Early Detection of Progressive Visual Loss in Glaucoma Using Deep Learning

使用深度学习早期检测青光眼进行性视力丧失

• 批准号: 10424899
• 负责人: MONA K. GARVIN
• 金额: --
• 依托单位: IOWA CITY VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10424899
• 关键词: Affect; Ambulatory Care Facilities; Area; Artificial Intelligence; Back; Binocular Vision; Blindness; Clinic; Clinical; Clinical Data; Communities; Computers; Data; Defect; Disease; Early Diagnosis; Early treatment; Eye; Future; Ganglion Cell Layer; Glaucoma; Global Change; Goals; Image; Individual; Inner Plexiform Layer; Intervention; Learning; Maps; Measures; Methods; Modeling; Monitor; Morphology; Optic Disk; Optic Nerve; Optical Coherence Tomography; Outcome; Outcome Measure; Output; Patients; Pattern; Quality of life; Reporting; Retina; Series; Severities; Standardization; Structure; Testing; Thick; Time; Training; Variant; Veterans; Vision; Visual; Visual Fields; Visuospatial; aggressive therapy; autoencoder; base; clinical practice; deep learning; denoising; diagnostic value; expectation; experience; field study; ganglion cell; instrument; interest; macula; novel; novel strategies; optic cup; preservation; retinal nerve fiber layer; success; treatment planning; trend analysis; visual map

Glaucoma, a leading cause of irreversible blindness, disproportionately affects veterans. While often progressing slowly, glaucoma can also progress rapidly, and especially given the variability of standard visual-field (VF) tests to monitor progression, it currently can be challenging to determine those individuals needing a more aggressive treatment plan. Veterans may experience permanent loss of vision (and corresponding vision-related quality of life) while waiting for subsequent tests to show VF loss progression (and thus indicating a change in treatment is needed). Structural optical coherence tomography (OCT) measures, such as the thickness of the macular ganglion cell layer (GCL), retinal nerve fiber layer (RNFL) and optic disc morphology can also be used to help monitor progression. However, existing clinical use of global parameters to assess glaucoma progression may be insensitive to worsening of focal defects. It is also not known how differing spatial patterns of progression affects quality of life. There is an unmet clinical need for simple-to-use approaches to more accurately estimate future progression and corresponding quality-of-life measures. We will use a specific type of deep-learning approach, called deep variational autoencoders (VAEs) to provide a novel standardized and sensitive approach to monitoring glaucomatous progression, comparable to a glaucoma expert. Our specific aims are as follows: 1. Evaluate how well image-based deep-learning variational autoencoder (VAE) models can be used to monitor a patient’s current glaucomatous progression. This aim will first involve training and evaluating a separate deep VAE model for each image-based structure of interest as well as a deep VAE model for 24-2 visual field threshold data. Once trained, each VAE model will allow for the extraction of the so-called latent variable values given the input image. The ability of these latent variable values to monitor change over time will be compared (in an independent test set) to standard global and regional parameters. Because of their ability to naturally capture both global and local changes, the latent-variable approach will be able to better detect changes over time compared to current clinical reports. 2. Evaluate how well image-based deep-learning variational autoencoder (VAE) models can be used to predict a patient’s future glaucomatous progression. In this aim, we will first develop an approach for predicting future latent-variable representations of structure/function based on learning from a prior time series of values. Once determined, future latent values will be mapped back to their original structure/function representations using the trained “decoder” part of the VAE. Such an approach will provide a clear advantage for a clinician in having visual spatial representations of future structure and function trajectories to optimize early treatment decisions. 3. Evaluate how latent variables from a novel binocular VAE model relate to visual quality-of-life measures. In this aim, we will first develop an additional VAE model to take into account binocular vision (what the patient sees with both eyes open) and then relate latent factors from each model to quality-of- life measures in a cross-sectional fashion. We hypothesize that binocular VAE models of structure and function will be more predictive of visual quality-of-life measures than current methods, helping to prioritize and guide treatment. Successful completion of these aims is expected to have positive impact to help veteran glaucomatous patients avoid permanent vision loss at an early disease stage and maintain vision-related quality of life.

青光眼是导致不可逆性失明的主要原因，它对退伍军人的影响尤为严重，而且病情往往会恶化。 青光眼也可能进展缓慢，尤其是考虑到标准视野 (VF) 测试的可变性 为了监测进展，目前确定哪些人需要更积极的治疗可能具有挑战性 退伍军人可能会出现永久性视力丧失（以及相应的视力相关质量丧失）。 生命），同时等待后续测试显示室颤丧失进展（从而表明治疗的改变） 需要结构光学相干断层扫描 (OCT) 测量，例如黄斑厚度。 神经节细胞层 (GCL)、视网膜神经纤维层 (RNFL) 和视盘形态也可用于帮助 然而，现有的临床使用全局参数来评估青光眼进展可能会发生。 对病灶缺陷的恶化不敏感也不知道进展的不同空间模式如何。 影响生活质量的临床需求尚未得到满足，需要简单易用的方法来更准确地进行估计。 我们将使用特定类型的深度学习来衡量未来的进展和相应的生活质量。 方法，称为深度变分自动编码器（VAE），提供一种新颖的标准化和敏感方法 与青光眼专家一样监测青光眼进展，我们的具体目标如下： 1. 评估基于图像的深度学习变分自动编码器 (VAE) 模型的使用效果 监测患者目前的青光眼进展情况。这一目标首先涉及培训和治疗。 评估每个基于图像的感兴趣结构的单独深度 VAE 模型以及深度 VAE 24-2 视野阈值数据的模型经过训练后，每个 VAE 模型将允许提取 所谓的潜在变量值是给定输入图像的能力。 随时间的变化将（在独立测试集中）与标准全球和区域参数进行比较。 由于潜变量方法能够自然地捕获全局和局部变化，因此 与当前的临床报告相比，能够更好地检测随时间的变化。 2. 评估基于图像的深度学习变分自动编码器 (VAE) 模型的使用效果 为了预测患者未来的青光眼进展，我们将首先开发一种方法。 用于基于先前时间的学习来预测结构/功能的未来潜变量表示 一旦确定，未来的潜在值将被映射回其原始值。 使用 VAE 的经过训练的“解码器”部分来表示结构/功能。 为临床医生提供未来结构的视觉空间表征和 功能轨迹以优化早期治疗决策。 3. 评估新型双眼 VAE 模型中的潜在变量与视觉生活质量的关系 为此，我们将首先开发一个额外的 VAE 模型来考虑双眼视觉。 （患者睁开双眼看到的东西），然后将每个模型的潜在因素与质量相关联 我们以横截面方式捕获了双目 VAE 模型的结构和特征。 与现有方法相比，该功能将更能预测视觉生活质量指标，从而有助于 优先考虑并指导治疗。 成功完成这些目标预计将对帮助患有青光眼的退伍军人产生积极影响 患者在疾病早期避免视力丧失，并保持与视力相关的永久生活质量。