Computational Methods and Deep Learning for Ophthalmology - Chapter 4: Epidemiological surveillance of blindness using deep learning approaches

Conventional surveillance methods used in present times could be improved with cutting-edge data science that not only helps to predict or track preventable blindness rapidly but coherently assess its systemic risk factors afflicting communities worldwide. The integration of big public health data, epidemiological informatics, and deep learning approaches has created new hopes in public health to tackle the burden of preventable blindness. This chapter will systematically appraise common applications of deep learning approaches and the utilization of big public health data to detect the risks of blindness within communities. The chapter will discuss the magnitude of the problem and subsequently conceptualize deep learning approaches and functions to be adopted in ophthalmic epidemiology. The methods, data sources, and possible outcomes from deep learning tasks will be enumerated using common case vignettes. Potential limitations and implications for public health practice will be discussed.