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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要动机和假设：通过圆形扫描测量的周围乳头状 RNFL 厚度 (cpRNFLT) 为常规用于青光眼诊断。精确的 cpRNFLT 规范对于评估 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) 设备只能调整 cpRNFLT 年龄标准。先前的研究试图通过以下方式调整视网膜解剖学的 cpRNFLT 规范：手动描绘的特征，例如血管位置和椎间盘中央凹角度或标准临床指标例如扫描直径和眼轴长度，而手动特征提取费力且标准临床指标不足以代表复杂的视网膜解剖变化。我们假设我们可以利用人工智能 (AI) 建模 (1) 通过自动调整来改进 cpRNFLT 规范由视网膜成像数据编码的视网膜解剖结构，可用于 (2) 改善青光眼诊断。目标 1：开发基于人工智能的模型，根据个体视网膜解剖结构个性化 cpRNFLT 规范。来自莱比锡基于人群的研究的健康受试者数据将用于开发 Lasso 线性回归和深度学习模型来调整以内限为代表的视网膜解剖学的逐点 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随后进行均匀流形逼近和投影，从 ILM 中提取视网膜解剖特征地图和 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缺失的成像值。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。来自马萨诸塞州眼耳科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。这逐点 cpRNFLT 偏差百分位数将用于预测伴随的 VF。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 检验比较使用通过我们的个性化 cpRNFLT 规范标准化的 cpRNFLT 偏差百分位数是否可以更好与仅调整年龄、性别和扫描直径的标准 cpRNFLT 规范相比，预测 VF。对于深度学习模型，将开发由注意力单元增强的一维卷积神经网络。主要可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 cpRNFLT 规范：使用尖端人工智能的视网膜成像数据自动调整个体视网膜解剖结构技术。该项目的成功可能会对改善青光眼患者的临床护理产生巨大影响。