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

• 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
• 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

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.

代理增强深度预测学习 早产儿视网膜病变 摘要 该提案旨在开发新的替代增强深度预测学习算法，用于预测 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， 在美国和世界范围内，有限的ROP专家是可用的，但ROP的早期检测， 治疗在预防儿童失明方面对婴儿有巨大的临床益处。使用独特的， 从1257例早产儿的纵向观察研究中收集了7905个图像集的大规模数据集 来自北美13个中心的婴儿，我们计划开发，验证和评估新的分析算法 这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）开发新的方法进行风险分层， 替代增强了对最早期视网膜图像（月经后34周之前）的深度预测学习 年龄[PMA]）和最重要的ROP风险因素（出生体重，胎龄），用于早期预测 糖尿病保证的ROP（RW-ROP），定义为加疾病、I区中的ROP或3期ROP或更高;以及（2） 通过对累积的数据的替代增强深度预测学习来优化ROP调度， 视网膜纵向图像用于RW-ROP的动态预测。我们的方法，经过适当的验证， 未来的前瞻性研究，可能作为一个有用的工具，ROP的风险分层和优化调度 的ROP检查，这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睛护理，以预防儿童失明。 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病变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统计学因素来预测RW-ROP。准确的风险 通过早期预测RW-ROP进行分层将有助于识别高危婴儿， 早期发现和及时治疗ROP的眼科医生，以及目前正在接受 不必要的身体紧张的视网膜检查，以减少频繁的ROP检查。 目标#2：实现累积视网膜图像的替代增强深度预测学习 以动态地预测RW-ROP。用深度法动态预测机械钻速的未来进程 学习纵向累积的视网膜图像将有助于优化ROP检查的时间表， 因此，可以为婴幼儿和眼科医生减轻ROP检查的负担。 该项目的成功完成将导致早期视网膜图像的新分析算法， 识别高危婴儿进行密切随访和优化ROP检查时间表，这将导致 以便在最小化ROP检查次数的同时更早地检测和及时地治疗ROP。本研究是 这是一项非常可行的研究，对早产儿ROP护理的全球影响具有潜在的变革性。