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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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Diabetic retinopathy screening using machine learning: a systematic review.

Fitsum Mesfin Dejene¹, Taye Girma Debelee^2,3, Friedhelm Schwenker⁴

¹Computer Vision, Ethiopian Artificial Intelligence Institute, Addis Ababa, 40782, Ethiopia.

BMC Biomedical Engineering

|September 2, 2025

Summary

This summary is machine-generated.

Machine learning (ML) offers a promising alternative for diabetic retinopathy (DR) screening, addressing limitations in manual image analysis. This study analyzes ML integration in DR screening, identifying challenges and future research directions.

Keywords:

Computer vision Deep learning Diabetic retinopathy screening Machine learning Transfer learning

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Diabetic retinopathy (DR) is a leading cause of blindness globally.
Manual screening of retinal images is time-consuming and faces expert shortages.
Machine learning (ML) and deep learning (DL) present viable alternatives for DR screening.

Purpose of the Study:

To analyze the research landscape of ML integration in diabetic retinopathy screening.
To identify and characterize available retinal fundus image datasets.
To discuss preprocessing techniques, ML progress, challenges, and future directions in DR screening.

Main Methods:

Literature review and analysis of ML techniques applied to DR screening.
Characterization of publicly available retinal fundus image datasets.
Discussion of common image preprocessing methods for DR detection.

Main Results:

Identified and characterized available retinal image datasets for DR screening.
Analyzed the progress and application of various ML techniques in DR detection.
Highlighted common preprocessing steps essential for effective DR screening.

Conclusions:

ML integration shows significant potential to improve the efficiency and accessibility of diabetic retinopathy screening.
Standardized datasets, model complexity, and computational resources remain key challenges.
Further research is needed to overcome existing hurdles and advance ML-based DR screening solutions.