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 glau...