AIMC Topic: Fundus Oculi
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Deep learning-based automatic image quality assessment in ultra-widefield fundus photographs.
BMJ open
May 21, 2025
OBJECTIVE: With a growing need for ultra-widefield fundus (UWF) fundus photographs in clinics and AI development, image quality assessment (IQA) of UWF fundus photographs is an important preceding step for accurate diagnosis and clinical interpretati...
Accelerated Biological Aging in Exfoliation Glaucoma Assessed by Fundus-Derived Predicted Age and Advanced Glycation End Products.
International journal of molecular sciences
May 15, 2025
Glaucoma is an age-related neurodegenerative disease characterized by progressive optic nerve damage. Accelerated biological aging, assessed using predicted age derived from fundus images, may serve as a biomarker for glaucoma progression. This study...
Deep Learning for Retinal Image Quality Assessment of Optic Nerve Head Disorders.
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
May 13, 2025
Deep learning (DL)-based retinal image quality assessment (RIQA) algorithms have been gaining popularity, as a solution to reduce the frequency of diagnostically unusable images. Most existing RIQA tools target retinal conditions, with a dearth of st...
Validation of artificial intelligence algorithm LuxIA for screening of diabetic retinopathy from a single 45° retinal colour fundus images: the CARDS study.
BMJ open ophthalmology
May 8, 2025
OBJECTIVE: This study validated the artificial intelligence (AI)-based algorithm LuxIA for screening more-than-mild diabetic retinopathy (mtmDR) from a single 45° colour fundus image of patients with diabetes mellitus (DM, type 1 or type 2) in Spain....
Grading of Foveal Hypoplasia Using Deep Learning on Retinal Fundus Images.
Translational vision science & technology
May 1, 2025
PURPOSE: This study aimed to develop and evaluate a deep learning model for grading foveal hypoplasia using retinal fundus images.
Relationships Between Retinal Vascular Characteristics and Systemic Indicators in Patients With Diabetes Mellitus.
Investigative ophthalmology & visual science
Apr 1, 2025
PURPOSE: To develop a deep learning method for vessel segmentation in fundus images, measure retinal vessels, and study the connection between retinal vascular features and systemic indicators in diabetic patients.
Artificial Intelligence Versus Rules-Based Approach for Segmenting NonPerfusion Area in a DRCR Retina Network Optical Coherence Tomography Angiography Dataset.
Investigative ophthalmology & visual science
Mar 3, 2025
PURPOSE: Loss of retinal perfusion is associated with both onset and worsening of diabetic retinopathy (DR). Optical coherence tomography angiography is a noninvasive method for measuring the nonperfusion area (NPA) and has promise as a scalable scre...
Retinal Arteriovenous Information Improves the Prediction Accuracy of Deep Learning-Based baPWV Index From Color Fundus Photographs.
Investigative ophthalmology & visual science
Feb 3, 2025
PURPOSE: To compare the prediction accuracy of brachial-ankle pulse wave velocity (baPWV) from color fundus photographs (CFPs) using different deep learning models.
ASSESSMENT OF ARTIFICIAL INTELLIGENCE SOFTWARE FOR AUTOMATIC SCREENING OF DIABETIC RETINOPATHY BASED ON FUNDUS PHOTOGRAPHS IN MELANODERM SUBJECTS.
Retina (Philadelphia, Pa.)
Feb 1, 2025
PURPOSE: To assess the Gaiha Prio Retino +™ Artificial Intelligence (AI) software for detecting diabetic retinopathy (DR).
Noninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings.
Translational vision science & technology
Jan 2, 2025
PURPOSE: The purpose of this study was to develop and validate a deep-learning model for noninvasive anemia detection, hemoglobin (Hb) level estimation, and identification of anemia-related retinal features using fundus images.
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