Sight is one of our five major senses through which we perceive our surroundings. Disruptions to sight result in a drastically decreased quality of life. Much of the blindness caused by eye diseases is potentially preventable by earlier detection and intervention.

The most common causes of irreversible blindness in Singapore and worldwide are age-related macular degeneration and glaucoma. The current hypothesis of this thematic area is that blindness results from eye diseases that ultimately converge onto a limited number of final common pathways. Thus, the search for biological insights providing an improved understanding of eye disease mechanisms is a priority.

At GIS, we are using genetic studies as a starting tool to discover molecular mechanisms underlying eye diseases. Promising “hits” emerging from the discovery programme will be assessed using bioinformatics and biological experiments for application as a precision medicine tool in the clinic and/ or as a therapeutic target for disease interception. We also foresee tremendous benefits in linking eye genetic data, clinical information, and imaging modalities using AI learning methods to construct personalised risk models for disease prediction.

Demand drivers in ophthalmology encompass academic, industry, and healthcare institutions

  • Academic demand drivers - Research institutions derive use because the biological insights from the data will illuminate new research directions as well as opportunities for collaboration.
  • Industry demand drivers - Pharmaceutical companies are particularly interested due to the illumination of drug targets through genetic studies applied on a large scale for eye diseases. This is particularly true, because for irreversible blinding disorders, very few treatment options exist (one to two drugs, which only work in <50 percent of patients). The scale of the genetic studies provides a measure of credibility to the candidate genes arising from them.
  • Healthcare demand drivers - Interest from the healthcare sector arises from the potential opportunities for early detection of asymptomatic disease and for the stratification of patients at higher risk (due to their genetic profile) for earlier intervention.

An emerging area in this thematic area is the use of AI in imaging diagnostics of eye diseases. This new technology raises the question whether the incorporation of patient-level genomic information could further improve the diagnostic yield.

Dr Khor Chiea Chuen