Surrogate Augmented Deep Predictive Learning for Retinopathy of Prematurity

早产儿视网膜病变的替代增强深度预测学习

基本信息

批准号：
10740289
负责人：
Yong Chen
金额：
$ 48.21万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-30 至 2025-09-29
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10740289
关键词：
Address Age Age Factors Algorithms Birth Weight Blindness Caring Childhood Clinical Data Data Set Data Sources Demographic Factors Detection Development Diagnostic Disease Early Diagnosis Early identification Evaluation Event Eye Funding Future Gestational Age Goals Healthcare Image Image Analysis Infant Infant Care Learning Longitudinal, observational study Machine Learning Medical Methods North America Ophthalmologist Ophthalmology Performance Premature Infant Prevention Prospective Studies Quality of Care Reader Reproducibility Research Resources Retina Retinopathy of Prematurity Risk Risk Factors Schedule Statistical Methods Survival Rate Techniques Time Training United States Validation clinical practice deep learning experience follow-up high risk infant improved innovation learning algorithm machine learning algorithm novel ophthalmic examination portability prediction algorithm prevent retinal imaging risk stratification serial imaging skills tool

项目摘要

Surrogate Augmented Deep Predictive Learning for Retinopathy of Prematurity ABSTRACT This proposal aims to develop novel surrogate augmented deep predictive learning algorithms for predicting retinopathy of prematurity (ROP). The proposal directly addresses a critical clinical burden in ophthalmology that limited ROP experts are available in the United States and worldwide, yet the early detection of ROP for timely treatment has tremendous clinical benefit for infants in preventing childhood blindness. Using a unique and massive dataset with 7905 image sets collected from a longitudinal observational study of 1257 premature infants from 13 centers in North America, we plan to develop, validate, and evaluate novel analytic algorithms that hold a promise of directly improving clinical practice in the ROP care of premature infants. The overarching goals of this proposal are: (1) to develop novel methods for performing risk stratification through the surrogate augmented deep predictive learning of earliest retinal images (prior to 34 weeks postmenstrual age [PMA]) and the most important ROP risk factors (birth weight, gestational age) for early prediction of referral-warranted ROP (RW-ROP), defined as plus disease, ROP in zone I, or stage 3 ROP or greater; and (2) to optimize the ROP schedule through the surrogate augmented deep predictive learning of accumulated longitudinal retinal images for the dynamic prediction of RW-ROP. Our methods, after proper validation in future prospective studies, may serve as a useful tool for ROP risk stratification and optimization of scheduling of ROP examinations, which can reduce the burden of ROP examination for both infants and ophthalmologists while improving the eye care of premature infants for the prevention of childhood blindness. The Specific Aims to achieve these goals are: Aim #1: Develop and evaluate the surrogate augmented deep predictive learning of the earliest retinal image sets taken prior to 34 weeks PMA and demographic factors to predict RW-ROP. Accurate risk stratification through earlier prediction of RW-ROP will help identify high-risk infants for close follow-up by ophthalmologists for early detection and timely treatment of ROP, and low risk infants who are currently receiving unnecessary physically stressful retinal examinations for less frequent ROP examinations. Aim #2: Implement the surrogate augmented deep predictive learning of accumulated retinal images over time to dynamically predict RW-ROP. The dynamic prediction of the future course of ROP by deep learning of longitudinally accumulated retinal images will help optimize the schedule of ROP examinations by ophthalmologists, thus reduce the burden of ROP examinations for both infants and ophthalmologists. The successful completion of this project will lead to novel analytic algorithms of retinal images for early identification of high-risk infants for close follow-up and for optimization of ROP exam schedule, which will lead to earlier detection and timely treatment of ROP while minimizing the number of ROP exams. This research is highly feasible and potentially transformative in its global impact on the ROP care of premature infants.

替代增强深度预测学习用于早产儿视网膜病变抽象的该提案旨在开发新颖的替代增强深度预测学习算法来预测早产儿视网膜病变（ROP）。该提案直接解决了眼科的关键临床负担美国和世界各地的 ROP 专家有限，但如何及早发现 ROP 并及时进行治疗治疗对婴儿预防儿童失明具有巨大的临床益处。使用独特且包含 7905 个图像集的海量数据集，这些图像集是从对 1257 个早产儿的纵向观察研究中收集的来自北美 13 个中心的婴儿，我们计划开发、验证和评估新颖的分析算法有望直接改善早产儿 ROP 护理的临床实践。该提案的总体目标是：（1）通过以下方式开发执行风险分层的新方法：最早视网膜图像（月经后 34 周之前）的替代增强深度预测学习年龄 [PMA]）和最重要的 ROP 危险因素（出生体重、孕龄），用于早期预测转诊保证 ROP (RW-ROP)，定义为附加疾病、ROP 处于 I 区或 3 期 ROP 或更高阶段；和（2）通过累积的代理增强深度预测学习来优化 ROP 调度用于 RW-ROP 动态预测的纵向视网膜图像。我们的方法经过适当验证后未来的前瞻性研究，可能作为 ROP 风险分层和调度优化的有用工具 ROP检查，可以减轻婴儿和眼科医生的ROP检查负担同时改善早产儿的眼部护理，预防儿童失明。实现这些目标的具体目标是：目标#1：开发和评估最早视网膜的替代增强深度预测学习在 34 周 PMA 之前拍摄的图像集和人口因素用于预测 RW-ROP。精准风险通过早期预测 RW-ROP 进行分层将有助于识别高危婴儿，以便进行密切随访眼科医生及早发现并及时治疗ROP，以及目前正在接受治疗的低危婴儿减少不必要的身体压力的视网膜检查，以减少 ROP 检查的频率。目标#2：对累积的视网膜图像实施替代增强深度预测学习随着时间的推移动态预测 RW-ROP。深度预测ROP未来进程的动态纵向累积的视网膜图像的学习将有助于优化 ROP 检查的时间表眼科医生，从而减轻婴儿和眼科医生的 ROP 检查负担。该项目的成功完成将为早期视网膜图像分析算法带来新的成果。识别高危婴儿进行密切随访并优化 ROP 检查时间表，这将导致及早发现并及时治疗 ROP，同时最大限度地减少 ROP 检查次数。这项研究是其对早产儿 ROP 护理的全球影响具有高度可行性和潜在变革性。