AIMC Topic: Fundus Oculi
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Evaluating real-world performance of an automated offline glaucoma AI on a smartphone fundus camera across glaucoma severity stages.
PloS one
Jun 26, 2025
PURPOSE: Leveraging an artificial intelligence system (AI) for glaucoma screening can mitigate the current challenges and provide prompt detection and management crucial in averting irreversible blindness. The study reports the real-world performance...
Realistic fundus photograph generation for improving automated disease classification.
The British journal of ophthalmology
Jun 23, 2025
AIMS: This study aims to investigate whether denoising diffusion probabilistic models (DDPMs) could generate realistic retinal images, and if they could be used to improve the performance of a deep convolutional neural network (CNN) ensemble for mult...
Research on the correlation between retinal vascular parameters and axial length in children using an AI-based fundus image analysis system.
PloS one
Jun 17, 2025
OBJECTIVE: This study aims to utilize artificial intelligence technology to conduct an in-depth analysis of fundus data from myopic children and adolescents, thoroughly exploring the correlation between retinal vascular parameters and axial length (A...
A Lesion-Fusion Neural Network for Multi-View Diabetic Retinopathy Grading.
IEEE journal of biomedical and health informatics
May 6, 2025
As the most common complication of diabetes, diabetic retinopathy (DR) is one of the main causes of irreversible blindness. Automatic DR grading plays a crucial role in early diagnosis and intervention, reducing the risk of vision loss in people with...
Latent space autoencoder generative adversarial model for retinal image synthesis and vessel segmentation.
BMC medical imaging
May 5, 2025
Diabetes is a widespread condition that can lead to serious vision problems over time. Timely identification and treatment of diabetic retinopathy (DR) depend on accurately segmenting retinal vessels, which can be achieved through the invasive techni...
A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images.
Scientific reports
Apr 30, 2025
Recent advancements in deep learning have significantly impacted medical image processing domain, enabling sophisticated and accurate diagnostic tools. This paper presents a novel hybrid deep learning framework that combines convolutional neural netw...
General retinal image enhancement via reconstruction: Bridging distribution shifts using latent diffusion adaptors.
Medical image analysis
Apr 26, 2025
Deep learning-based fundus image enhancement has attracted extensive research attention recently, which has shown remarkable effectiveness in improving the visibility of low-quality images. However, these methods are often constrained to specific dat...
Optimized glaucoma detection using HCCNN with PSO-driven hyperparameter tuning.
Biomedical physics & engineering express
Apr 22, 2025
. This study is focused on creating an effective glaucoma detection system employing a Hybrid Centric Convolutional Neural Network (HCCNN) model. By using Particle Swarm Optimization (PSO), classification accuracy is increased and computing complexit...
Two-stage color fundus image registration via Keypoint Refinement and Confidence-Guided Estimation.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 19, 2025
Color fundus images are widely used for diagnosing diseases such as Glaucoma, Cataracts, and Diabetic Retinopathy. The registration of color fundus images is crucial for assessing changes in fundus appearance to determine disease progression. In this...
Acceptability, applicability, and cost-utility of artificial-intelligence-powered low-cost portable fundus camera for diabetic retinopathy screening in primary health care settings.
Diabetes research and clinical practice
Apr 5, 2025
AIMS: To evaluate the acceptability, applicability, and cost-utility of AI-powered portable fundus cameras for diabetic retinopathy (DR) screening in Hong Kong, providing a viable alternative screening solution for resource-limited areas.
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