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

利用眼底照片客观量化神经损伤，用于青光眼筛查、诊断和监测

基本信息

批准号：
10225458
负责人：
Felipe Medeiros
金额：
$ 19.52万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10225458
关键词：
Agreement Artificial Intelligence Blindness Clinical Trials Computerized Medical Record Consumption Data Data Set Databases Development Diabetic Retinopathy Diagnosis Diagnostic Disease Early Diagnosis Exhibits Eye Eye diseases Fundus Fundus photography Future Glaucoma Human Imaging technology India Investigation Label Latino Los Angeles Manuals Measurement Medical Methods Modeling Monitor Names Nature Ocular Hypertension Optical Coherence Tomography Output Patients Performance Population Study Race Reference Standards Registries Reproducibility Risk Science Screening procedure Structure Surveys Testing Thick Time Training Validation Visual impairment algorithm training clinical care convolutional neural network cost cost effective deep learning deep learning algorithm deep neural network flexibility hypertension treatment intelligent algorithm interest large datasets learning network longitudinal database novel optic nerve disorder point of care population based predictive modeling programs racial diversity relating to nervous system retinal nerve fiber layer screening teleophthalmology time use tool

项目摘要

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.

项目摘要青光眼是一种进行性视神经病变，是世界上不可逆性失明的主要原因。为疾病在晚期之前基本上没有症状，迫切需要开发负担得起的在视力损害发生之前进行筛查的方法。虽然先进的成像技术，由于光谱域光学相干断层扫描（SDOCT）可以提供高度可重复和准确的昏迷损害的定量评估，它们在广泛筛查或非专门筛查中的应用由于成本高和运营商的要求，这种设置不可行。眼底照相是一种低成本的替代方案已成功地用于远程眼科项目。然而，主观的人类评分青光眼眼底照片的可重复性差，高度不准确，因为分级往往在很大程度上过度或低估伤害。我们提出了一个新的范例，通过训练一个深层次的学习（DL）卷积神经网络，以提供神经损伤量的定量估计眼底照片在我们的机器对机器（M2M）方法中，我们训练了一个DL网络来分析眼底照片，并预测由SDOCT提供的青光眼损伤的定量测量，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预测与原始SDOCT观测具有非常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分级者，其可潜在地用于远程眼科学和非眼科学中的筛查、诊断和监测。专门的护理点设置。在这项提案中，我们的目标是完善和验证M2M模型，来自基于人群的研究、电子病历和临床试验数据的大型数据集。我们的中央假设M2M方法在筛选中比主观的人类分级更准确，诊断、预测和检测纵向损伤。在目标1中，我们将研究性能使用来自6项基于人群的研究的大型数据集，使用M2M模型筛选昏迷性损伤：蓝山眼科研究、洛杉矶拉丁裔眼科研究、特马眼科调查、北京眼科研究、印度中部眼科和医学研究和乌拉尔眼科和医学研究，将提供25，000多名受试者的数据，不同的种族群体。在目标2中，我们将研究M2M模型预测未来发展的能力。使用来自高眼压治疗研究（OHTS）的数据对可疑青光眼患者的眼睛进行评估。在目标3中，我们将研究M2M模型在使用来自以下研究的数据检测随时间推移的昏迷进展中的能力：杜克青光眼登记处是一个大型的青光眼纵向结构和功能数据数据库， 25，000名患者随时间进行随访。如果成功，该提案将导致一个有效的，廉价的，广泛的适用于青光眼筛查、早期诊断和监测的工具，可在基于人群的环境以及非专业护理点环境。

项目成果

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Improved Prediction of Perimetric Loss in Glaucomatous Eyes Using Latent Class Mixed Modeling.

使用潜在类混合建模改进青光眼眼视野损失的预测。

DOI：
10.1016/j.ogla.2023.05.003
发表时间：
2023
期刊：
Ophthalmology. Glaucoma
影响因子：
0
作者：
Swaminathan,SwarupS;Jammal,AlessandroA;Rao,JSunil;Medeiros,FelipeA
通讯作者：
Medeiros,FelipeA

Comparison of 10-2 and 24-2 Perimetry to Diagnose Glaucoma Using OCT as an Independent Reference Standard.

使用 OCT 作为独立参考标准比较 10-2 和 24-2 视野检查诊断青光眼。

DOI：
10.1016/j.ogla.2022.08.017
发表时间：
2023
期刊：
Ophthalmology. Glaucoma
影响因子：
0
作者：
Onyekaba,Ndidi-AmakaE;Estrela,Tais;Naithani,Rizul;McCarthy,KayneM;Jammal,AlessandroA;Medeiros,FelipeA
通讯作者：
Medeiros,FelipeA

Validation of Rates of Mean Deviation Change as Clinically Relevant End Points for Glaucoma Progression.

验证平均偏差变化率作为青光眼进展的临床相关终点。