The global prevalence of diabetes has surged from 108 million in 1980 to 422 million in 2014, with a notable increase in low- and middle-income countries. In Tanzania, where diabetes affects 12.3% of the population aged 20 to 79, the actual numbers may be higher due to a lack of awareness among over 60% of individuals with diabetes. Diabetic retinopathy (DR), a vision impairment condition, poses a significant risk to adults aged 25 to 64 in Tanzania, with a prevalence of 12% of adults reported in Tanzania among those screened. Conventional methods for diagnosing DR are cumbersome, expensive, time-consuming, and highly error-prone. In Tanzania, where there is a shortage of eye specialists, with only 55 ophthalmologists for over 60 million people, the ratio far exceeds WHO recommendations. This scarcity of specialists, coupled with the high cost of diagnosis, leads to late-stage detection and delayed treatment, resulting in the prevalence of blindness in the population. The project seeks to address the growing challenge of DR, a diabetes-related eye disease that can lead to irreversible blindness if not detected early. 

To tackle these issues, the project is developing an AI-based screening tool for early DR detection. The tool utilizes deep learning algorithms to classify retinal images into various stages of DR severity, from no DR to proliferative DR. This AI-driven approach offers several advantages, including reducing healthcare costs, improving the accuracy and efficiency of diagnoses, and expanding access to DR screening, especially in rural areas where healthcare services are limited.

The initiative aims to improve patient outcomes by offering timely and accurate diagnoses, reducing the burden on healthcare professionals, and lowering the overall cost of care. The success of this initiative could serve as a model for deploying AI-based healthcare solutions in other low- and middle-income countries.