Purpose: The recovery of visual sensitivity after a photobleach in early AMD is slowed in rods but cones also may be abnormal. The purpose of this article was to test different stimulus locations to investigate cone function and its relation to rod abnormalities.
Methods: Stimuli were presented at two locations, 3.0° and 5.5°, in the inferior visual field. Post photobleach dark adaptation (DA) curves from 50 early-AMD patients were compared with those from 15 healthy controls of similar age. Curves were characterized in terms of four parameters: ct, cone threshold; α, the transition point from cone to rod function; S2, the slope of the second rod-mediated component; and β, the transition from the second to the third rod-mediated component.
Results: There were strong location effects for the healthy group and the AMD group. Cone threshold was higher for the outer compared with the inner stimulus (P = 0.001), S2 was steeper for outer compared with inner (P < 0.001), α was shorter for outer (P = 0.004), and β was shorter for outer than inner (P = 0.002). The high variance in the patient data, particularly for α and β, explained the absence of a group*location interaction in the statistics.
Conclusions: The data provide a novel perspective on abnormal cone- and rod-sensitivity recovery in early dry AMD. The comparison of pairs of DA curves from different locations highlights the involvement of cones in the underlying pathology of AMD. Dynamic measures of visual function are particularly sensitive to early AMD.
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http://dx.doi.org/10.1167/iovs.18-24227 | DOI Listing |
Medicina (Kaunas)
December 2024
Ophthalmology Laboratory, Neuroscience Institute, Lithuanian University of Health Sciences, Medical Academy, Eiveniu 2, LT-50161 Kaunas, Lithuania.
: Age-related macular degeneration (AMD) is the leading cause of blindness, affecting millions worldwide. Its pathogenesis involves the death of the retinal pigment epithelium (RPE), followed by photoreceptor degeneration. Although AMD is multifactorial, various genetic markers are strongly associated with the disease and may serve as biomarkers for evaluating treatment efficacy.
View Article and Find Full Text PDFBioengineering (Basel)
November 2024
Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK.
Macular atrophy (MA) is an irreversible endpoint of age-related macular degeneration (AMD), which is the leading cause of blindness in the world. Early detection is therefore an unmet need. We have developed a novel automated method to identify MA in patients undergoing follow-up with optical coherence tomography (OCT) for AMD based on the combination of 2D and 3D Unet architecture.
View Article and Find Full Text PDFBioengineering (Basel)
November 2024
Department of Ophthalmology, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USA.
Eye diseases such as age-related macular degeneration (AMD) are major causes of irreversible vision loss. Early and accurate detection of these diseases is essential for effective management. Optical coherence tomography (OCT) imaging provides clinicians with in vivo, cross-sectional views of the retina, enabling the identification of key pathological features.
View Article and Find Full Text PDFBiomedicines
November 2024
Department of Ophthalmology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
: We investigated the prevalence of age-related macular degeneration (AMD) and associated risk factors in Korean subjects who underwent comprehensive health screening examinations. : This single health screening center-based cross-sectional study included a total of 73,574 consecutive participants older than 30 years who underwent a health screening examination, including fundus photography, between October 2003 and December 2010. Weighted prevalence and risk factors for AMD were evaluated.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast BT9 5BN, UK.
: Age-related macular degeneration (AMD) is a significant cause of vision loss in older adults, often progressing without early noticeable symptoms. Deep learning (DL) models, particularly convolutional neural networks (CNNs), demonstrate potential in accurately diagnosing and classifying AMD using medical imaging technologies like optical coherence to-mography (OCT) scans. This study introduces a novel CNN-based DL method for AMD diagnosis, aiming to enhance computational efficiency and classification accuracy.
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