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15 articles for “retinopathy”
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A summary continuation analysis evaluating the prevalence and predictors of diabetic retinopathy in newly diagnosed type 2 diabetic patients.
Abstract: Context: Diabetic retinopathy (DR), the leading cause of acquired blindness in adults, affects approximately 93 million people globally. It is a serious complication of type 2 diabetes, resulting from prolonged damage to the blood vessels in the retina. Although largely preventable and treatable, DR continues to be the main cause of vision loss among working-age adults and significantly impacts quality of life. While most studies on DR in Nepal have …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 2, 2024 · pp. 21–30 Read article
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The Analysis of Deep Learning-Based Methods for Identifying Diabetic Retinopathy
Abstract: Diabetic retinopathy (DR) is a degenerative eye condition resulting from diabetes mellitus, where high blood glucose levels lead to lesions on the retina. This condition is considered the leading cause of blindness among working-age diabetic patients, particularly in developing countries. As the disease is irreversible, the treatment aims to preserve the patient’s current vision. Early detection is crucial for effective management of DR to maintain vision. One of the main …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 3, 2024 · pp. 15–31 Read article
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Exploring Diabetic Retinopathy from an Ayurvedic Lens: Insights Leveraging Bioinformatics
Abstract: Diabetic retinopathy is one among the target diseases for VISION 2025. The huge cost required for treatment, economic loss due to absenteeism from work etc... has made it as a great public challenge. Therefore it is in need of the hour to address the issues of diabetes mellitus with its complication Diabetic Retinopathy with all seriousness and it is quite essential to search for a affordable medical care for the …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 11, Issue 2, 2024 · pp. 45–51 Read article
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Detection and Classification of Diabetic Retinopathy Using Deep Learning Techniques
Abstract: This project delves into the evaluation of three prominent deep learning architectures Basic CNN, ResNet, and DenseNet for their efficacy in detecting diabetic retinopathy from retinal images. Utilizing a diverse dataset, the study employs standard deep learning frameworks to train and validate each model. The focus extends to exploring the potential benefits of transfer learning on a limited dataset. Evaluation metrics like specificity, sensitivity, and accuracy are employed for a …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 2, 2024 · pp. 64–69 Read article
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Collaborative Care for Diabetic Retinopathy: Integrating Artificial Intelligence and Clinical Pharmacy Services - A Comprehensive Review
Abstract: Background: Diabetic retinopathy (DR) remains the leading cause of blindness among working-age adults globally, affecting approximately 103 million people worldwide. The integration of artificial intelligence (AI) technologies with clinical pharmacy services presents unprecedented opportunities to enhance screening, diagnosis, and management of DR through collaborative care models. Objective: This comprehensive review examines the current landscape of collaborative care approaches for diabetic retinopathy management, focusing on the integration of AI-powered diagnostic tools …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 15, Issue 3, 2025 · pp. 118–128 Read article
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A Comprehensive Review of CNN-Based Framework for Multi-Sign Detection of Diabetic Retinopathy in Fundus Images Using Public Datasets
Abstract: Diabetic retinopathy (DR) is one of the main causes of vision impairment. Blindness prevention and effective treatment depend on early detection. A thorough deep learning-based framework for the automatic segmentation and simultaneous detection of exudates, hemorrhages, and microaneurysms – three important DR indicators – from retinal fundus images is presented in this work. These three pathological signs’ corresponding annotated image patches, along with background (no-sign) areas, were used to train …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 14–23 Read article
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Survey on Retinal OCT Image Preprocessing, Segmentation, and Deep Learning Based Classification
Abstract: Optical coherence tomography (OCT) is a non-invasive technique that generates high-resolution, detailed cross-sectional images of biological tissues. By utilizing low-coherence interferometry, OCT enables visualization of tissue microstructure with micron-scale resolution, making it useful in various medical fields such as ophthalmology, cardiology, and dermatology. In ophthalmology, OCT is extensively used for diagnosing and monitoring retinal diseases like macular degeneration and diabetic retinopathy, allowing doctors to assess changes in tissue morphology over …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 3, 2024 · pp. 1–9 Read article
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The Integration of Artificial Intelligence in Ophthalmology: Augmenting Clinical Decision or Clinical Dependency?
