ReviewSensors (Basel, Switzerland)2022
The Role of Different Retinal Imaging Modalities in Predicting Progression of Diabetic Retinopathy: A Survey.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Creatinine as a predictor of proliferative diabetic retinopathy among patients with type 2 diabetes mellitus: a systematic review and meta-analysis.International urology and nephrology · 2025Pooled it
- Recent advances and applications of optical coherence tomography angiography in diabetic retinopathy.Frontiers in endocrinology · 2025Pooled it
- Influence of Cardiometabolic Disease on Macular Structural Changes After Uncomplicated Phacoemulsification.Journal of clinical medicine · 2026Article
- The evolving role of interventional radiology in ophthalmology: advancements in imaging, therapeutic applications, and future prospects, a narrative review.Annals of medicine and surgery (2012) · 2026Article
- Contrast Sensitivity Impairment in Diabetic Retinopathy: Clinical and Structural Correlates.Diagnostics (Basel, Switzerland) · 2026Review
- ZDHHC5: a pivotal palmitoyltransferase orchestrating signaling networks - unraveling mechanisms and therapeutic horizons.Biomarker research · 2026Review
- Systemic-retinal inflammatory crosstalk in diabetic macular edema: correlation between hematologic indices and macular OCT-features.International journal of retina and vitreous · 2026Article
- Breakthroughs in diabetic retinopathy diagnosis and treatment using preclinical research models: current progress and future directions.Annals of medicine · 2025Review
- Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review.Annals of translational medicine · 2025Review
- Early Diabetic Retinopathy Detection from OCT Images Using Multifractal Analysis and Multi-Layer Perceptron Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Retinal Imaging as a Window into Cardiovascular Health: Towards Harnessing Retinal Analytics for Precision Cardiovascular Medicine.Journal of cardiovascular development and disease · 2025Review
- Article
- Diabetic Retinopathy-A Review.Current diabetes reviews · 2025Review
- Gold Nanoparticles for Retinal Molecular Optical Imaging.International journal of molecular sciences · 2024Review
- Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis.Scientific reports · 2024Article
- CSANet: a lightweight channel and spatial attention neural network for grading diabetic retinopathy with optical coherence tomography angiography.Quantitative imaging in medicine and surgery · 2024Article
- An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis.Scientific reports · 2024Article
- Vison transformer adapter-based hyperbolic embeddings for multi-lesion segmentation in diabetic retinopathy.Scientific reports · 2023Article
- Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images.Sensors (Basel, Switzerland) · 2023Article
- Flavoprotein fluorescence elevation is a marker of mitochondrial oxidative stress in patients with retinal disease.Frontiers in ophthalmology · 2023Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 3 countries.
Funding
Abstract
Diabetic retinopathy (DR) is a devastating condition caused by progressive changes in the retinal microvasculature. It is a leading cause of retinal blindness in people with diabetes. Long periods of uncontrolled blood sugar levels result in endothelial damage, leading to macular edema, altered retinal permeability, retinal ischemia, and neovascularization. In order to facilitate rapid screening and diagnosing, as well as grading of DR, different retinal modalities are utilized. Typically, a computer-aided diagnostic system (CAD) uses retinal images to aid the ophthalmologists in the diagnosis process. These CAD systems use a combination of machine learning (ML) models (e.g., deep learning (DL) approaches) to speed up the diagnosis and grading of DR. In this way, this survey provides a comprehensive overview of different imaging modalities used with ML/DL approaches in the DR diagnosis process. The four imaging modalities that we focused on are fluorescein angiography, fundus photographs, optical coherence tomography (OCT), and OCT angiography (OCTA). In addition, we discuss limitations of the literature that utilizes such modalities for DR diagnosis. In addition, we introduce research gaps and provide suggested solutions for the researchers to resolve. Lastly, we provide a thorough discussion about the challenges and future directions of the current state-of-the-art DL/ML approaches. We also elaborate on how integrating different imaging modalities with the clinical information and demographic data will lead to promising results for the scientists when diagnosing and grading DR. As a result of this article's comparative analysis and discussion, it remains necessary to use DL methods over existing ML models to detect DR in multiple modalities.
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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.