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盲目疾病，导致视网膜神经节的变性 细胞及其轴突。青光眼的患病率预计在接下来的两个 几十年来，老年人构成了全球人口中增长最快的部分。青光眼的负担 因此，护理将继续增长，而眼科医生的数量增加，或者 可用资源。结果，青光眼护理所需的需求可能会超过能力，并且 为那些患有视力丧失风险最高的患者提供护理优先级的资源。没有混凝土 支持个人测试或一组测试的证据，这些测试表现出优势，可以识别人们处于危险中的人 发展青光眼或青光眼进展风险较高的青光眼。青光眼风险因素也是 在识别可能发展青光眼的个体方面不敏感。眼底照片缺乏细节， 光盘和周围视网膜神经纤维层的高分辨率信息，用于青光眼评估 和视野测试提供了令人惊讶的不一致和可变结果，尤其是在亚临床青光眼中 以及更严重的视野损失（青光眼光谱两侧）的患者。虽然青光眼是 高度遗传的疾病，遗传因素，但仅解释了所有青光眼的轻微部分。可靠和 未满足的人的准确模型是未满足视觉损失风险的较高风险的模型。我们建议使用 人工智能（AI）构造可发现青光眼和合成的视野和成像特征 这些具有经典危险因素和遗传数据的特征，以识别患有青光眼发展的个体 和未来的视力丧失。该提议的中心假设是AI应用于眼底照片，视觉 田地和遗传因素可能识别并量化青光眼诱导的迹象，从而产生更好的特征 与当前主观指定或常规的目前相比，对于青光眼的发展和视力丧失 确定的功能。因此，我们将开发AI模型，以预测眼底照片和 然后，视野提取青光眼的眼底和视野内表型（特征）。然后我们会 开发全基因组协会研究（GWAS）和机器学习模型，以解决无水平的GWAS 限制并开发AI模型以预测已鉴定的遗传标记的青光眼。我们终于开发了一个人工智能 构建以与经典青光眼风险因素合成发现的眼底和视野特征 和遗传数据以预测青光眼。该AI结构也可以与任何这些方式一起使用 因此，也提供了潜在的工具进行筛选。为了实现这些目标，我们已经组装了 一个跨学科专家团队，可以访问大型临床注释的多模式青光眼数据。 我们提出的研究可能会发现青光眼以及视野和视野的新遗传因素和 青光眼的成像内表型，可以用作改善青光眼临床的替代终点 试验并提供改进，以识别患者发展的青光眼和未来视力丧失的风险。