Abstract: The diagnostic accuracy of artificial intelligence (AI) algorithms for glaucoma and diabetic retinopathy screening is on par with or higher than that of skilled doctors. Their acceptance has accelerated due to regulatory clearances, practical implementation in telehealth networks, and growing proof of cost-effectiveness. However, this very success raises a question that the field has been reluctant to address: are we unintentionally undermining the clinical reasoning abilities of the upcoming generation …
Published in Research and Reviews: A Journal of Health Professions · Vol. 16, Issue 1, 2026 · pp. 31–34 Read article
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Recent Trends in Ocular Drug Delivery: Challenges and Approaches
Abstract: Drug topically regulation can be accessed most easily in the eye. When given topically as eye drops, medication ocular bioavailability is quite low. Drug entry into specific ocular locations is complicated by the complex anatomy and physiology of the human eye. Research has long been interested in the topical administration of successful treatments. To provide a suitable ocular penetration and extend the duration of drug residence is their challenging task. …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 11, Issue 3, 2024 · pp. 22–38 Read article
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Image Preprocessing and Analysis on Eye Fundus Images Segmentation by Using Density Clustering Methods
Abstract: In order to do an automated evaluation of various retinal illnesses such as Diabetic retinopathy, Glaucoma, and Macular Edema, fundus images must be pre-processed first. For many reasons, it's difficult to accurately detect the optic disc. Many blood vessels cross the optic disc, making it difficult to discern the disc's boundaries in fundus images. Lesion regions in diabetic retinopathy look very much like an optic disc's colour and texture, so …
Published in Recent Trends in Sensor Research & Technology Read article
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Eye Disease Classification Using K-means Clustering Algorithm and Ensemble Classification Approach
Abstract: In this study, we present a comprehensive approach for the classification of eye diseases, specifically targeting normal, cataract, glaucoma, and diabetic retinopathy conditions. This research uses a dataset from Kaggle, which provides a wide and varied collection of retinal images to ensure good representation. The methodology encompasses advanced image processing and machine learning techniques to ensure accurate diagnosis and prediction. The preprocessing phase involves a series of image enhancement techniques …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 3, Issue 2, 2025 · pp. 15–27 Read article
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Retinal Disease Detection Using Deep CNN
Abstract: Age-related macular degeneration, glaucoma, and diabetic retinopathy are the three main causes of blindness in the globe. To avoid visual loss, early identification and treatment of these disorders are essential. The goal of this research is to create an automated method for detecting retinal diseases by analyzing retinal fundus pictures with machine learning techniques. Python and the Tkinter package for the graphical user interface are used in the construction of …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 2, 2024 · pp. 46–50 Read article
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Refining Retinal Layer Segmentation in OCT Imaging with Advanced Techniques and Clinical Applications
Abstract: Segmenting retinal layers from Optical Coherence Tomography (OCT) pictures entails locating and separating different retinal layers to offer comprehensive anatomical and pathological information. Age-related macular degeneration, diabetic retinopathy, and glaucoma are among the retinal illnesses for which this procedure is crucial for diagnosis and follow-up. By utilizing preprocessing techniques to improve image quality and applying advanced algorithms—such as intensity-based, gradient-based, and texture-based methods—alongside deep learning approaches, clinicians can accurately measure …
Published in Trends in Opto-electro & Optical Communication · Vol. 14, Issue 2, 2024 · pp. 01–06 Read article
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Transfer Learning in Deep Learning Models for Medical Imaging: Utilizing Pretrained Models to Improve Performance in Medical Image Analysis
Abstract: Transfer learning is now a trending technique in deep learning, especially in medical imaging. This technique solves landmark problems by utilizing the pre-trained models, including the limited availability of the annotated medical data and the time-consuming computational costs of training deep learning models from scratch. The generalizability of deep models could increase diagnostic precision for specific medical tasks, require fewer samples to train, and take less time to train due …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 1, 2025 · pp. 67–85 Read article
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Non-Invasive Glucose Monitoring Device Using Max30102 Sensor
Abstract: Diabetes mellitus is a chronic metabolic disorder affecting millions globally, requiring continuous blood glucose monitoring to prevent complications such as cardiovascular disease, kidney failure, neuropathy, and retinopathy. Conventional invasive finger- prick techniques result in pain, skin irritation, and an increased risk of infection, which lowers patient compliance, particularly in young patients and the elderly. This paper presents a non-invasive glucose monitoring prototype using the MAX30102 optical biosensor interfaced with the …
Published in Journal of Instrumentation Technology & Innovations · Vol. 16, Issue 1, 2026 · pp. 36–44 Read article