Learning-based 3D modeling of AMD to assess disease progression and response to treatment

基于学习的 AMD 3D 建模，用于评估疾病进展和治疗反应

• 批准号: 10592517
• 负责人: Majda Hadziahmetovic
• 金额: $ 43.38万
• 依托单位: DUKE UNIVERSITY
• 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/10592517
• 关键词: 3-Dimensional; Affect; Age related macular degeneration; Angiography; Area; Blindness; Classification; Computer Models; Computing Methodologies; Data; Data Set; Developed Countries; Development; Diagnostic; Dimensions; Disease; Disease Progression; Drusen; Dryness; Early treatment; Elderly; Evolution; Fundus; Goals; Human; Image; Individual; Institution; Label; Learning; Maps; Medical Imaging; Methods; Modality; Modeling; Morphology; Multimedia; Natural History; Network-based; Neural Network Simulation; Nonexudative age-related macular degeneration; Optical Coherence Tomography; Outcome; Output; Pathologic; Pathology; Patient-Focused Outcomes; Patients; Recording of previous events; Records; Registries; Research; Retina; Retinal Diseases; Risk; Robotics; Scanning; Schedule; Slice; Specific qualifier value; Techniques; Testing; Thick; Time; Tissues; Training; Visit; Visual; Western World; Work; aging population; automated image analysis; biomarker identification; clinical database; clinical practice; convolutional neural network; deep learning; deep learning model; deep neural network; disease classification; disease natural history; feature extraction; follow-up; high reward; high risk; imaging biomarker; imaging modality; improved; inclusion criteria; innovation; insight; learning strategy; long short term memory; longitudinal dataset; neural network; novel; patient stratification; prediction algorithm; reconstruction; retinal imaging; spatiotemporal; three-dimensional modeling; transfer learning; treatment response

Abstract: Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss in developed countries. With the growing aging population, the burden of AMD will continue to rise. Despite significant research efforts, we still have limited understanding of what drives the disease progression and why some patients advance to final stages, not respond to available treatments, and have profound vision loss. To improve visual outcomes for these patients, it is critical to develop methods that can identify individuals with early, asymptomatic changes who are at the risk of developing advanced forms of disease. Consequently, by employing recent developments in deep learning and automated image and video analysis, the goal of this project is to develop computational methods and models for automated image analysis and biomarker identification to improve our understanding of AMD and help us to predict its course. To achieve this, we will develop Deep Neural Network (DNN)-based models to identify useful image biomarkers for AMD from different imaging modalities commonly used in clinical practice – Optical-Coherence Tomography (OCT), Fundus Autofluorescence (FAF), and Fundus Angiography (FA). We hypothesize that adding FAF and FA images will significantly improve localization and classification of pathology; thus, facilitate a better understanding of the disease's natural history and response to treatment. To execute this innovative, high-risk/high-reward project, we will use the data from The Duke Ophthalmic Registry, the largest single-institution clinical database for ophthalmic records in the world. We have access to a downloaded and expert-annotated large image dataset numbering over 6400 patients that meet our study's inclusion criteria – progression of AMD from early to advanced stages (dry and wet). For each patient, OCT, FAF, and FA images were captured during a single visit or within specified follow-up intervals (up to ten years or more). We will develop learning-based methods and models for one-time-point image fusion and analysis; specifically, DNN models for AMD classification (early vs. intermediate vs. advanced dry AMD vs. wet AMD), segmentation of the diseased tissue, and 3D reconstruction of the affected area. Finally, using the longitudinal datasets, we will develop deep-learning methods and models to analyze disease evolution over time. The critical insight and novelty are to consider longitudinal datasets as sequences of 2D and 3D pathology models, which allows for the use of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM)-based neural networks, which were recently introduced and employed for time-dependent video frame prediction in the context of autonomous vehicles and human action recognition. The proposed research will result in early AMD patient stratification, which will allow for individually tailored follow-up schedules and lead to timely treatments; this is very important since the early treatment of wet AMD results in better visual outcomes.

抽象的： 年龄相关性黄斑变性（AMD）是发达国家不可逆视力丧失的主要原因。 随着人口老龄化的不断加剧，AMD 的负担仍将继续增加，尽管进行了大量的研究工作。 我们对疾病进展的驱动因素以及为什么一些患者会进展为疾病的了解仍然有限 最后阶段，对可用的治疗没有反应，并有严重的视力丧失。 对于这些患者，开发能够识别早期无症状变化的方法至关重要 通过采用最新进展，对处于晚期疾病风险的人群进行测试。 在学习深度和自动化图像和视频分析时，该项目的目标是开发计算能力 自动图像分析和生物标志物识别的方法和模型，以提高我们的理解 AMD 并帮助我们预测其进程 为了实现这一目标，我们将开发基于深度神经网络 (DNN) 的技术。 从临床常用的不同成像方式中识别 AMD 有用的图像生物标志物的模型 实践 – 光学相干断层扫描 (OCT)、眼底自发荧光 (FAF) 和眼底血管造影 (FA) 我们追求添加 FAF 和 FA 图像将显着改善定位和分类。 病理学；从而有助于更好地了解疾病的自然史和对治疗的反应。 为了执行这个创新、高风险/高回报的项目，我们将使用杜克眼科的数据 我们可以访问世界上最大的单一机构眼科记录临床数据库。 下载并经过专家注释的大型图像数据集，包含超过 6400 名患者，符合我们的研究要求 纳入标准 – AMD 从早期到晚期的进展（干性和湿性）对于每位患者，OCT， FAF 和 FA 图像是在单次访问期间或在指定的随访间隔内（最长十年或 我们将开发基于学习的一次性点图像融合和分析的方法和模型； 具体来说，用于 AMD 分类的 DNN 模型（早期、中期、高级干性 AMD 与湿性 AMD）， 病变组织的分割，以及受影响区域的 3D 重建，最后使用纵向。 数据集，我们将开发深度学习方法和模型来分析疾病随时间的演变。 洞察力和新颖性是将纵向数据集视为 2D 和 3D 病理模型的序列，这 允许使用基于卷积神经网络 (CNN) 和长短期记忆 (LSTM) 的神经网络 网络，最近被引入并用于上下文中的时间相关视频帧预测 拟议的研究将导致早期 AMD 患者。 分层，这将允许单独定制后续时间表并导致及时治疗； 非常重要，因为湿性 AMD 的早期治疗可以带来更好的视力结果。