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7 March 2022 Machine learning for comprehensive prediction of high risk for Alzheimer’s disease based on chromatic pupilloperimetry
Ygal Rotenstreich, Yael Lustig, Inbal Sharvit-Ginon, Yael Feldman, Michael Mrejen, Michal Schnaider Beeri, Aron Weller, Ramit Ravona-Springer, Ifat Sher
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Proceedings Volume PC11941, Ophthalmic Technologies XXXII; PC119410A (2022) https://doi.org/10.1117/12.2609331
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
The pupil light reflex (PLR) for focal chromatic stimuli was tested in 125 healthy middle-aged subjects offspring of Alzheimer Disease (AD) patients and 61 age-matched controls,. Machine learning algorithms identified features associated with PLR latency with an Area Under Curve of 0.91±0.05 in the left eye and 0.88 ± 0.05 in the right eye. Parameters associated with the contraction arm of the PLR were more discriminative compared to parameters associated with the relaxation arm. This study suggests that subtle changes in pupil constriction latency may be detected decades before the onset of AD clinical symptoms using a simple, non-invasive chromatic pupilloperimetry test.
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Ygal Rotenstreich, Yael Lustig, Inbal Sharvit-Ginon, Yael Feldman, Michael Mrejen, Michal Schnaider Beeri, Aron Weller, Ramit Ravona-Springer, and Ifat Sher "Machine learning for comprehensive prediction of high risk for Alzheimer’s disease based on chromatic pupilloperimetry", Proc. SPIE PC11941, Ophthalmic Technologies XXXII, PC119410A (7 March 2022); https://doi.org/10.1117/12.2609331
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KEYWORDS
Colorimetry
Data modeling
Alzheimer's disease
Machine learning
Performance modeling
Cognitive modeling
Control systems
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