International Journal For Multidisciplinary Research

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

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Automatic detection of Genetic Diseases in pediatric age using pupillometry

Author(s) Dr. K Jaya Prakash, Mr. L Ritesh, Mr. I Sasank, Ms. V Subbulu, Mr. E Sundar Rao
Country India
Abstract Early detection of genetic diseases in pediatric populations is essential for timely intervention and improved healthcare outcomes. However, conventional diagnostic methods are often invasive, expensive, and time-consuming, limiting their applicability for large-scale screening. This paper proposes a novel, non-invasive, and automated approach for early detection of genetic disorders using pupillometry. The system analyzes dynamic pupil responses, including constriction latency, dilation rate, and reflex amplitude, captured through infrared eye-tracking under controlled light stimuli. These pupillary features reflect the functioning of the autonomic nervous system, which is often affected in genetic and neurodevelopmental disorders. To enhance diagnostic accuracy, the proposed framework integrates signal processing techniques with machine learning models such as Support Vector Machines and Convolutional Neural Networks for effective classification of normal and abnormal pupil behaviors. Experimental results demonstrate high accuracy and reliability in detecting early indicators of disorders such as autism spectrum disorder and Down Syndrome. The proposed method provides a rapid, cost-effective, and child-friendly screening solution, making it suitable for real-time clinical applications and large-scale pediatric healthcare programs.
Keywords Pupillometry, Genetic Disorders, Pediatric Diagnosis, Machine Learning, Deep Learning, Eye Tracking, Non-invasive Detection, Biomedical Signal Processing, Artificial Intelligence in Healthcare, Early Disease Screening
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-27
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.72542
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E-ISSN 2582-2160
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