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> Disorders > Disease or Syndrome > Geographic Atrophy

Geographic Atrophy

Geographic Atrophy (GA) is an advanced form of age-related macular degeneration, characterized by gradual loss of retinal pigment epithelium and photoreceptors in the macula.
This progressive condition can lead to central vision impairment and blindness.
PubCompare.ai offers an innovative approach to GA research, leveraging AI-driven comparisons of the latest literature, preprints, and patents.
This platform enables researchers to identify the most effective protocols and products, optimizing reproducibility and accuracy.
Advance your GA studies to the next level with the powerful insights provided by PubCompare.ai.

Most cited protocols related to «Geographic Atrophy»

1
Age-Related Eye Disease Study 2

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Publication 2012
beta Carotene Diet Disease Progression Geographic Atrophy Lung Cancer Lutein Omega-3 Fatty Acids Pathologic Neovascularization Placebos Xanthophylls Zeaxanthin Zinc
2
Leveraging Cross-Sectional OCTA Imaging
To date most OCTA literature has emphasized using en face OCTA viewing because of its natural correspondence with fundus imaging. However, en face OCTA is extremely susceptible to segmentation errors in the retinal layers, especially in eyes with pathology. Retinal segmentation strategies are inherently based on the anatomy of the normal retina. Normal retinas have well defined variations in architecture, while a retina with pathology can be abnormal in a myriad of ways. Given the complexity of pathologies such as edema, cyst formation, subretinal fluid, pigment epithelial detachment, neovascularization and geographic atrophy, it is currently virtually impossible for a computer algorithm to accurately identify retinal layers in the presence of all types of pathology. For this reason, viewing cross-sectional OCT scans with an OCTA flow overlay is very helpful. These OCTA B-scans typically show flow in color over a grayscale OCT image. Cross sectional viewing using B-scans can help obviate dependence on en face imaging with inherent segmentation errors and also be used to evaluate for the potential for projection artifacts. Fig. 9 shows an example of OCT and OCTA en face vs B-scan visualization of macular neovascularization (MNV) lesions with both classic and occult components. En face OCT (A) and OCTA images with a full projection (B) vs projection through the depths spanned by the lesion (E) are shown along with a cross sectional OCTA (G) and OCT (H). FA shows the classic component of the lesion (C) and ICGA the occult component (D). Panels I though L show the OCTA signal overlaid on the OCT structural image in orthoplane view (I), cross sections (J, K) and en face (L). Cross sectional OCTA images enable the location of vascular pathology to be assessed without possible segmentation errors which can cause artifacts in en face OCTA images. This approach is especially powerful because it is possible to display structural OCT cross sectional images which are intrinsically co-registered with the OCTA cross sections. The advantage of examining cross sectional images is the precise visualization of flow information versus depth in the tissue. The disadvantages are the need to scroll through numerous images and the absence of an image showing the entire extent of neovascularization in an en face sense. At the same time, if there are questionable findings in en face OCTA images, it is important to review cross sectional OCT and OCTA images in order to check for possible segmentation errors, projection artifacts or attenuation artifacts.
Commercial OCT instruments have implemented different methods for visualizing OCTA and OCT data. Fig. 10 shows examples of the graphic user interfaces for several instruments. Multiple pane displays typically show en face OCTA projections of different retinal layers as well as registered cross sectional OCT images. En face OCT projections are sometimes color coded according to layer or depth and overlaid to facilitate rapid interpretation. Cross sectional OCTA images are less frequently displayed, but are gaining popularity. Segmentation errors, which can cause errors in en face OCTA, remain a challenging problem in situations where pathology distorts the normal retinal architecture.
Spaide R.F., Fujimoto J.G., Waheed N.K., Sadda S.R, & Staurenghi G. (2017). Optical coherence tomography angiography. Progress in retinal and eye research, 64, 1-55.
Publication 2017
Blood Vessel Cyst Edema Eye Face Geographic Atrophy Macula Lutea Pathologic Neovascularization Radionuclide Imaging Retina Retinal Pigment Epithelial Detachment Sub-Retinal Fluid Tissues
3
Automated EZ Mapping for Outer Retinal Evaluation
Cleveland Clinic IRB-approval was obtained for a retrospective assessment of
eyes undergoing SDOCT testing with a novel EZ mapping tool. Initial conditions
selected for evaluation included normal controls, geographic atrophy secondary to
age-related macular degeneration, hydroxychloroquine toxicity, ocriplasmin-related
EZ attenuation, and outer retinal dynamics following membrane peeling. These
conditions were selected due to the predominance of outer retinal alterations.
Additionally, for initial assessment purposes, conditions with minimal RPE
disturbance were selected.
An automated EZ mapping tool was developed at the Ophthalmic Imaging Center
at Cleveland Clinic through collaboration with ImageIQ (Cleveland, OH) for
segmenting the EZ with additional retinal layers, providing linear, area, and
volumetric measurements, as well as en face visualization for
evaluating EZ and outer retinal dynamics (Figure
1). In brief, the macular cube data set was imported into a novel OCT
automated segmentation tool. Automated segmentation and mapping was performed. The
SDOCT image stack was passed through multiple filters and the RPE was identified as
the floor for the algorithm segmentation. Utilizing a prespecified search area, the
EZ was identified, if present, based on relative intensity, proximity and relative
location of the outer retinal bands. Manual independent reviewer of the segmentation
accuracy was performed for each of the eyes. To evaluate reliability of the
algorithm, multiple time points in normal samples were assessed and compared for
variability. The data set was the then transformed into multiple output components.
Cross-sectional area and cubic volumetric data was generated related to the EZ-RPE
thickness. Three-dimensional reconstruction was also performed of the entire dataset
with embedded EZ-mapping for visualization of areas of pathology. Finally,
en face EZ thickness topographic maps were created for each
dataset. Zeiss Cirrus SD-OCT system was utilized for all normal, AMD,
hydroxychlorquine, and ocriplasmin eyes with a 6 mm × 6 mm macular cube. The
intraoperative OCT scan assessment following membrane peeling was performed with the
Bioptigen Envisu system with a 10 mm × 10 mm macular cube.
Itoh Y., Vasanji A, & Ehlers J.P. (2015). Volumetric Ellipsoid Zone Mapping for Enhanced Visualization of Outer Retinal Integrity with Optical Coherence Tomography. The British journal of ophthalmology, 100(3), 295-299.
Publication 2015
Age-Related Macular Degeneration Eye Face Geographic Atrophy Hydroxychloroquine Macula Lutea Microtubule-Associated Proteins ocriplasmin Radionuclide Imaging Reconstructive Surgical Procedures Retina Tissue, Membrane Training Programs
4
Grading Scar Characteristics in Wet AMD

