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. 2024 Jul 9;14(7):686.
doi: 10.3390/brainsci14070686.

Age-Based Developmental Biomarkers in Eye Movements: A Retrospective Analysis Using Machine Learning

Melissa Hunfalvay  1   2 , Takumi Bolte  1 , Abhishek Singh  1 , Ethan Greenstein  3 , Nicholas P Murray  4 , Frederick Robert Carrick  5   6   7   8
Affiliations

Age-Based Developmental Biomarkers in Eye Movements: A Retrospective Analysis Using Machine Learning

Melissa Hunfalvay et al. Brain Sci. .

Abstract

This study aimed to identify when and how eye movements change across the human lifespan to benchmark developmental biomarkers. The sample size comprised 45,696 participants, ranging in age from 6 to 80 years old (M = 30.39; SD = 17.46). Participants completed six eye movement tests: Circular Smooth Pursuit, Horizontal Smooth Pursuit, Vertical Smooth Pursuit, Horizontal Saccades, Vertical Saccades, and Fixation Stability. These tests examined all four major eye movements (fixations, saccades, pursuits, and vergence) using 89 eye-tracking algorithms. A semi-supervised, self-training, machine learning classifier was used to group the data into age ranges. This classifier resulted in 12 age groups: 6-7, 8-11, 12-14, 15-25, 26-31, 32-38, 39-45, 46-53, 54-60, 61-68, 69-76, and 77-80 years. To provide a descriptive indication of the strength of the self-training classifier, a series of multiple analyses of variance (MANOVA) were conducted on the multivariate effect of the age groups by test set. Each MANOVA revealed a significant multivariate effect on age groups (p < 0.001). Developmental changes in eye movements across age categories were identified. Specifically, similarities were observed between very young and elderly individuals. Middle-aged individuals (30s) generally showed the best eye movement metrics. Clinicians and researchers may use the findings from this study to inform decision-making on patients' health and wellness and guide effective research methodologies.

Keywords: eye movements; eye tracking; lifespan development; machine learning.

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Conflict of interest statement

Greenstein, Singh, Murray, and Carrick have no conflicts of interest to declare. Hunfalvay and Bolte are full-time employees of RightEye, LLC.

Figures

Figure 1
Figure 1
Head box guidance system showing real-time head box adjustments and ideal participant location to RightEye vision system. Measurement in centimeters (cm).
Figure 2
Figure 2
Frequency of eye movement, blink, and pupil algorithms.
Figure 3
Figure 3
Age distribution histogram.
Figure 4
Figure 4
The left graph (a) shows positive skewness in the Vertical Saccades test variable of Band 2 Under. The right graph (b) shows negative skewness in the Vertical Synchronization Circular Smooth Pursuit test variable.
Figure 5
Figure 5
Example of the inverted U as seen in the latent smooth pursuit percentage demonstrating eye movement behavior across the lifespan.
See this image and copyright information in PMC

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This research received no external funding.

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