuRe) Score in Smartphone-Based Pupillometry.” Frontiers in Neurology 15: 1363190–90.
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