Clinical and genetic analysis of retinopathy of prematurity

早产儿视网膜病变的临床及遗传学分析

• 批准号: 10431850
• 负责人: John Peter Campbell
• 金额: $ 58.28万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10431850
• 关键词: Address; Adoption; Affect; Algorithms; Area; Artificial Intelligence; Bioinformatics; Biomedical Research; Blindness; Blood Vessels; Caring; Childhood; Clinical; Clinical Informatics; Clinical Medicine; Clinical Research; Cohort Studies; Computational Biology; Data; Detection; Development; Diagnosis; Disease; Disease Management; Environment; Evaluation; Expert Systems; Feedback; Funding; Gene Expression; Genes; Genetic; Genetic Markers; Genetic Risk; Genomics; Genotype; Goals; Grant; Health; Image; Image Analysis; Infant; Informatics; Information Management; International; Knowledge; Longitudinal Studies; Machine Learning; Macular degeneration; Measurement; Medical Genetics; Mendelian randomization; Methods; Modeling; Molecular Genetics; Network-based; Ophthalmic examination and evaluation; Ophthalmology; Other Genetics; Paper; Pathogenesis; Peer Review; Perception; Performance; Phenotype; Predisposition; Premature Birth; Premature Infant; Publishing; Reference Standards; Research; Research Personnel; Retina; Retinopathy of Prematurity; Risk; Risk Factors; Severities; System; Technology; Testing; United States; Validation; Work; analytical tool; base; biomedical informatics; care delivery; clinical diagnosis; clinical examination; clinical phenotype; clinical risk; clinically significant; computer science; data access; data integration; deep learning; detection platform; diagnosis standard; disorder risk; feature extraction; genetic analysis; genetic variant; high risk; imaging genetics; improved; insight; machine learning prediction; multidisciplinary; multiple data types; neovascular; neural network; novel; phenotypic data; prospective; prototype; real world application; recruit; retinal imaging; risk prediction model; screening; serial imaging; supplemental oxygen

Project Summary The long-term goal of this project is to establish a quantitative framework for retinopathy of prematurity (ROP) care based on clinical, imaging, genetic, and informatics principles. In the previous grant period, we have developed artificial intelligence methods for ROP diagnosis, but real-world adoption has been limited by lack of prospective validation and by perception of these systems as “black boxes” that do not explain their rationale for diagnosis. Furthermore, although biomedical research data are being generated at an enormous pace, much less work has been done to integrate disparate scientific findings across the spectrum from genomics to imaging to clinical medicine. This renewal will address current gaps in knowledge in these areas. Our overall hypotheses are that developing a quantitative framework for ROP care using artificial intelligence and analytics will improve clinical disease management, that building “explainable” artificial intelligence systems will enhance clinical acceptance and educational opportunities, and that analysis of relationships among clinical, imaging, environmental, and genetic findings, in ROP will improve understanding of disease pathogenesis and risk. These hypotheses will be tested using three Specific Aims: (1) Evaluation performance of an artificial intelligence system for ROP diagnosis and screening prospectively. This will include: (a) recruit a target of over 2000 eye exams including wide-angle retinal images from 375 subjects at 5 centers, (b) optimize an image quality detection algorithm we have recently developed, and (c) analyze system accuracy for ROP diagnosis and screening (using a novel quantitative vascular severity scale). (2) Improve the interpretability of our existing artificial intelligence methods for ROP diagnosis. This will include: (a) increase “explainability” of systems by combining deep learning with traditional feature extraction methods, (b) develop neural networks to identify changes between serial images, and (c) evaluate these methods through systematic feedback by experts. (3) Develop integrated models for ROP pathogenesis and risk. This will include: (a) build and improve ROP risk prediction models based on clinical, image, and demographic features, and (b) integrate genetic, imaging, clinical, and environmental variables through genetic risk prediction by machine learning, by investigating casual relationships with genetic variants and genetic risk scores, and by incorporating SNP associations with gene expression measurements to identify functional genes of ROP. Ultimately, these studies will significantly reduce barriers to adoption of technologies such as artificial intelligence for clinicians, and will demonstrate a prototype for health information management which combines genotypic and phenotypic data. This project will be performed by a multi-disciplinary team of investigators who have worked successfully together for nearly 10 years, and who have expertise in ophthalmology, biomedical informatics, computer science, computational biology, ophthalmic genetics, genetic analysis, and statistical genetics.

项目摘要 该项目的长期目标是建立一个定量框架，以实现早产性视网膜病变（ROP） 基于临床，成像，遗传和信息原则的护理。在上一个赠款期，我们有 开发的人工智能方法用于ROP诊断，但实际采用的限制是由于缺乏 前瞻性验证并通过将这些系统视为“黑匣子”，这些系统无法解释其理由 用于诊断。此外，尽管在巨大空间中生成了生物医学研究数据，但 从基因组学到整个频谱之间的不同科学发现进行了少得多的工作 成像临床医学。这种续约将解决这些领域知识的当前差距。我们的整体 假设是使用人工智能和分析来开发一个定量框架以进行ROP护理 将改善临床疾病管理，建立“可解释的”人工智能系统将增强 临床接受和教育机会，以及对临床，成像之间关系的分析 ROP中的环境和遗传发现将改善对疾病发病机理和风险的理解。 这些假设将使用三个特定目的进行测试：（1）人造的评估性能 ROP诊断和前瞻性筛查的情报系统。这将包括：（a）招募一个目标 2000眼检查，包括来自5个中心的375名受试者的广角残差图像，（b）优化图像 我们最近开发的质量检测算法，以及（c）ROP诊断的分析系统精度 和筛查（使用新型的定量血管严重程度量表）。 （2）提高我们的解释性 现有的人工智能方法用于ROP诊断。这将包括：（a）增加的“解释性” 通过将深度学习与传统特征提取方法相结合，（b）开发神经网络 确定串行图像之间的变化，（c）通过系统反馈评估这些方法 专家。 （3）开发用于ROP发病机理和风险的综合模型。这将包括：（a）建立和改进 ROP风险预测模型基于临床，图像和人口特征，以及（b）综合遗传， 通过机器学习，通过遗传风险预测，通过机器学习的成像，临床和环境变量 研究与遗传变异和遗传风险评分的随意关系，并通过编码SNP 与基因表达测量值的关联，以鉴定ROP的功能基因。最终，这些 研究将大大减少采用诸如临床医生人工智能等技术的障碍， 并将展示一个结合基因型和的健康信息管理原型 表型数据。该项目将由工作人员的多学科团队进行 成功在一起近10年，并且在眼科，生物医学信息中拥有专业知识， 计算机科学，计算生物学，眼科遗传学，遗传分析和统计遗传学。