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

基本信息

批准号：
10620354
负责人：
John Peter Campbell
金额：
$ 59.82万
依托单位：
OREGON HEALTH & SCIENCE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-30 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10620354
关键词：
Address Adoption Affect Algorithms Area Artificial Intelligence Bioinformatics Biomedical Research Blindness Blood Vessels Caring Childhood Clinical Clinical Medicine Cohort Studies Computational Biology Data Detection Development Diagnosis Disease Disease Management Disparate Education 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 Machine Learning Macular degeneration Measurement Mendelian randomization Methods Modeling Molecular Network-based 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 Vascular Diseases Visualization Work analytical tool artificial intelligence method 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 improved insight machine learning prediction model building multidisciplinary multiple data types neovascular neural network novel ophthalmic examination phenotypic data prospective prototype real world application recruit retinal imaging risk prediction model screening serial imaging supplemental oxygen

项目摘要

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.

项目概要该项目的长期目标是建立早产儿视网膜病变（ROP）的定量框架基于临床、影像学、遗传和信息学原理的护理在之前的拨款期间，我们有。开发了用于 ROP 诊断的人工智能方法，但由于缺乏前瞻性验证以及将这些系统视为“黑匣子”，无法解释其基本原理为了进一步诊断，尽管生物医学研究数据正在以惊人的速度生成，在整合从基因组学到跨领域的不同科学发现方面所做的工作要少得多。影像学到临床医学的更新将解决我们在这些领域的整体知识差距。假设正在使用人工智能和分析开发 ROP 护理的定量框架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度和教育机会，以及对临床、影像、 ROP 中的环境和遗传发现将提高对疾病发病机制和风险的了解。这些假设将使用三个具体目标进行测试：（1）人工的评估性能用于 ROP 诊断和前瞻性筛查的情报系统这将包括： (a) 招募超过目标。 2000 次眼科检查，包括 5 个中心 375 名受试者的广角视网膜图像，(b) 优化图像我们最近开发的质量检测算法，以及 (c) 分析 ROP 诊断的系统准确性和筛查（使用定量新型血管严重程度量表）（2）提高我们的可解释性。用于 ROP 诊断的现有人工智能方法这将包括：(a) 提高诊断的“可解释性”。通过将深度学习与传统特征提取方法相结合的系统，（b）开发神经网络识别连续图像之间的变化，并且（c）通过系统反馈评估这些方法 (3) 开发 ROP 发病机制和风险的综合模型，其中包括： (a) 建立和改进。基于临床、图像和综合人口特征的 ROP 风险预测模型，以及 (b) 遗传、通过机器学习进行遗传风险预测，通过成像、临床和环境变量随意调查与遗传变异和遗传风险评分的关系，并通过纳入 SNP 最终，这些与基因表达测量的关联来识别 ROP 的功能基因。研究将大大减少忠诚者采用人工智能等技术的障碍，并将展示一个健康信息管理原型，该原型结合了基因型和该项目将由多学科研究人员团队执行。成功合作近 10 年，他们在眼科、生物医学信息学、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。

项目成果

期刊论文数量（39）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Operationalization of Retinopathy of Prematurity Screening by the Application of the Essential Public Health Services Framework.

通过应用基本公共卫生服务框架来实施早产儿视网膜病变筛查。

DOI：
发表时间：
2023-01-01
期刊：
影响因子：
0
作者：
Sobhy, Myrna;Cole, Emily;Jabbehdari, Sayena;Valikodath, Nita G;Al;Kalinoski, Lauren;Chervinko, Margaret;Cherwek, David Hunter;Chuluunkhuu, Chimgee;Shah, Parag K;K C, Sagun;Jonas, Karyn E;Scanzera, Angel;Yap, Vivien L;Yeh, Steven
通讯作者：
Yeh, Steven

Telemedical diagnosis of retinopathy of prematurity: accuracy of expert versus non-expert graders.

早产儿视网膜病变的远程医疗诊断：专家与非专家分级者的准确性。

DOI：
发表时间：
2010-03
期刊：
影响因子：
0
作者：
Williams, Steven L;Wang, Lu;Kane, Steven A;Lee, Thomas C;Weissgold, David J;Berrocal, Audina M;Rabinowitz, Daniel;Starren, Justin;Flynn, John T;Chiang, Michael F
通讯作者：
Chiang, Michael F

Health Equity and Disparities in ROP Care: A Need for Systematic Evaluation.

ROP 护理中的健康公平和差异：需要系统评估。

DOI：
发表时间：
2022
期刊：
影响因子：
0
作者：
Ndukwe, Tochukwu;Cole, Emily;Scanzera, Angelica C;Chervinko, Margaret A;Chiang, Michael F;Campbell, John Peter;Chan, Robison Vernon Paul
通讯作者：
Chan, Robison Vernon Paul

Influence of Fluorescein Angiography on the Diagnosis and Management of Retinopathy of Prematurity.

荧光素血管造影对早产儿视网膜病变诊断和治疗的影响。

DOI：
10.1016/j.ophtha.2015.04.023
发表时间：
2015-08
期刊：
Ophthalmology
影响因子：
13.7
作者：
Klufas MA;Patel SN;Ryan MC;Patel Gupta M;Jonas KE;Ostmo S;Martinez-Castellanos MA;Berrocal AM;Chiang MF;Chan RV
通讯作者：
Chan RV

Accuracy of retinopathy of prematurity image-based diagnosis by pediatric ophthalmology fellows: implications for training.

儿科眼科研究员基于图像的早产儿视网膜病变诊断的准确性：对培训的影响。

DOI：
发表时间：
2011-12
期刊：
影响因子：
0
作者：
Myung, Jane S;Paul Chan, Robison Vernon;Espiritu, Michael J;Williams, Steven L;Granet, David B;Lee, Thomas C;Weissgold, David J;Chiang, Michael F
通讯作者：
Chiang, Michael F