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Quantifying the Characteristics of Diabetic Retinopathy in Macular Optical Coherence Tomography Angiography Images: A
Ali Akbar Movassagh1, Mahdie Jajroudi2, Amir Homayoun Jafari1
1Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, IRN.
Background:
Early detection and accurate staging of diabetic retinopathy (DR) are important to prevent vision loss. Optical coherence tomography angiography (OCTA) images provide detailed insights into the retinal vasculature, revealing intricate changes that occur as DR progresses. However, interpreting these complex images requires significant expertise and is often time-intensive. Deep learning techniques have the potential to automate DR analysis. However, they typically require large datasets for effective training. To address the challenge of limited data in this emerging imaging field, a combined approach using few-shot learning (FSL) and self-attention mechanisms within explainable AI (XAI) was explored.
Objective:
To investigate and evaluate the potential of an FSL-self-attention XAI approach to improve the accuracy of DR staging classification using OCTA images.
Methods:
A total of 206 OCTA images, comprising 104 non-proliferative diabetic retinopathy (NPDR) and 102 proliferative diabetic retinopathy (PDR) cases, were analyzed using the FSL method. Three pre-trained networks (ResNet-50, DenseNet-161, and MobileNet-v2) were employed, with the top-performing model subsequently integrated with the Match-Them-Up Network (MTUNet) to provide explainable interpretations using a self-attention mechanism. The performance of the models was evaluated by applying standard metrics, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). The performance of the MTUNet model is assessed by calculating pattern-matching scores for PDR and NPDR classes.
Results:
The ResNet-50 pre-trained model in FSL demonstrated the best overall performance, achieving an accuracy of 76.17%, a sensitivity of 81.83%, a specificity of 70.5%, and 0.82 AUC in classifying DR stages. MTUNet provided pattern-matching scores of 0.77 and 0.75 for PDR and NPDR classes, respectively.
Conclusions:
FSL and self-attention mechanisms in XAI offer promising approaches for accurate DR stage classification, especially in data-limited scenarios. This could potentially facilitate early DR detection and inform clinical decision-making.
