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PeerJ. Computer Science 2024Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a...
Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.
PubMed: 38699206
DOI: 10.7717/peerj-cs.1947 -
International Ophthalmology Oct 2019Diabetic retinopathy (DR) is one of the leading causes of blindness worldwide. Accurate investigative tools are essential for the early diagnosis and monitoring of the...
PURPOSE
Diabetic retinopathy (DR) is one of the leading causes of blindness worldwide. Accurate investigative tools are essential for the early diagnosis and monitoring of the disease. Optical coherence tomography angiography (OCTA) is a recently developed technology that enables visualisation of the retinal microvasculature.
METHODS
A systematic review of the literature was performed to examine the diagnostic use of OCTA in DR to date. Medline, EMBASE, and Cochrane databases were searched to find relevant studies. Sixty-one original studies were selected for the review.
RESULTS AND DISCUSSION
OCTA has demonstrated the ability to identify microvascular features of DR such as microaneurysms, neovascularisation, and capillary non-perfusion. Furthermore, OCTA is enabling quantitative evaluation of the microvasculature of diabetic eyes. It has demonstrated the ability to detect early microvascular changes, in eyes with or without clinically evident DR. It has also been shown to detect progressive changes in the foveal avascular zone, and vascular perfusion density, with worsening severity of disease. It provides three-dimensional visualisation of the individual retinal vascular networks and is thereby enhancing our understanding of the role of the deeper vasculature in the pathogenesis of diabetic retinopathy and maculopathy.
CONCLUSION
However, limitations exist with current OCTA technology, in respect to the small field of view, image quality, projection artefact, and inaccuracies in analysis of the deeper vascular layers. While questions remain regarding its practical applicability in its present form, with continuing development and improvement of the technology, the diagnostic value of OCTA in diabetic retinopathy is likely to become evident.
Topics: Capillaries; Diabetic Retinopathy; Early Diagnosis; Fluorescein Angiography; Humans; Microvessels; Retinal Vessels; Tomography, Optical Coherence
PubMed: 30382465
DOI: 10.1007/s10792-018-1034-8