Transl Vis Sci Technol
Department of Ophthalmology, Hyogo College of Medicine, Hyogo, Japan.
Published: October 2020
Purpose: The sunset glow fundus (SGF) appearance in Vogt-Koyanagi-Harada (VKH) disease was evaluated by means of adaptive binarization of patients' fundus photographs.
Methods: Twenty-nine Japanese patients with acute VKH were enrolled in this study. We evaluated one eye of each patient, and thereby divided the patients into two groups; SGF+ and SGF- at 6 months after treatment. We compared patient age, gender, and spherical equivalent refractive error (SERE) and choroidal thickness measured using optical coherence tomography. We also compared the choroidal vascular appearance index (CVAI), derived by adaptive binarization image processing of fundus photographs, between the two groups. Measurements of choroidal thickness and CVAI were taken at the onset of disease, and 1, 3, and 6 months after treatment. The sunset glow index (SGI), as previously reported, was calculated using color fundus photographs, and compared to the CVAI.
Results: Eight patients (27.6%) were categorized into the SGF+ group. At all time points, the mean CVAI in the SGF+ group was significantly greater than that in the SGF- group. No significant difference was observed in choroidal thicknesses at any time point. The SGI was significantly greater in the SGF+ group at 6 months.
Conclusions: CVAI could be a new predictive biomarker for the development of SGF in patients with VKH disease.
Translational Relevance: Detecting SGF is important for management of patients with VKH, and CVAI may indicate the possibility of developing into SGF, although the color fundus photographs do not yet show SGF at that time.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7552939 | PMC |
http://dx.doi.org/10.1167/tvst.9.11.10 | DOI Listing |
Ophthalmology
January 2025
University of Bordeaux, INSERM, BPH, U1219, F-33000 Bordeaux, France; FRCRnet, F-CRIN network, France.
Purpose: We assessed the associations of macular layer thicknesses, measured using spectral-domain OCT (SD-OCT), with incident age-related macular degeneration (AMD) and AMD polygenic risk scores (PRS).
Design: Population-based cohort study PARTICIPANTS: 653 participants of the Alienor study, with biennial eye imaging from 2009 to 2024.
Methods: Macular layer thicknesses of eight distinct layers and three compound layers were automatically segmented based on SD-OCT imaging of the macula.
Am J Hypertens
January 2025
3rd Department of Internal Medicine, Papageorgiou Hospital, Aristotle University of Thessaloniki, Greece.
Background: Changes in retinal vessel caliber are crucial for detecting early retinopathy, a significant cause of blindness in individuals with Diabetes Mellitus type 2 (T2DM). This study aims to evaluate the changes in retinal vessel caliber and identify factors associated with these changes in recently diagnosed T2DM patients.
Methods: The study included newly diagnosed T2DM patients (within 6 months of diagnosis) who were free of antidiabetic treatment (except metformin) and matched individuals based on age and blood pressure (BP).
JAMA Netw Open
January 2025
Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Importance: Determining spectacle-corrected visual acuity (VA) is essential when managing many ophthalmic diseases. If artificial intelligence (AI) evaluations of macular images estimated this VA from a fundus image, AI might provide spectacle-corrected VA without technician costs, reduce visit time, or facilitate home monitoring of VA from fundus images obtained outside of the clinic.
Objective: To estimate spectacle-corrected VA measured on a standard eye chart among patients with diabetic macular edema (DME) in clinical practice settings using previously validated AI algorithms evaluating best-corrected VA from fundus photographs in eyes with DME.
Transl Vis Sci Technol
January 2025
Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
Purpose: To evaluate the refractive differences among school-aged children with macular or peripapillary fundus tessellation (FT) distribution patterns, using fundus tessellation density (FTD) quantified by deep learning (DL) technology.
Methods: The cross-sectional study included 1942 school children aged six to 15 years, undergoing ocular biometric parameters, cycloplegic refraction, and fundus photography. FTD was quantified for both the macular (6 mm) and peripapillary (4 mm) regions, using DL-based image processing applied to 45° color fundus photographs.
Mayo Clin Proc Digit Health
December 2024
School of Computed and Augmented Intelligence, Arizona State University, Tempe, AZ.
Objective: To report the development and performance of 2 distinct deep learning models trained exclusively on retinal color fundus photographs to classify Alzheimer disease (AD).
Patients And Methods: Two independent datasets (UK Biobank and our tertiary academic institution) of good-quality retinal photographs derived from patients with AD and controls were used to build 2 deep learning models, between April 1, 2021, and January 30, 2024. ADVAS is a U-Net-based architecture that uses retinal vessel segmentation.
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