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Diabetic Retinopathy

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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Updated: Jun 23, 2026

Behavioral Assessment of Visual Function via Optomotor Response and Cognitive Function via Y-Maze in Diabetic Rats

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Implementing an artificial intelligence system into a diabetic eye screening programme in Tanzania.

Charles R Cleland^1,2, William U Makupa², Bernadetha R Shilio³

¹International Centre for Eye Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK.

Transactions of the Royal Society of Tropical Medicine and Hygiene

|December 16, 2024

Summary

This summary is machine-generated.

Tanzania faces challenges in diabetic eye care due to high diabetes rates and few specialists. Implementing artificial intelligence (AI) offers a promising solution for improving diabetic retinopathy screening services in low-resource settings.

Keywords:

artificial intelligence diabetic retinopathy implementation screening

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Area of Science:

Ophthalmology
Medical Informatics
Public Health

Background:

Tanzania has the highest age-adjusted diabetes prevalence in sub-Saharan Africa.
Diabetic retinopathy is a leading cause of preventable vision loss.
A shortage of specialist eye care professionals hinders effective screening and treatment.

Purpose of the Study:

To describe the experiences of selecting, procuring, and implementing an artificial intelligence (AI) system for diabetic eye screening.
To assess the potential of AI in task-shifting and improving screening services in a low-resource setting.

Main Methods:

Selection and procurement of an AI system for automated clinical decision-making.
Implementation of the AI system into a regional diabetic eye screening program in northern Tanzania.

Main Results:

The study details the practical steps and challenges encountered during AI system implementation.
The AI system was integrated into an existing regional screening program.

Conclusions:

Artificial intelligence systems show potential for enhancing diabetic retinopathy screening services in resource-limited areas.
Task-shifting through AI can help overcome specialist shortages and improve patient outcomes.