International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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PupilDx: Novel AI driven Pupillary device for Non-Invasive Neurological Risk Screening

Author(s) Mr. Veerendra Battula
Country India
Abstract This study presents PupilDx, an AI-powered neurodiagnostic framework designed to detect early signs of neurological dysfunction associated with motor impairment and paralysis through the analysis of real-time pupillary responses. A total of 300 individuals underwent blind screening using high-frequency infrared pupillometry, capturing dynamic features such as baseline pupil diameter, constriction latency and velocity, dilation parameters, hippus amplitude, and inter-eye asymmetry (anisocoria). These features were processed using a supervised machine learning pipeline that included feature engineering, dimensionality reduction, and classification through models such as Random Forest, XGBoost, and LSTM. The system achieved an overall accuracy of 91.7%, with a sensitivity of 90.0%, specificity of 93.3%, precision of 93.1%, and F1-score of 91.5%. The area under the ROC curve (AUC) was 0.95, reflecting excellent discriminative performance. Feature importance analysis identified constriction latency and anisocoria as the most predictive parameters. These findings suggest that PupilDx offers a robust, non-invasive, and real-time solution for early neurological risk screening. Its integration of physiological biomarkers with interpretable machine learning models provides a scalable tool for clinical decision support and proactive monitoring. Further validation in real-world clinical settings will be essential to confirm its diagnostic utility.
Keywords PupilDx, paralysis, AI, Machine learning
Field Computer Applications
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-30
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.49322
Short DOI https://doi.org/g9r7kr
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