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.

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 challenged to determine those individuals need 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 worry of focal defects. It is also not known how differentiating 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 monitor glaucomatous progression, comparable to a glaucoma expert.我们的具体目的如下： 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 clinical 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 措施。 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.