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: 10595052
• 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/10595052
• 关键词: Ablation; Active Learning; Adopted; Adverse effects; Age; Behavior; Biomedical Engineering; Brain; California; Categories; Characteristics; Child; Childhood; Classification; Clinic; Clinical; 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; 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 Assessment; 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 System; Visual evoked cortical potential; Visual impairment; 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.

项目摘要 皮质视觉障碍（CVI）是发达国家小儿视觉障碍的主要原因。那里 没有循证治疗，并且缺乏经过验证的方法，临床试验的设计受到了阻碍 视觉评估捕获了在小儿损害的视觉功能的众多方面 CVI。我们的实验室正在调查CVI儿童的眼动追踪。在眼睛跟踪期间，红外线 相机跟踪瞳孔和角膜光反射，而孩子在计算机上观看视觉刺激 监视器。眼睛跟踪器以高空间和临时频率来计算眼睛凝视的方向。我们的眼睛 跟踪协议在12分钟内评估多个传入，有效和高阶视觉参数 录制会话。我们的初始数据表明，眼睛跟踪是可靠的，并量化了多个视觉和动眼动物 CVI儿童的参数。鉴于眼睛跟踪产生的大量数据（2,000个数据点 每秒），需要高级分析。我们将验证一种机器学习模型在 CVI的儿童通过三个特定目标。在AIM 1中，我们将量化小儿CVI中视觉功能的定义 使用眼科跟踪，通过包含有力的样本来增强我们的初步数据中的发现。在 AIM 2，我们将使用机器学习来开发CVI眼睛跟踪严重程度得分。在AIM 3中，我们将验证眼睛 通过比较和对比与CVI儿童中的另外两种视觉评估方法进行跟踪，Sweete 视觉引起的电位和CVI范围。这些研究将共同​​建立眼睛跟踪作为定量， 目的和全面测量小儿CVI中的视觉功能。在计划在R01申请中 K23奖励期结束时，我们将在CVI眼睛跟踪严重程度评分中作为结果度量 对CVI的标准疗法和靶向疗法的纵向研究。为了追求这些目标，我将被一个 洛杉矶儿童医院和 南加州大学。在他们的指导下，我还将攻读应用数据的硕士学位 科学并获得电生理学的经验。我职业生涯中获得的培训 开发奖将使我能够过渡到领导研究计划的独立调查员 专注于开发下一代技术，以询问各种各样的儿童的视觉系统 神经发育障碍。