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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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Automated FAZ segmentation and diabetic retinopathy classification using OCTA images.

Jamshid Saeidian¹, Hamid Riazi-Esfahani², Hossein Azimi¹

¹Faculty of Mathematical Sciences and Computer, Kharazmi University, No. 50, Taleghani Avenue, Tehran, Iran.

BMC Ophthalmology

|October 29, 2025

Summary

This study developed an automated deep learning framework for segmenting the foveal avascular zone (FAZ) in OCTA images and classifying diabetic retinopathy (DR). The system achieved high accuracy in both segmentation and DR classification, offering a promising tool for early diagnosis.

Keywords:

Classification Deep learning Diabetic retinopathy Foveal avascular zone Image segmentation OCTA

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Diabetic retinopathy (DR) diagnosis relies on identifying alterations in the foveal avascular zone (FAZ), a key biomarker.
Accurate FAZ segmentation in optical coherence tomography angiography (OCTA) images is crucial for DR assessment.

Purpose of the Study:

To develop and evaluate an automated deep learning framework for FAZ segmentation and DR classification using OCTA images.
To explore the feasibility of using deep learning for early and accurate DR diagnosis.

Main Methods:

A two-step deep learning pipeline was employed, integrating DeepLabv3+, EfficientNetB0, SE blocks, and ASPP for FAZ segmentation.
A GoogLeNet-based CNN was utilized for classifying DR stages (normal, NPDR, PDR) based on segmented FAZ images.
Data augmentation and SMOTE were applied to enhance classification performance, with 5-fold cross-validation used.

Main Results:

The FAZ segmentation network achieved a high Dice Similarity Coefficient (DSC) of 97.5%.
The classification model demonstrated 100% AUC for binary (normal vs. DR) and 87% AUC for three-class (normal, NPDR, PDR) classification.
The dataset included 253 OCTA scans from 161 participants with varying stages of DR.

Conclusions:

The developed automated framework shows significant potential as an assistive tool for clinicians.
This system can enable earlier and more accurate diagnosis of diabetic retinopathy from OCTA imaging.
Integration into clinical workflows could improve patient outcomes through timely DR detection.