Objective Quantification of Neural Damage for Screening, Diagnosis and Monitoring of Glaucoma with Fundus Photographs

项目总结 青光眼是一种进行性视神经病变，是世界上导致不可逆性失明的主要原因。作为 疾病直到晚期仍基本没有症状，迫切需要制定出负担得起的 视力损害发生前的筛查方法。尽管先进的成像技术，如 AS谱域光学相干层析成像(SDOCT)可以提供高度重复性和准确性 青光眼损害的定量评估及其在广泛筛查或非专科筛查中的应用 考虑到高成本和操作员要求，设置是不可行的。眼底照相是一种低成本的 已在远程眼科项目中成功使用的替代方案。然而，主观的人类评分 青光眼眼底照片的可重复性很差，而且非常不准确，因为分级往往在很大程度上过度或 低估了损失。我们提出了一种新的评估青光眼损害的方法，即通过对青光眼患者进行深度训练。 学习(DL)卷积神经网络提供对神经损伤量的定量估计 从眼底照片来看。在我们的机器对机器(M2M)方法中，我们训练了一个DL网络来分析 由SDOCT提供的眼底照片和预测青光眼损害的定量测量，例如 视网膜神经纤维层(RNFL)厚度和神经视网膜边缘测量。我们的初步结果显示 M2M预报与SDOCT原始观测有很高的相关性和一致性。这 提供了一种客观的方法来量化眼底照片中的神经损伤，而不需要人类评分员， 它可能被用于远程眼科和非眼科的筛查、诊断和监测 专门的护理点设置。在本提案中，我们旨在完善和验证M2M模型， 来自基于人群的研究、电子医疗记录和临床试验数据的大数据集。我们的中央 假设M2M方法在筛查中将比主观人类分级更准确， 随着时间的推移，诊断、预测和检测纵向损伤。在目标1中，我们将调查性能 使用来自6个基于人群的研究的大数据集来筛选青光眼损害的M2M模型： 蓝山眼科研究，洛杉矶拉丁人眼科研究，特马眼科调查，北京眼科研究，印度中部 眼科和医学研究以及乌拉尔眼科和医学研究，将提供超过25,000名受试者的数据 不同的种族群体。在目标2中，我们将研究M2M模型对未来发展的预测能力 使用来自眼压治疗研究(OHTS)的数据对疑似患者的青光眼进行研究。在《目标3》中， 我们将使用以下数据来研究M2M模型检测青光眼进展的能力 杜克青光眼登记处，一个关于青光眼纵向结构和功能数据的大型数据库 随着时间的推移，25,000名患者进行了随访。如果成功，这项提议将带来经过验证的、廉价的和广泛的 适用于青光眼筛查、早期诊断和监测的工具，可应用于 在以人口为基础的环境中，也在非专门的护理地点环境中。

PROJECT SUMMARY Glaucoma is a progressive optic neuropathy and the leading cause of irreversible blindness in the world. As the disease remains largely asymptomatic until late stages, there is a pressing need to develop affordable approaches for screening before visual impairment occurs. Although sophisticated imaging technologies such as Spectral domain-optical coherence tomography (SDOCT) can provide highly reproducible and accurate quantitative assessment of glaucomatous damage, their application in widespread screening or non-specialized settings is unfeasible, given the high cost and operator requirements. Fundus photography is a low-cost alternative that has been used successfully in teleophthalmology programs. However, subjective human grading of fundus photos for glaucoma is poorly reproducible and highly inaccurate, as gradings tend to largely over- or underestimate damage. We propose a new paradigm for assessing glaucomatous damage by training a deep learning (DL) convolutional neural network to provide quantitative estimates of the amount of neural damage from fundus photographs. In our Machine-to-Machine (M2M) approach, we trained a DL network to analyze fundus photos and predict quantitative measurements of glaucomatous damage provided by SDOCT, such as retinal nerve fiber layer (RNFL) thickness and neuroretinal rim measurements. Our preliminary results showed that the M2M predictions have very high correlation and agreement with the original SDOCT observations. This provides an objective method to quantify neural damage in fundus photos without requiring human graders, which could potentially be used for screening, diagnoses and monitoring in teleophthalmology and non- specialized point-of-care settings. In this proposal, we aim at refining and validating the M2M model in suitable, large datasets from population-based studies, electronic medical records, and clinical trial data. Our central hypothesis is that the M2M approach will be more accurate than subjective human gradings in screening, diagnosing, predicting and detecting longitudinal damage over time. In Aim 1, we will investigate the performance of the M2M model to screen for glaucomatous damage using large datasets from 6 population-based studies: Blue Mountains Eye Study, Los Angeles Latino Eye Study, Tema Eye Survey, Beijing Eye Study, Central India Eye and Medical Study and the Ural Eye and Medical Study, which will provide data on over 25,000 subjects of diverse racial groups. In Aim 2, we will investigate the ability of the M2M model to predict future development of glaucoma in eyes of suspects using the data from the Ocular Hypertension Treatment Study (OHTS). In Aim 3, we will investigate the ability of the M2M model in detecting glaucomatous progression over time using data from the Duke Glaucoma Registry, a large database of longitudinal structure and function data in glaucoma with over 25,000 patients followed over time. If successful, this proposal will lead to a validated, inexpensive, and widely applicable tool for screening, early diagnosis and monitoring of glaucoma, that could be applied under population-based settings and also at non-specialized point-of-care settings.