Ophthalmology

On QSPR analysis of glaucoma drugs using machine learning with XGBoost and regression models.

Glaucoma is an irreversible, progressive, degenerative eye disorder arising because of increased intraocular pressure, resulting in eventual vision loss if untreated. The QSPR relates, mathematically, by employing various algorithms, a specified property of a molecule that arises either from physical, chemical, or biological phenomena using various aspects of its structure. Here in, a similar appl...

OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning.

Glaucoma poses a growing health challenge projected to escalate in the coming decades. However, current automated diagnostic approaches on Glaucoma diagnosis solely rely on black-box deep learning models, lacking explainability and trustworthiness. To address the issue, this study uses optical coherence tomography (OCT) images to develop an explainable artificial intelligence (XAI) tool for diagno...

Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models.

Diabetic retinopathy, a retinal disorder resulting from diabetes mellitus, is a prominent cause of visual degradation and loss among the global popula...

Multi scale multi attention network for blood vessel segmentation in fundus images.

Precise segmentation of retinal vasculature is crucial for the early detection, diagnosis, and treatment of vision-threatening ailments. However, this...

Retinal OCT image classification based on MGR-GAN.

Accurately classifying optical coherence tomography (OCT) images is essential for diagnosing and treating ophthalmic diseases. This paper introduces a...

Optimising deep learning models for ophthalmological disorder classification.

Fundus imaging, a technique for recording retinal structural components and anomalies, is essential for observing and identifying ophthalmological dis...

Enhanced Phenotype Identification of Common Ocular Diseases in Real-World Datasets.

OBJECTIVE: For studies using real-world data, accurately identifying patients with phenotypes of interest is challenging. To identify cohorts of inter...

The research progress of biologics in elderly-onset rheumatoid arthritis (EORA).

is a distinct subtype of characterized by heightened treatment challenges due to immune aging and the complexity of comorbidities. This review syste...

Deep learning generalization for diabetic retinopathy staging from fundus images.

. Diabetic retinopathy (DR) is a serious diabetes complication that can lead to vision loss, making timely identification crucial. Existing data-drive...

DAU-Net: a novel U-Net with dual attention for retinal vessel segmentation.

In fundus images, precisely segmenting retinal blood vessels is important for diagnosing eye-related conditions, such as diabetic retinopathy and hype...

Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study.

BACKGROUND: Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists use personalized and high...

A recurrent YOLOv8-based framework for event-based object detection.

Object detection plays a crucial role in various cutting-edge applications, such as autonomous vehicles and advanced robotics systems, primarily relyi...

ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus.

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This ch...

Systematic application of saliency maps to explain the decisions of convolutional neural networks for glaucoma diagnosis based on disc and cup geometry.

This paper systematically evaluates saliency methods as explainability tools for convolutional neural networks trained to diagnose glaucoma using simp...

DCTCNet: Sequency discrete cosine transform convolution network for visual recognition.

The discrete cosine transform (DCT) has been widely used in computer vision tasks due to its ability of high compression ratio and high-quality visual...

Early Prediction of Cardio Vascular Disease (CVD) from Diabetic Retinopathy using improvised deep Belief Network (I-DBN) with Optimum feature selection technique.

Cardio Vascular Disease (CVD) is one of the leading causes of mortality and it is estimated that 1 in 4 deaths happens due to it. The disease prevalen...

Advancements and Prospects in Nanorobotic Applications for Ophthalmic Therapy.

This study provides a bibliometric and bibliographic review of emerging applications of micro- and nanotechnology in treating ocular diseases, with a ...

Enhanced AMD detection in OCT images using GLCM texture features with Machine Learning and CNN methods.

Global blindness is substantially influenced by age-related macular degeneration (AMD). It significantly shortens people's lives and severely impairs ...

Glaucoma detection and staging from visual field images using machine learning techniques.

PURPOSE: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (V...

Method for assessing visual saliency in children with cerebral/cortical visual impairment using generative artificial intelligence.

Cerebral/cortical visual impairment (CVI) is a leading cause of pediatric visual impairment in the United States and other developed countries, and is...