Personalizing Circumpapillary Retinal Nerve Fiber Layer Thickness Norms for Glaucoma

项目摘要 动机和假设：圆周扫描测量的乳头状RNFL厚度(CpRNFLT)为 常规用于青光眼诊断。精确的cpRNFLT标准对于评估cpRNFLT很重要 异常，而目前用于青光眼护理的光学相干断层扫描(OCT)设备仅调整 CpRNFLT的年龄常模。先前的研究试图通过以下方式调整视网膜解剖的cpRNFLT标准 手动描绘的特征，如血管位置和盘中心凹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且标准的临床 测量指标不足以代表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(1)通过自动调整以下各项来改进cpRNFLT规范 视网膜影像数据编码的视网膜解剖结构，可用于(2)提高青光眼诊断水平。 目的1：开发基于人工智能的模型，以个性化视网膜解剖来个性化cpRNFLT规范。 来自莱比锡基于人群的研究的健康受试者数据将被用于开发Lasso线性回归 和深度学习模型，以调整由内限表示的视网膜解剖的逐点cpRNFLT规范 膜(ILM)图和扫描激光眼底镜(SLO)眼底图像。60%、20%和20%的 整个数据集将分别用于训练、验证和测试。CpRNFLT规范精度将为 用平均绝对误差和R2来测量。对于套索模型，我们将应用主成分分析 然后用均匀流形逼近和投影从ILM中提取视网膜解剖特征 MAP和SLO眼底图像。对于深度学习模型，我们将使用预先训练的深度学习 型号ResNet-50和定制设计的卷积神经网络，可忽略丢失的成像值。 目的2：基于个体视网膜的个性化cpRNFLT常模的临床相关性验证 解剖学。来自马萨诸塞州眼科和耳科的青光眼患者数据将被用来证明临床 我们的个性化cpRNFLT常模与Lasso线性回归和深度学习模型的相关性。这个 点状cpRNFLT偏差百分位数将用于预测伴随的VFS。平均绝对误差和 测试子集上的R2将用于评估模型性能。将进行配对t检验以 比较使用我们的个性化cpRNFLT规范归一化的cpRNFLT偏差百分位数是否可以更好地 与仅根据年龄、性别和扫描直径调整的标准cpRNFLT标准相比，预测VFS。 对于深度学习模型，将开发一种由注意单元增强的一维卷积神经网络。 主要交付成果和公共卫生影响：该项目将通过以下方式构建个性化的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 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很大影响。

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.