ArticleBiomedical optics express2023
Optimizing the OCTA layer fusion option for deep learning classification of diabetic retinopathy.
Article in Biomedical optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 37 citations in OpenAlex.
- Enhanced deep learning model for prediction of diabetic mellitus on optical coherence tomography angiography images.Quantitative imaging in medicine and surgery · 2026Article
- Interpretable Machine Learning-Based Concentric Regional Analysis of OCTA Images for Enhanced Diabetic Retinopathy Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Attention-Based Multimodal Deep Learning for Uveal Melanoma Classification Using Ultra-Widefield Fundus Images and Ocular Ultrasound.Ophthalmology science · 2026Article
- Integration of ultra-widefield optical coherence tomography angiography images and clinical indicators for identifying early-stage diabetic retinopathy in type 2 diabetes.Frontiers in endocrinology · 2026Article
- Detection of diabetic retinopathy using multicolor image by multimodal network incorporating information bottleneck (MNIIB).Scientific reports · 2025Article
- OCTA-ReVABiomedical optics express · 2025Article
- Deep learning segmentation of periarterial and perivenous capillary-free zones in optical coherence tomography angiography.Journal of biomedical optics · 2025Article
- A deep learning based model for diabetic retinopathy grading.Scientific reports · 2025Article
- A panoramic perspective: application prospects and outlook of multimodal artificial intelligence in the management of diabetic retinopathy.Frontiers in public health · 2025Review
- Artificial intelligence in the diagnosis of uveal melanoma: advances and applications.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- OCTA-ReVA: an open-source toolbox for comprehensive retinal vessel feature analysis in optical coherence tomography angiography.Biomedical optics express · 2024Article
- Colour fusion effect on deep learning classification of uveal melanoma.Eye (London, England) · 2024Article
- Differential Capillary and Large Vessel Analysis Improves OCTA Classification of Diabetic Retinopathy.Investigative ophthalmology & visual science · 2024Article
- Assessing spectral effectiveness in color fundus photography for deep learning classification of retinopathy of prematurity.Journal of biomedical optics · 2024Article
- Differential artery-vein analysis improves the OCTA classification of diabetic retinopathy.Biomedical optics express · 2024Article
- Correlations between Retinal Microvascular Parameters and Clinical Parameters in Young Patients with Type 1 Diabetes Mellitus: An Optical Coherence Tomography Angiography Study.Diagnostics (Basel, Switzerland) · 2024Article
- Color Fusion Effect on Deep Learning Classification of Uveal Melanoma.Research square · 2023Article
- OCT-angiography based artificial intelligence-inferred fluorescein angiography for leakage detection in retina [Invited].Biomedical optics express · 2023Article
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Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
The purpose of this study is to evaluate layer fusion options for deep learning classification of optical coherence tomography (OCT) angiography (OCTA) images. A convolutional neural network (CNN) end-to-end classifier was utilized to classify OCTA images from healthy control subjects and diabetic patients with no retinopathy (NoDR) and non-proliferative diabetic retinopathy (NPDR). For each eye, three en-face OCTA images were acquired from the superficial capillary plexus (SCP), deep capillary plexus (DCP), and choriocapillaris (CC) layers. The performances of the CNN classifier with individual layer inputs and multi-layer fusion architectures, including early-fusion, intermediate-fusion, and late-fusion, were quantitatively compared. For individual layer inputs, the superficial OCTA was observed to have the best performance, with 87.25% accuracy, 78.26% sensitivity, and 90.10% specificity, to differentiate control, NoDR, and NPDR. For multi-layer fusion options, the best option is the intermediate-fusion architecture, which achieved 92.65% accuracy, 87.01% sensitivity, and 94.37% specificity. To interpret the deep learning performance, the Gradient-weighted Class Activation Mapping (Grad-CAM) was utilized to identify spatial characteristics for OCTA classification. Comparative analysis indicates that the layer data fusion options can affect the performance of deep learning classification, and the intermediate-fusion approach is optimal for OCTA classification of DR.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.