Revolutionizing Diabetic Retinopathy Diagnostics and Therapy Through Artificial Intelligence

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 136 - 155

Published: Feb. 9, 2024

Using artificial intelligence (AI) to its transformative advantage, the smart vision initiative represents a paradigm shift in diagnostics and treatment of diabetic retinopathy. The primary aim this is address all forms retinopathy using cutting-edge AI techniques, including deep neural networks machine learning. These advanced algorithms are designed for rapid precise diagnosis, enabling swift interventions prevent visual impairment by identifying intricate patterns that invisible human eye. Through identification complex eye, these guarantee quick accurate diagnosis. This early detection crucial as it allows immediate care, significantly reducing risk irreversible loss. sets stage future where no longer leads blindness, offering brighter, clearer, safer optical those affected condition.

Language: Английский

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G2ViT: Graph Neural Network-Guided Vision Transformer Enhanced Network for retinal vessel and coronary angiograph segmentation

Neural Networks, Journal Year: 2024, Volume and Issue: 176, P. 106356 - 106356

Published: May 3, 2024

Language: Английский

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Molecular and Cellular Mechanisms Involved in the Pathophysiology of Retinal Vascular Disease—Interplay Between Inflammation and Oxidative Stress

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(21), P. 11850 - 11850

Published: Nov. 4, 2024

Retinal vascular diseases encompass several retinal disorders, including diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, and occlusion; these disorders are classified as similar groups due to impaired vascularization. The aim this review is address the main signaling pathways involved in pathogenesis identify crucial molecules importance their interactions. Vascular endothelial growth factor (VEGF) recognized a central molecule abnormal neovascularization key phenomenon thus, anti-VEGF therapy now most successful form treatment for disorders. Interaction between angiopoietin 2 Tie2 receptor results aberrant signaling, resulting loss pericytes, neovascularization, inflammation. Notch hypoxia-inducible factors ischemic conditions induce pathological disruption blood-retina barrier. An increase pro-inflammatory cytokines-TNF-α, IL-1β, IL-6-and activation microglia create persistent inflammatory milieu that promotes breakage blood-retinal barrier neovascularization. Toll-like nuclear factor-kappa B important dysregulation immune response diseases. Increased production reactive oxygen species oxidative damage follow inflammation together vicious cycle because each amplifies other. Understanding complex interplay among various pathways, cascades, enables development new more therapeutic options.

Language: Английский

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Retinal Vein Occlusion–Background Knowledge and Foreground Knowledge Prospects—A Review

Journal of Clinical Medicine, Journal Year: 2024, Volume and Issue: 13(13), P. 3950 - 3950

Published: July 5, 2024

Thrombosis of retinal veins is one the most common vascular diseases that may lead to blindness. The latest epidemiological data leave no illusions burden on healthcare system, as impacted by patients with this diagnosis, will increase worldwide. This obliges scientists search for new therapeutic and diagnostic options. In 21st century, there has been tremendous progress in imaging techniques, which facilitated a better understanding mechanisms related development vein occlusion (RVO) its complications, consequently enabled introduction treatment methods. Moreover, artificial intelligence (AI) likely assist selecting best option near future. aim comprehensive review re-evaluate old but still relevant RVO confront them studies. paper provide detailed overview current treatment, prevention, future possibilities regarding RVO, well clarifying mechanism macular edema disease entity.

Language: Английский

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Attention-guided cascaded network with pixel-importance-balance loss for retinal vessel segmentation

Frontiers in Cell and Developmental Biology, Journal Year: 2023, Volume and Issue: 11

Published: May 9, 2023

Accurate retinal vessel segmentation from fundus images is essential for eye disease diagnosis. Many deep learning methods have shown great performance in this task but still struggle with limited annotated data. To alleviate issue, we propose an Attention-Guided Cascaded Network (AGC-Net) that learns more valuable features a few images. Attention-guided cascaded network consists of two stages: the coarse stage produces rough prediction map image, and fine refines missing details map. In attention-guided network, incorporate inter-stage attention module (ISAM) to cascade backbone these stages, which helps focus on regions better refinement. We also Pixel-Importance-Balance Loss (PIB Loss) train model, avoids gradient domination by non-vascular pixels during backpropagation. evaluate our mainstream image datasets (i.e., DRIVE CHASE-DB1) achieve AUCs 0.9882 0.9914, respectively. Experimental results show method outperforms other state-of-the-art performance.

