Validating a machine learning model of eye tracking in children with cortical visual impairment (CVI)

Project summary
Cortical visual impairment (CVI) is the leading cause of pediatric visual impairment in developed countries. There
is no evidence-based treatment, and design of clinical trials is hampered by the absence of a validated method
of visual assessment that captures the numerous aspects of visual function that are compromised in pediatric
CVI. Our laboratory is investigating the use of eye tracking in children with CVI. During eye tracking, an infrared
camera tracks the pupillary and corneal light reflections while a child watches visual stimuli on a computer
monitor. The eye tracker calculates the direction of eye gaze with high spatial and temporal frequency. Our eye
tracking protocol assesses multiple afferent, efferent, and higher-order visual parameters during a 12-minute
recording session. Our initial data show that eye tracking is reliable and quantifies multiple visual and oculomotor
parameters in children with CVI. Given the large amount of data generated by eye tracking (2,000 data points
per second), higher-level analytics are required. We will validate a machine-learning model of eye tracking in
children with CVI via three Specific Aims. In Aim 1, we will quantify deficits of visual function in pediatric CVI
using eye tracking, strengthening the findings in our preliminary data by inclusion of a well-powered sample. In
Aim 2, we will use machine learning to develop a CVI eye tracking severity score. In Aim 3, we will validate eye
tracking by comparing and contrasting with two other methods of visual assessment in children with CVI, sweep
visual evoked potentials and the CVI Range. Together, these studies will establish eye tracking as a quantitative,
objective, and comprehensive measure of visual function in pediatric CVI. In the R01 application planned at the
end of the K23 award period, we will incorporate the CVI eye tracking severity score as an outcome measure in
a longitudinal study of standard and targeted therapies for CVI. In pursuit of these aims, I will be mentored by a
highly experienced, interdisciplinary, internationally recognized team at Children’s Hospital Los Angeles and
University of Southern California. Under their guidance, I will also pursue a Masters degree in Applied Data
Science and gain experiential learning in electrophysiology. The training acquired during my Career
Development Award will enable me to transition to an independent investigator leading a research program
focused on developing next-generation technologies to interrogate the visual system in children with a variety of
neurodevelopmental disorders.

Grant Number: 5K23EY033790-05
NIH Institute/Center: NIH
Principal Investigator: Melinda Chang