Robust AI to develop risk models in retinopathy of prematurity using deep learning

ROP是一种影响早产儿的视网膜新生血管疾病，也是儿童失明的主要原因 国际吧已知的临床风险因素包括早产、低出生体重和使用辅助供氧 但需要改进的风险模型来确定婴儿是否进展到需要治疗的疾病， 失明深度学习技术已被用于成功识别多个疾病中的“加号”疾病， 机构队列，并提供疾病严重程度的连续测量。深度的一个主要局限性 然而，学习需要大量精心策划的数据集。其他限制包括过拟合 和“脆性”，这可能会导致模型性能下降的外部数据。然而， 构建和托管这些大型中央存储库的障碍，这些存储库需要多机构数据， 深度学习，包括对数据共享、法规成本、患者隐私和知识产权的担忧 财产在这个项目中，我们的目标是展示分布式/联合深度学习方法的实用性。 其中数据位于机构内，但模型参数与中央服务器共享。 然而，阻碍这项研究的一个主要挑战是需要大量的标记图像 数据来训练深度学习模型。创建图像数据的大型公共集中式集合的努力是 受到数据共享障碍、图像去识别成本、患者隐私问题和控制的阻碍 如何使用数据。当前的深度学习模型正在使用来自一个或几个 机构受到潜在的过度拟合和较差的普遍性的限制。而不是集中或共享患者 图像，我们的目标是通过计算将深度学习模型的训练分布在机构之间。 在本地图像数据上执行。 具体来说，我们寻求建立强大的风险模型来预测需要治疗的疾病。两个大型队列 将用于验证假设，即使用分布式学习的风险模型的性能 接近集中托管的模式，比基于单一机构数据集的模型更强大。 赠款管理 更新04.01.2019 JBou

ROP is a retinal neovascular disease affecting preterm infants, and is a leading cause of childhood blindness worldwide. Known clinical risk factors include preterm birth, low birthweight and use of supplemental oxygen but improved risk models are needed to identify infants that progress to treatment requiring disease and blindness. Deep learning techniques have been used to successfully identify “plus” disease in multi- institutional cohorts and to provide a continuous measure of disease severity. A major limitation of deep learning, however, is the need for large amounts of well curated datasets. Other limitations include overfitting and “brittleness” that can cause model performance to drop on external data. There are, however, numerous barriers to building and hosting these large central repositories with multi-institutional data required for robust deep learning including concerns about data sharing, regulations costs, patient privacy and intellectual property. In this project, we aim to demonstrate the utility of distributed/federated deep learning approaches where the data are located within institutions, but model parameters are shared with a central server. A major challenge thwarting this research, however, is the requirement for large quantities of labeled image data to train deep learning models. Efforts to create large public centralized collections of image data are hindered by barriers to data sharing, costs of image de-identification, patient privacy concerns, and control over how data are used. Current deep learning models that are being built using data from one or a few institutions are limited by potential overfitting and poor generalizability. Instead of centralizing or sharing patient images, we aim to distribute the training of deep learning models across institutions with computations performed on their local image data. Specifically, we seek to build robust risk models for predicting treatment requiring disease. Two large cohorts will be used to validate the hypothesis that the performance of the risk models using distributed learning approaches that of centrally hosted and is more robust than models built on single institutional datasets. Grants Admin Updated 04.01.2019 JBou