1. Aims:
1.1. To develop a deep learning algorithm for glaucoma using patients’ clinical assessment, epidemiologic data, living environment record and imaging.
1.2. To explore the genetic and metabolomics factors associated with the mechanisms of glaucoma pathogenesis.
1.3 To exploit a multi-modal deep learning strategy to predict glaucoma onset and progression combing clinical, genetic and metabolomics data.
2. Scientific rationale:
Glaucoma is the leading cause of irreversible blindness worldwide.It is a multifactorial disease, whith intraocular pressure (IOP) being the only modifiable risk factor. However, for many glaucoma patients, the disease progresses even with a well-controlled IOP, suggesting that there are other factors contributing to glaucoma. Additionally, the onset and progression of glaucoma is insidious. Patients are aware of the disease until reaching the advanced stages. Thus, it is urgent to recognize the early markers for the disease. Our team is working on a multi-modal deep learning strategy using clinical data, OCT and fundus photography to recognize and grade the severity of glaucomatous visual field damages. Recent GWAS and metabolomics studies have also shown the possible genetic and metabolomic mechanisms of glaucoma. Therefore, combing the clinical, genetic and metabolomics data may help identify novel strategies for predicting.As a result, we will better manage glaucoma beyond controlling eye pressure.
3. Project duration
The project will last 36 months.
4. Public health impact
The UK Biobank dataset comprises a vast number of participants, allowing for robust statistical analyses with increased statistical power to detect associations.Using multi-modal strategies to combing clinical data, genetic and metabolomic information, our research can identify biomarkers and risk factors that allow for earlier detection and intervention, enabling timely treatment to prevent glaucoma progression, and personalized treatment plans based on an individual’s genetic and metabolic profile, optimizing IOP-lowering therapy and reducing side effects. Additionally, integration with other omics data from the UK Biobank could provide a holistic view of glaucoma’s molecular underpinnings for better understanding of the disease.