Language: Английский

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Artificial intelligence in retinal imaging: current status and future prospects

Expert Review of Medical Devices, Journal Year: 2023, Volume and Issue: 21(1-2), P. 73 - 89

Published: Dec. 13, 2023

The steadily growing and aging world population, in conjunction with continuously increasing prevalences of vision-threatening retinal diseases, is placing an burden on the global healthcare system. main challenges within retinology involve identifying comparatively few patients requiring therapy large mass, assurance comprehensive screening for disease individualized planning. In order to sustain high-quality ophthalmic care future, incorporation artificial intelligence (AI) technologies into our clinical practice represents a potential solution.

Language: Английский

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Multi-Layer Preprocessing and U-Net with Residual Attention Block for Retinal Blood Vessel Segmentation

Diagnostics, Journal Year: 2023, Volume and Issue: 13(21), P. 3364 - 3364

Published: Nov. 1, 2023

Retinal blood vessel segmentation is a valuable tool for clinicians to diagnose conditions such as atherosclerosis, glaucoma, and age-related macular degeneration. This paper presents new framework segmenting vessels in retinal images. The has two stages: multi-layer preprocessing stage subsequent employing U-Net with multi-residual attention block. three steps. first step noise reduction, U-shaped convolutional neural network matrix factorization (CNN MF) detailed (D_U-Net) minimize image noise, culminating the selection of most suitable based on PSNR SSIM values. second dynamic data imputation, utilizing multiple models purpose filling missing data. third augmentation through utilization latent diffusion model (LDM) expand training dataset size. segmentation, where U-Nets block are used segment images after they have been preprocessed removed. experiments show that effective at vessels. It achieved Dice scores 95.32, accuracy 93.56, precision 95.68, recall 95.45. also efficient results removing using CNN (MF) D-U-NET according values (0.1, 0.25, 0.5, 0.75) levels noise. LDM an inception score 13.6 FID 46.2 step.

Language: Английский

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Artificial intelligence in therapeutic management of hyperlipidemic ocular pathology

Experimental Eye Research, Journal Year: 2024, Volume and Issue: 245, P. 109954 - 109954

Published: June 4, 2024

Hyperlipidemia has many ocular manifestations, the most prevalent being retinal vascular occlusion. Hyperlipidemic lesions and occlusions to vessels supplying retina result in permanent blindness, necessitating prompt detection treatment. Retinal occlusion is diagnosed using different imaging modalities, including optical coherence tomography angiography. These diagnostic techniques obtain images representing blood flow through vessels, providing an opportunity for AI utilize image recognition detect blockages abnormalities before patients present with symptoms. already used as a non-invasive method other pathology, well predict treatment outcomes. As providers see increase presenting new occlusions, use of treat these conditions potential improve patient outcomes reduce financial burden on healthcare system. This article comprehends implications current management strategies (RVO) hyperlipidemia recent developments technology diseases.

Language: Английский

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Development and validation of an automated machine learning model for the multi-class classification of diabetic retinopathy, central retinal vein occlusion and branch retinal vein occlusion based on color fundus photographs

JFO Open Ophthalmology, Journal Year: 2024, Volume and Issue: 7, P. 100117 - 100117

Published: June 14, 2024

Automated machine learning (AutoML) is a novel artificial intelligence (AI) strategy that enables clinicians without coding experience to develop their own AI models. This study assessed the discriminative performance of AutoML in differentiating diabetic retinopathy (DR), central retinal vein occlusion (CRVO) and branch (BRVO) from normal fundi using color fundus photographs (CFPs). We carried out model design CFPs retrieved publicly available CFP data set (3200 labelled images). The were reviewed for quality then uploaded Google Cloud Vertex platform training testing. trained multi-class classification differentiate DR, CRVO, BRVO 875 externally validated 210 obtained another dataset. Performance metrics, including area under receiver operator curve (AUROC) sensitivity reported. compared state-of-the-art deep (DL)-based DR RVO models identified through literature search. Our showed high CRVO based on CFPs, with an AUROC, precision recall reaching 0.995, 95.4% respectively at 0.5 confidence threshold. per-label specificity, respectively, (97.5%, 100%), (100%, 93.88%), (66.67%, 100%) (71.43%, 98.73%). generally similar DL classifiers. can detect good diagnostic accuracy potentially useful screening tool.

Language: Английский

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Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

International Journal of Ophthalmology, Journal Year: 2023, Volume and Issue: 17(1), P. 1 - 6

Published: Dec. 26, 2023

To develop an artificial intelligence (AI) diagnosis model based on deep learning (DL) algorithm to diagnose different types of retinal vein occlusion (RVO) by recognizing color fundus photographs (CFPs).

Language: Английский

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