Abstract

Summary

This paper applies machine learning techniques to pupillometry data in children, leveraging the distinct kinetics of rod/cone photoreceptors and melanopsin-containing ipRGCs to detect and forecast disorders. The practical implication is that pupillary light response profiling could serve as a non-invasive diagnostic tool, with potential relevance to lighting environments designed to elicit diagnostic pupillary responses.
Categories

Categories

Eye Health & Vision: Paper uses pupillometry data to detect and forecast disorders in children, implicating pupillary light response mechanisms.
The Science of Light: Paper references melanopsin-containing retinal ganglion cells (ipRGCs) and their role in pupillary light responses, relevant to photoreceptor biology.
Authors

Author(s)

D Chaluvadi, TD Reddy, DVS Pavan
Publication Date

Publication Year

2022
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