Personalizing Circumpapillary Retinal Nerve Fiber Layer Thickness Norms for Glaucoma

个性化青光眼环视乳头视网膜神经纤维层厚度标准

• 批准号: 10728042
• 负责人: Mengyu Wang
• 金额: $ 55.7万
• 依托单位: SCHEPENS EYE RESEARCH INSTITUTE
• 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 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10728042
• 关键词: Ablation; Age; Age related macular degeneration; Anatomy; Artificial Intelligence; Attention; Blindness; Blood Vessels; Caring; Clinical; Complex; Custom; Data; Data Set; Devices; Diabetic Retinopathy; Diagnosis; Diameter; Ear; Exclusion; Eye; Fundus; Gender; Germany; Glaucoma; Image; Individual; Individual Adjustment; Inner Limiting Membrane; Lasers; Lasso; Length; Linear Regressions; Location; Manuals; Maps; Massachusetts; Measurement; Measures; Modeling; Monitor; Motivation; Neural Network Simulation; Ophthalmoscopy; Optic Disk; Optical Coherence Tomography; Patients; Performance; Population Study; Principal Component Analysis; Public Health; Retina; Scanning; Specialist; Structure; Surface; Technology; Testing; Thick; Torsion; Training; Validation; Variant; Visual Fields; clinical care; clinically relevant; convolutional neural network; deep learning model; design; feature extraction; fovea centralis; functional loss; fundus imaging; high dimensionality; improved; innovation; novel; retinal imaging; retinal nerve fiber layer; success

Project Summary Motivation and Hypotheses: The circumpapillary RNFL thickness (cpRNFLT) measured by circle scan is routinely used for glaucoma diagnosis. Precise cpRNFLT norms are important for assessing cpRNFLT abnormalities, while current optical coherence tomography (OCT) devices used in glaucoma care only adjust the cpRNFLT norms for age. Prior studies attempted to adjust cpRNFLT norms for retinal anatomy either by manually delineated features such as blood vessel location and disc-fovea angle or standard clinical metrics such as scan diameter and axial length, while manual feature extraction is laborious and standard clinical metrics are insufficient to represent the complex retinal anatomical variation. We hypothesize that we can leverage artificial intelligence (AI) modeling to (1) improve cpRNFLT norms by automatically adjusting for retinal anatomy encoded by retinal imaging data, which can be then used to (2) improve glaucoma diagnosis. Aim 1: Developing AI-based models to personalize cpRNFLT norms with individual retinal anatomy. Healthy subject data from the Leipzig population-based study will be used to develop Lasso linear regression and deep learning models to adjust pointwise cpRNFLT norms for retinal anatomy represented by inner limiting membrane (ILM) maps and scanning laser ophthalmoscopy (SLO) fundus images. 60%, 20% and 20% of the entire dataset will be used for training, validation and testing, respectively. The cpRNFLT norm accuracy will be measured by mean absolute error and R2. For the Lasso model, we will apply principal component analysis followed by uniform manifold approximation and projection to extract retinal anatomical features from the ILM map and SLO fundus image. For the deep learning model, we will use both the pre-trained deep learning model ResNet-50 and a custom designed convolutional neural network ignoring missing imaging values. Aim 2: Clinical relevance validation for the personalized cpRNFLT norms based on individual retinal anatomy. Glaucoma patient data from Massachusetts Eye and Ear will be used to demonstrate the clinical relevance of our personalized cpRNFLT norms with Lasso linear regression and deep learning models. The pointwise cpRNFLT deviation percentiles will be used to predict accompanying VFs. Mean absolute error and R2 on the testing subset will be used to evaluate model performance. Paired t-test will be performed to compare if using cpRNFLT deviation percentiles normalized by our personalized cpRNFLT norms can better predict VFs compared with by the standard cpRNFLT norms only adjusting for age, gender and scan diameter. For the deep learning model, a 1D convolutional neural network enhanced by attention units will be developed. Main Deliverables and Public Health Impacts: This project will construct personalized cpRNFLT norms by automatically adjusting for individual retinal anatomy using retinal imaging data with cutting edge AI technology. The success of this project may have a great impact to improve clinical care for glaucoma patients.

项目摘要 动机和假设：由圆扫描测量的圆形RNFL厚度（CPRNFLT）为 通常用于青光眼诊断。精确的CPRNFLT规范对于评估CPRNFLT很重要 异常，而在青光眼护理中使用的当前光学相干断层扫描（OCT）设备仅调整 年龄的CPRNFLT规范。先前的研究试图通过 手动划定的特征，例如血管位置和圆盘角度或标准临床指标 例如扫描直径和轴向长度，而手动特征提取是费力的和标准的临床 指标不足以表示复杂的视网膜解剖变异。我们假设我们可以 利用人工智能（AI）建模到（1）通过自动调整来改善CPRNFLT规范 视网膜成像数据编码的视网膜解剖学，然后可以用来（2）改善青光眼诊断。 AIM 1：开发基于AI的模型，以个性化视网膜解剖结构来个性化CPRNFLT规范。 基于莱比锡的研究的健康受试者数据将用于发展套索线性回归 和深度学习模型，以调整视网膜解剖结构的尖锐CPRNFLT规范，以内部限制为代表 膜（ILM）图和扫描激光眼镜检查（SLO）眼镜图像。 60％，20％和20％ 整个数据集将分别用于培训，验证和测试。 CPRNFLT规范精度将是 通过平均绝对误差和R2测量。对于Lasso模型，我们将应用主成分分析 然后进行均匀的歧管近似和投影，以从ILM提取视网膜解剖特征 地图和SLO眼睛图像。对于深度学习模型，我们将同时使用预训练的深度学习 模型Resnet-50和一个定制设计的卷积神经网络忽略了缺少的成像值。 目标2：基于个体视网膜的个性化CPRNFLT规范的临床相关性验证 解剖学。来自马萨诸塞州眼睛和耳朵的青光眼患者数据将用于证明临床 我们个性化CPRNFLT规范与套索线性回归和深度学习模型的相关性。这 CPRNFLT偏差百分位数将用于预测随附的VF。平均绝对错误和 测试子集上的R2将用于评估模型性能。配对t检验将进行 比较如果使用我们个性化CPRNFLT规范标准化的CPRNFLT偏差百分比可以更好 与仅根据年龄，性别和扫描直径调整的标准CPRNFLT规范相比，预测VF。 对于深度学习模型，将开发一个1D卷积神经网络通过注意力单位增强。 主要可交付成果和公共卫生影响：该项目将通过 使用带有尖端AI的视网膜成像数据自动调整单个视网膜解剖结构 技术。该项目的成功可能会对改善青光眼患者的临床护理产生重大影响。