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: 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
• 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

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、遗传和信息学原理的护理。在上一个资助期内，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 用于诊断。此外，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。此次更新将解决这些领域目前的知识差距。我们的整体 假设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 ROP 前瞻性诊断和筛查的智能系统。这将包括： (a) 招募超过 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（使用新型定量血管严重程度量表）。 (2) 提高我们的可解释性 现有用于 ROP 诊断的人工智能方法。这将包括： (a) 增加“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 专家。 (3) 开发ROP发病机制和风险的综合模型。这将包括： (a) 建立和改进 基于临床、图像和人口特征的 ROP 风险预测模型，以及 (b) 整合遗传、 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 研究与遗传变异和遗传风险评分的因果关系，并通过纳入 SNP 与基因表达测量的关联以确定 ROP 的功能基因。最终，这些 研究将显着减少临床医生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 表型数据。该项目将由多学科研究人员团队执行，这些研究人员曾在 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

Implementation and evaluation of a tele-education system for the diagnosis of ophthalmic disease by international trainees.

国际学员诊断眼科疾病的远程教育系统的实施和评估。

• 发表时间: 2015
• 期刊: AMIA ... Annual Symposium proceedings. AMIA Symposium
• 作者: Campbell,JPeter;Swan,Ryan;Jonas,Karyn;Ostmo,Susan;Ventura,CamilaV;Martinez-Castellanos,MariaA;Anzures,RachelleGoAngSam;Chiang,MichaelF;Chan,RVPaul
• 通讯作者: Chan,RVPaul

Image analysis for retinopathy of prematurity: where are we headed?

早产儿视网膜病变的图像分析：我们将走向何方？

• DOI: 10.1016/j.jaapos.2012.08.001
• 发表时间: 2012
• 期刊: Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus
• 作者: Chiang,MichaelF

Toward a severity index for ROP: An unsupervised approach.

制定 ROP 严重程度指数：一种无监督方法。

• DOI: 10.1109/embc.2016.7590948
• 发表时间: 2016
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: PengTian;Ataer-Cansizoglu,Esra;Kalpathy-Cramer,Jayashree;Ostmo,Susan;Jonas,Karyn;Chan,RVPaul;Campbell,JPeter;Chiang,MichaelF;Erdogmus,Deniz
• 通讯作者: Erdogmus,Deniz

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: 10.1097/iio.0000000000000448
• 发表时间: 2023
• 期刊: International ophthalmology clinics
• 作者: Sobhy,Myrna;Cole,Emily;Jabbehdari,Sayena;Valikodath,NitaG;Al-Khaled,Tala;Kalinoski,Lauren;Chervinko,Margaret;Cherwek,DavidHunter;Chuluunkhuu,Chimgee;Shah,ParagK;KC,Sagun;Jonas,KarynE;Scanzera,Angel;Yap,VivienL;Yeh,Steven
• 通讯作者: Yeh,Steven

Accuracy of retinopathy of prematurity diagnosis by retinal fellows.

• DOI: 10.1097/iae.0b013e3181c9696a
• 发表时间: 2010-06
• 期刊: Retina (Philadelphia, Pa.)
• 作者: Paul Chan RV;Williams SL;Yonekawa Y;Weissgold DJ;Lee TC;Chiang MF
• 通讯作者: Chiang MF