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@article{183966,
        author = {Sumaiya Sultana and Dr.Megha Rani Raigonda},
        title = {Automatic Detection of Genetic Diseases in Pediatric Age Using Pupillometry},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {3747-3755},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183966},
        abstract = {Improving health outcomes and directing therapeutic treatments requires early detection of genetic abnormalities in children.  This work introduces a non-invasive automated technology that uses pupillometry and machine learning to identify genetic disorders in children.  Pupillometry is a diagnostic tool that detects anomalies in the nervous system and the genes by measuring the pupil's reaction to visual stimuli.  A dataset was created by collecting pupillary response data under controlled illumination settings from children with known genetic disorders as well as from healthy youngsters.  Pupil response curves were used to extract important properties, such as recovery time, amplitude, and latency.  Subject classification using these characteristics was taught to a variety of supervised machine learning algorithms, including Neural Networks, Support Vector Machines (SVM), and Random Forests.  With 99.5% accuracy and high sensitivity and specificity scores, the Random Forest classifier outperformed all of the other models evaluated.  A quick, kid-friendly, and inexpensive diagnostic aid, the suggested method proves the practicability of combining pupillometry with AI for early genetic disease screening.  Expanded datasets, generalizability across disorders, and interaction with other biometric modalities are all areas that will be investigated further in future study.},
        keywords = {Pupillometry, Genetic, Support Vector Machines (SVM), Random Forests, Neural Networks},
        month = {August},
        }