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

验证皮质视觉障碍 (CVI) 儿童眼球追踪的机器学习模型

• 批准号: 10425929
• 负责人: Melinda Chang
• 金额: $ 23.46万
• 依托单位: CHILDREN'S HOSPITAL OF LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10425929
• 关键词: Ablation; Active Learning; Adopted; Adverse effects; Age; Behavior; Biomedical Engineering; Brain; California; Categories; Characteristics; Child; Childhood; Clinic; Clinical; Clinical Research; Clinical Trials; Clinical Trials Design; Clinical assessments; Collaborations; Contrast Sensitivity; Cornea; Crowding; Data; Data Analyses; Data Science; Data Set; Developed Countries; Disease; Electrophysiology (science); Enrollment; Evaluation; Evidence based treatment; Exhibits; Eye; Foundations; Frequencies; Future; Goals; International; Interview; Joints; K-Series Research Career Programs; Knowledge; Laboratories; Learning; Light; Longitudinal Studies; Los Angeles; Machine Learning; Master' s Degree; Measures; Mentored Patient-Oriented Research Career Development Award; Mentors; Methods; Neural Network Simulation; Neurodevelopmental Disorder; Neurologic; Ophthalmology; Outcome Measure; Parents; Patients; Pediatric Hospitals; Protocols documentation; Quality of life; Questionnaires; Research; Research Personnel; Resolution; Saccades; Sampling; Severities; Techniques; Technology; Time; Training; Translating; Universities; Vision; Vision Tests; Visual; Visual Acuity; Visual Pathways; Visual evoked cortical potential; Visual impairment; Visual system structure; base; cohort; comorbidity; computer monitor; cortical visual impairment; deep learning; deep neural network; experience; gaze; large datasets; machine learning model; neurodevelopment; next generation; novel; oculomotor; programs; prospective; sex; signal processing; success; targeted treatment; translational potential; visual control; visual dysfunction; visual stimulus; visual tracking

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.

项目总结