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：通过环形扫描测量的视乳头周围RNFL厚度（cpRNFLT）是 常规用于青光眼诊断。精确的cpRNFLT标准对于评估cpRNFLT非常重要 异常，而目前用于青光眼护理的光学相干断层扫描（OCT）设备只能调整 年龄的cpRNFLT标准。先前的研究试图通过以下方式调整视网膜解剖结构的cpRNFLT标准： 例如血管位置和椎间盘中央凹角度或标准临床度量 例如扫描直径和轴向长度，而手动特征提取是费力和标准临床 度量不足以表示复杂的视网膜解剖学变化。我们假设我们可以 利用人工智能（AI）建模来（1）通过自动调整 由视网膜成像数据编码的视网膜解剖结构，其然后可用于（2）改进青光眼诊断。 目标1：开发基于AI的模型，以个性化cpRNFLT规范与个体视网膜解剖结构。 莱比锡人群研究的健康受试者数据将用于开发Lasso线性回归 和深度学习模型来调整由内部限制表示的视网膜解剖结构的逐点cpRNFLT规范 膜（ILM）图和扫描激光检眼镜（SLO）眼底图像。60%、20%和20% 整个数据集将分别用于训练、验证和测试。cpRNFLT范数准确度为 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 然后进行均匀流形逼近和投影，以从ILM中提取视网膜解剖特征 图和SLO眼底图像。对于深度学习模型，我们将使用预训练的深度学习 模型ResNet-50和定制设计的卷积神经网络，忽略丢失的成像值。 目的2：基于个体视网膜病变的个性化cpRNFLT规范的临床相关性验证 解剖学。来自马萨诸塞州眼耳的青光眼患者数据将用于证明临床 我们的个性化cpRNFLT规范与Lasso线性回归和深度学习模型的相关性。的 逐点cpRNFLT偏差矩阵将用于预测伴随VF。平均绝对误差和 测试子集上的R2将用于评价模型性能。将进行配对t检验， 比较使用由我们的个性化cpRNFLT规范归一化的cpRNFLT偏差矩阵是否可以更好地 与仅调整年龄、性别和扫描直径的标准cpRNFLT标准相比，预测VF。 对于深度学习模型，将开发由注意力单元增强的1D卷积神经网络。 主要指标和公共卫生影响：本项目将通过以下方式构建个性化cpRNFLT规范： 使用具有尖端AI的视网膜成像数据自动调整个体视网膜解剖结构 技术.该项目的成功可能对改善青光眼患者的临床护理产生重大影响。