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Patients with age-related macular degeneration (AMD) have difficulty recognising people's faces. We tested whether this could be improved using caricaturing: an image enhancement procedure derived from cortical coding in a perceptual 'face-space'. Caricaturing exaggerates the distinctive ways in which an individual's face shape differs from the average. We tested 19 AMD-affected eyes (from 12 patients; ages 66-93 years) monocularly, selected to cover the full range of vision loss. Patients rated how different in identity people's faces appeared when compared in pairs (e.g., two young men, both Caucasian), at four caricature strengths (0, 20, 40, 60% exaggeration). This task gives data reliable enough to analyse statistically at the individual-eye level. All 9 eyes with mild vision loss (acuity ≥ 6/18) showed significant improvement in identity discrimination (higher dissimilarity ratings) with caricaturing. The size of improvement matched that in normal-vision young adults. The caricature benefit became less stable as visual acuity further decreased, but caricaturing was still effective in half the eyes with moderate and severe vision loss (significant improvement in 5 of 10 eyes; at acuities from 6/24 to poorer than <6/360). We conclude caricaturing has the potential to help many AMD patients recognise faces.
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http://dx.doi.org/10.1038/s41598-018-33543-3 | DOI Listing |
Invest Ophthalmol Vis Sci
September 2025
Center for Visual Science, University of Rochester, Rochester, NY, United States.
Purpose: Adaptive optics scanning light ophthalmoscopy (AOSLO) paired with intravitreal injection of a viral vector coding for the calcium indicator GCaMP has enabled visualization of neuronal activity in retinal ganglion cells (RGCs) at single cell resolution in the living eye. However, the inner limiting membrane (ILM) restricts viral transduction to the fovea in humans and non-human primates, hindering both therapeutic intervention and physiological study of the retina. To address this issue, we explored peeling the ILM before intravitreal injection to expand calcium imaging beyond the fovea in the living primate eye.
View Article and Find Full Text PDFInt J Surg
September 2025
Department of Ophthalmology, The First Affiliated Hospital of Dalian Medical University.
Diabetic retinopathy (DR) remains a leading cause of preventable blindness worldwide, with the affected population projected to reach 270 million by 2045. Our study analyzed 2 434 interventional trials registered between 2007 and 2024 in the Informa Pharma Intelligence database and found that anti-VEGF agents dominate the therapeutic landscape-bevacizumab represents 24.0 % of studies, ranibizumab 15.
View Article and Find Full Text PDFRadiol Adv
September 2024
Department of Radiology, Northwestern University and Northwestern Medicine, Chicago, IL, 60611, United States.
Background: In clinical practice, digital subtraction angiography (DSA) often suffers from misregistration artifact resulting from voluntary, respiratory, and cardiac motion during acquisition. Most prior efforts to register the background DSA mask to subsequent postcontrast images rely on key point registration using iterative optimization, which has limited real-time application.
Purpose: Leveraging state-of-the-art, unsupervised deep learning, we aim to develop a fast, deformable registration model to substantially reduce DSA misregistration in craniocervical angiography without compromising spatial resolution or introducing new artifacts.
J Healthc Sci Humanit
January 2024
Communications Manager for Richmond County, Chosen Church, Director of Care Team Ministry, | 706-394-3709.
In 2022, Dr. Ebony Michelle Collins-a scholar, author, and vision-health advocate-suffered sudden bilateral retinal detachment and blindness following a COVID-19 infection, despite no prior history of ocular disease. Her story reveals a largely overlooked consequence of the pandemic: the potential for serious neurological and ocular complications.
View Article and Find Full Text PDFJ Oral Biol Craniofac Res
August 2025
Neura Integrasi Solusi, Jl. Kebun Raya No. 73, Rejowinangun, Kotagede, Yogyakarta, 55171, Indonesia.
Background: Periodontal disease is an inflammatory condition causing chronic damage to the tooth-supporting connective tissues, leading to tooth loss in adults. Diagnosing periodontitis requires clinical and radiographic examinations, with panoramic radiographs crucial in identifying and assessing its severity and staging. Convolutional Neural Networks (CNNs), a deep learning method for visual data analysis, and Dense Convolutional Networks (DenseNet), which utilize direct feed-forward connections between layers, enable high-performance computer vision tasks with reduced computational demands.
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