Predicting the risk of glaucoma from structural, functional, and genetic factors using artificial intelligence

利用人工智能从结构、功能和遗传因素预测青光眼风险

• 批准号: 10364871
• 负责人: Siamak Yousefi
• 金额: $ 57.38万
• 依托单位: UNIVERSITY OF TENNESSEE HEALTH SCI CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10364871
• 关键词: Address; Affect; Anatomy; Artificial Intelligence; Atlases; Axon; Blindness; Caring; Clinical; Clinical Trials; Complex; Country; Cox Proportional Hazards Models; Data; Developing Countries; Development; Diagnosis; Disease; Elderly; Evaluation; Eye; Functional disorder; Fundus; Future; Genetic; Genetic Markers; Glaucoma; Growth; Hereditary Disease; High Prevalence; Image; Incidence; Individual; Lead; Machine Learning; Modality; Modeling; Nerve Degeneration; Ocular Hypertension; Older Population; Ophthalmologist; Optic Disk; Outcome; Participant; Patients; Pattern; Persons; Phenotype; Population; Prevalence; Primary Open Angle Glaucoma; Resolution; Resources; Retina; Risk; Risk Factors; Screening procedure; Side; Single Nucleotide Polymorphism; Specific qualifier value; Surrogate Endpoint; Testing; Validation; Visual Fields; Work; aging population; archetypal analysis; base; care burden; case control; clinical care; clinical practice; clinically relevant; deep learning; deep learning model; demographics; endophenotype; experience; field study; genome wide association study; high risk; hypertension treatment; improved; machine learning model; multimodality; novel; novel marker; ocular imaging; population based; predictive modeling; prevent; retinal ganglion cell degeneration; retinal nerve fiber layer; screening; sight restoration; statistical and machine learning; tool

Glaucoma is a complex neurodegenerative blinding disease that causes the degeneration of retinal ganglion cells and their axons. The prevalence of glaucoma is projected to increase by almost 50% over the next two decades as older people making up the fastest growing part of the global population. The burden of glaucoma care will therefore continue to grow, without a competing increase in the number of ophthalmologists or available resources. As a result, the required demand for glaucoma care will likely exceed capacity and resources leading to prioritizing care for those patients at highest risk of vision loss. There is no concrete evidence in support of an individual test, or group of tests, that show superiority for identifying people at-risk of developing glaucoma or those at higher risk of glaucoma progression. Glaucoma risk factors are too insensitive in identifying individuals who will likely develop glaucoma. Fundus photographs lack detailed and high-resolution information of the optic disc and surrounding retinal nerve fiber layer for glaucoma assessment and visual field tests provide surprisingly inconsistent and variable results, especially in subclinical glaucoma and in patients with more severe visual field loss (both sides of glaucoma spectrum). Although glaucoma is a highly inheritable disease, genetic factors yet explain only slight segment of all glaucoma. Reliable and accurate models for detecting individuals at higher risk of visual loss is an unmet need. We propose to use artificial intelligence (AI) constructs to discover visual field and imaging signatures of glaucoma and synthesize these signatures with classic risk factors and genetic data to identify individuals at-risk of developing glaucoma and future vision loss. The central hypothesis of this proposal is that AI applied to fundus photographs, visual fields and genetic factors may recognize and quantify the glaucoma-induced signs, yielding better signatures for glaucoma development and vision loss compared to current subjectively specified or conventionally identified features. As such, we will develop AI models to predict glaucoma from fundus photographs and visual fields then extract fundus and visual field endophenotypes (signatures) of glaucoma. We will then develop genome-wide association study (GWAS) and machine learning models to address underpower GWAS limitation and develop AI models to predict glaucoma from identified genetic markers. We finally develop an AI construct to synthesizes the discovered fundus and visual field signatures with classic glaucoma risk factors and genetic data to predict glaucoma. This AI construct can work with any or all of these modalities as well thus providing a potential tool for screening purposes as well. To achieve these objectives, we have assembled a team of interdisciplinary experts with access to large clinically annotated multi-modal glaucoma data. Our proposed studies will potentially uncover novel genetic factors of glaucoma as well as visual field and imaging endophenotypes of glaucoma that may serve as surrogate endpoints to improve glaucoma clinical trials and offer improvements in identifying individuals at-risk of developing glaucoma and future vision loss.

青光眼是一种复杂的神经退行性致盲疾病，导致视网膜神经节变性。 细胞和它们的轴突。在接下来的两年中，青光眼的患病率预计将增加近50%。 几十年来，老年人口构成了全球人口增长最快的部分。青光眼的负担 因此，护理将继续增长，而眼科医生或 可用的资源。因此，青光眼护理所需的需求可能会超过能力， 为那些视力丧失风险最高的患者确定护理优先顺序的资源。没有具体的东西 支持一项或一组测试的证据，这些测试显示了在识别高危人群方面的优越性 发展中的青光眼或青光眼进展的高危人群。青光眼的危险因素也是 在识别可能发展为青光眼的个体时不敏感。眼底照片缺乏细节和 视盘及周围视网膜神经纤维层的高分辨率信息在青光眼评估中的应用 视野测试提供了令人惊讶的不一致和可变的结果，特别是在亚临床青光眼中 而在视野丧失更严重的患者(青光眼谱的两侧)。尽管青光眼是一个 高度可遗传的疾病，遗传因素仍然只解释了所有青光眼的一小部分。可靠且 用于检测视力丧失风险较高的个体的准确模型是一个尚未得到满足的需求。我们建议使用 人工智能(AI)构建来发现青光眼的视野和成像特征并合成 这些带有经典风险因素和遗传数据的签名可以识别有发展为青光眼风险的个体 以及未来的视力丧失。这一提议的中心假设是人工智能应用于眼底照片，视觉 田野和遗传因素可以识别和量化青光眼诱导的体征，产生更好的体征 与目前主观或常规规定的青光眼发展和视力丧失相比 已确定的功能。因此，我们将开发人工智能模型来根据眼底照片和 然后视野提取青光眼的眼底和视野内表型(信号)。到时候我们会的 开发全基因组关联研究和机器学习模型，以解决全基因组关联研究能力不足的问题 限制并开发人工智能模型以根据已识别的遗传标记预测青光眼。我们终于开发了一种人工智能 构建将已发现的眼底和视野信号与经典的青光眼危险因素相结合 和基因数据来预测青光眼。该AI构造也可以与这些通道中的任何一个或所有通道一起工作 从而也为筛选目的提供了一种潜在的工具。为了实现这些目标，我们聚集在一起 一个跨学科专家团队，可以访问大量临床注释的多模式青光眼数据。 我们拟议的研究可能会揭示青光眼的新遗传因素以及视野和 青光眼内表型成像可作为改善青光眼临床的替代终点 并在识别青光眼和未来视力丧失的风险个体方面提供改进。