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转失明的主要原因，对退伍军人的影响不成比例。虽然经常取得进展 慢慢地，青光眼也可以快速发展，特别是考虑到标准视野(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是具有挑战性的 治疗计划。退伍军人可能会经历永久性的视力丧失(以及相应的视力相关质量 生命)，同时等待后续测试显示室颤消失进展(并因此指示治疗的改变 是必需的)。结构光学相干断层扫描(OCT)测量，如黄斑厚度 神经节细胞层(GCL)、视网膜神经纤维层(RNFL)和视盘形态也可以帮助 监控进度。然而，现有的评估青光眼进展的全球参数的临床应用可能 对局灶性缺陷的恶化不敏感。也不知道不同的空间发展模式是如何 影响生活质量。临床上还需要一种简单易用的方法来更准确地估计 未来的进展和相应的生活质量措施。我们将使用一种特定类型的深度学习 一种称为深度变分自动编码器(VAE)的方法，以提供一种新的标准化且敏感的方法 监测青光眼的进展，堪比青光眼专家。我们的具体目标如下： 1.评估基于图像的深度学习变量自动编码器(VAE)模型的使用情况 以监测患者目前青光眼的进展情况。这一目标将首先涉及培训和 为每个基于图像的感兴趣结构评估单独的深度VAE模型以及深度VAE 24-2视野阈值数据模型。一旦经过训练，每个VAE模型将允许提取 所谓的潜在变量值给出了输入图像。这些潜在变量值的监控能力 随着时间的变化将(在独立的测试集中)与标准的全球和区域参数进行比较。 由于它们能够自然地捕捉全局和局部变化，潜在变量方法将 与当前的临床报告相比，能够更好地检测随着时间的推移而发生的变化。 2.评估基于图像的深度学习变量自动编码器(VAE)模型的使用情况 来预测患者未来青光眼的进展。在这一目标中，我们将首先制定一种方法 用于基于从先前时间的学习来预测结构/功能的未来潜变量表示 一系列的值。一旦确定，未来的潜在价值将被映射回其原始 使用VAE中训练有素的“解码器”部分的结构/功能表示。这样的方法将 为临床医生提供对未来结构的视觉空间表示和 功能轨迹，以优化早期治疗决策。 3.评估来自新的双眼VAE模型的潜在变量与视觉生活质量的关系 措施。在这个目标中，我们将首先开发一个额外的VAE模型来考虑双目视觉 (患者睁开双眼看到的)，然后将每个模型的潜在因素与质量联系起来 生活以一种横截面的方式进行衡量。我们假设双目VAE模型的结构和 功能将比目前的方法更能预测视觉生活质量的测量，有助于 优先安排和指导治疗。 这些目标的成功完成预计将对帮助退伍军人青光眼产生积极影响 患者在疾病早期避免永久性视力丧失，并保持与视力相关的生活质量。