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Related Concept Videos

Diabetic Retinopathy01:27

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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Definition Diabetic nephropathy is a chronic kidney complication that results from prolonged hyperglycemia.Prevalence It is the most common cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) worldwide, affecting up to half of individuals with diabetes.Pathophysiology • Sustained hyperglycemia triggers multiple hemodynamic and metabolic changes in the kidney. • Early in the disease, increased renal blood flow and glomerular hyperfiltration occur due to afferent arteriolar...

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Comprehensive Review of Open-Source Fundus Image Databases for Diabetic Retinopathy Diagnosis.

Valérian Conquer1, Thomas Lambolais2, Gustavo Andrade-Miranda1

  • 1IMT Mines Ales, 30100 Alès, France.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

This review analyzes 22 open-source fundus retinal image databases for diabetic retinopathy (DR) detection. Objective comparisons using statistical features aid researchers in selecting optimal datasets for AI model development and clinical applications.

Keywords:
computer science image analysisdatabasesdiabetic retinopathyfundus image

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Diabetic retinopathy (DR) is a leading cause of blindness, necessitating early detection.
  • Fundus photography is a cost-effective, non-contact method for diagnosing and grading DR.
  • AI models require robust datasets for training, validation, and comparison in DR detection.

Purpose of the Study:

  • To comprehensively review 22 open-source fundus retinal image databases for DR research.
  • To highlight the characteristics and features of these commonly used datasets.
  • To provide an objective comparison of databases to aid researchers and practitioners.

Main Methods:

  • Systematic review of 22 open-source fundus retinal image databases released between 2000 and 2022.
  • In-depth image analysis using color space distances.
  • Principal Component Analysis (PCA) applied to 16 key statistical features for objective comparison.

Main Results:

  • Identification and characterization of 22 relevant open-source fundus image databases.
  • Objective quantitative comparison of databases based on image statistical features.
  • Analysis reveals variations in dataset characteristics crucial for AI model development.

Conclusions:

  • This review provides a valuable resource for selecting appropriate datasets in diabetic retinopathy research.
  • Informed database selection can enhance the development and validation of AI models for DR detection.
  • Ultimately, this supports improved clinical decision-making and patient outcomes in DR management.