Automatic Diagnosis of Congenital Conditions in Pediatric Age Using Pupillometry with Machine Learning

Children who suffer from inherited retinal illnesses have a significantly reduced ability to see. This can cause damage to either the inner or outer retina, which can lead to blindness in infants. The clinical measures that clinics use to screen children and young adults for visual impairments are i...

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Bibliographic Details
Published in2023 4th International Conference on Smart Electronics and Communication (ICOSEC) pp. 808 - 812
Main Authors VijayaKumari, E.N., Swetha, Kora, Kumar, Atul, Tulasi, Rajesh
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.09.2023
Subjects
CDSS
Chromatic
Electric potential
Machine learning
Machine learning algorithms
Pediatrics
Pupillometry
Retinal Disease
Sensitivity
Support vector machines
SVM
Visual impairment
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Summary:Children who suffer from inherited retinal illnesses have a significantly reduced ability to see. This can cause damage to either the inner or outer retina, which can lead to blindness in infants. The clinical measures that clinics use to screen children and young adults for visual impairments are insufficient. These are not the tactics that should be used with infants. Chromatic Pupillometry is a state-of-the-art method that has been developed to evaluate both the exterior and inner functioning of the retina. This study has used a technology called the Support Vector Machine (SVM), which is based on machine learning and makes use of pupillometry. Additionally, a system called "Clinical Decision Support System (CDSS)" is used. It is guaranteed that the desired outcome will be accomplished.
DOI:10.1109/ICOSEC58147.2023.10276019