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Purpose: To maintain visual fields and quality of life over a lifetime, medical practice must be conducted taking into consideration not only visual field progression but also future visual field changes that occur over the patients' expected lifespan. The purpose of this study is to investigate the feasibility of establishing a model that predicts prognosis, estimating the proportion of glaucoma patients with severe visual field defects.
Patients And Methods: The data of 191 patients with primary open-angle glaucoma, with a predominance of normal-tension glaucoma, were used for this study. The model was developed based on patients' backgrounds and risk factors, using Monte Carlo simulation. A "severe visual field defect" was defined as ≤-20 dB. The mean deviation (MD) value for 10,000 virtual patients in each simulation pattern (144 patterns) was calculated using a predictive formula to estimate the MD slope, and the effects of risk factors and intraocular pressure (IOP) reduction on the proportion of patients with severe visual field defects were evaluated.
Results: Younger age, later-stage disease, more severe glaucomatous structural abnormalities and the presence of disc hemorrhage were associated with an increase in the progression rate of patients with severe visual field defects. Conversely, lower IOP was associated with a decrease in this rate.
Conclusion: Combining regression analysis with Monte Carlo simulation could be a useful method for developing predictive models of prognosis in glaucoma patients.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7360425 | PMC |
http://dx.doi.org/10.2147/OPTH.S247618 | DOI Listing |
ObjectiveThis work examined performance costs for a spatial integration task when two sources of information were presented at increasing eccentricities with an augmented-reality (AR) head-mounted display (HMD).BackgroundSeveral studies have noted that different types of tasks have varying costs associated with the spatial proximity of information that requires mental integration. Additionally, prior work has found a relatively negligible role of head movements associated with performance costs.
View Article and Find Full Text PDFPLoS Comput Biol
September 2025
Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America.
Research into the mechanisms underlying neuromodulation by tES using in-vivo animal models is key to overcoming experimental limitations in humans and essential to building a detailed understanding of the in-vivo consequences of tES. Insights from such animal models are needed to develop targeted and effective therapeutic applications of non-invasive brain stimulation in humans. The sheer difference in scale and geometry between animal models and the human brain contributes to the complexity of designing and interpreting animal studies.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
September 2025
Human beings have the ability to continuously analyze a video and immediately extract the motion components. We want to adopt this paradigm to provide a coherent and stable motion segmentation over the video sequence. In this perspective, we propose a novel long-term spatio-temporal model operating in a totally unsupervised way.
View Article and Find Full Text PDFCereb Cortex
August 2025
Nencki Institute of Experimental Biology, PAS, 3 Pasteur Street, 02-093 Warsaw, Poland.
In the visual cortices, receptive fields (RFs) are arranged in a gradient from small sizes in the center of the visual field to the largest sizes at the periphery. Using functional magnetic resonance imaging (fMRI) mapping of population RFs, we investigated RF adaptation in V1, V2, and V3 in patients after long-term photoreceptor degeneration affecting the central (Stargardt disease [STGD]) and peripheral (Retinitis Pigmentosa [RP]) regions of the retina. In controls, we temporarily limited the visual field to the central 10° to model peripheral loss.
View Article and Find Full Text PDFJ Glaucoma
September 2025
Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, CA, United States.
Precis: Artificial intelligence applied to OCTA images demonstrated high accuracy in estimating 24-2 visual field maps by leveraging information from pararpapillary area.
Purpose: To develop deep learning (DL) models estimating 24-2 visual field (VF) maps from optical coherence tomography angiography (OCTA) optic nerve head (ONH) en face images.
Methods: A total of 3148 VF OCTA pairs were collected from 994 participants (1684 eyes).