Purpose: To describe a method for monitoring progression of glaucoma using the multifocal visual evoked potential (mfVEP) technique.
Methods: Eighty-seven patients diagnosed with open-angle glaucoma were divided into two groups. Group I, comprised 43 patients who had a repeat mfVEP test within 50 days (mean 0.9 +/- 0.5 months), and group II, 44 patients who had a repeat test after at least 6 months (mean 20.7 +/- 9.7 months). Monocular mfVEPs were obtained using a 60-sector pattern reversal dartboard display. Monocular and interocular analyses were performed. Data from the two visits were compared. The total number of abnormal test points with P < 5% within the visual field (total scores) and number of abnormal test points within a cluster (cluster size) were calculated. Data for group I provided a measure of test-retest variability independent of disease progression. Data for group II provided a possible measure of progression.
Results: The difference in the total scores for group II between visit 1 and visit 2 for the interocular and monocular comparison was significant (P < 0.05) as was the difference in cluster size for the interocular comparison (P < 0.05). Group I did not show a significant change in either total score or cluster size.
Conclusion: The change in the total score and cluster size over time provides a possible method for assessing progression of glaucoma with the mfVEP technique.
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http://dx.doi.org/10.1007/s10633-008-9149-2 | DOI Listing |
J Imaging Inform Med
January 2025
College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.
This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.
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January 2025
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December 2024
Cornea and Refractive Surgery, Al-Shifa Trust Eye Hospital, Rawalpindi, PAK.
Background: Glaucoma, particularly open-angle glaucoma (OAG), is a leading cause of irreversible blindness, associated with optic nerve damage, retinal ganglion cell death, and visual field defects. Corneal biomechanical properties and cellular components, such as corneal nerve and keratocyte densities assessed by in vivo confocal microscopy (IVCM), may serve as biomarkers for glaucoma progression. This study aimed to explore the relationship between corneal nerve parameters, keratocyte density, and optical coherence tomography (OCT)-derived retinal nerve fiber layer (RNFL) thickness in primary open-angle glaucoma (POAG) patients and controls.
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Ocular Oncology Service, Institute of Oncology, Tecnologico de Monterrey, Monterrey, Mexico.
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Pharmaceutics
January 2025
Unidad de Investigación y Desarrollo en Tecnología Farmacéutica (UNITEFA), CONICET and Departamento de Ciencias Farmacéuticas, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Córdoba 5000, Argentina.
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