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Publication 2013
Angiography Blood Vessel Choroid Cicatrix Epiretinal Membrane Fibrosis Fingers Fluorescence Geographic Atrophy Hemorrhage Pathologic Neovascularization Photoreceptor Cells POU2F2 protein, human Radionuclide Imaging Retina Retinal Pigment Epithelial Detachment Serum Stains Sub-Retinal Fluid Tissue, Membrane Tissues Visual Acuity
5
Age-Related Macular Degeneration Clinical Trial

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Publication 2012
Age-Related Macular Degeneration Bevacizumab Choroidal Neovascularization Ethics Committees, Research Fibrosis Fluorescein Angiography Geographic Atrophy Hemorrhage Histoplasmosis Investigational New Drugs Medical Devices Myopia, Degenerative Operative Surgical Procedures Pathologic Neovascularization Patients Pharmaceutical Preparations Retina Retinal Diseases Retinal Pigment Epithelial Detachment Retinal Pigment Epithelium Retinal Pigments sequels Tears Therapies, Investigational Tomography, Optical Coherence Vision Visual Acuity Wounds and Injuries

Most recents protocols related to «Geographic Atrophy»

1
Eye Movement Patterns in Visual Impairment
We recruited five patients with moderately advanced RP, six patients with bilateral geographic atrophy due to late atrophic AMD, and seven healthy control subjects with normal vision (NV). The patients with RP we selected were required to have some remaining central vision in each eye. Central visual acuity in these patients was between 20/400 and 20/32 (see Table 1). Adhering to the Beckman classification,29 (link) the patients with AMD had to be 50 years or older with bilateral geographic atrophy. Their monocular visual acuities ranged between 20/32 and 20/800 (see Table 1). Controls (NV) had no eye disease, nor cause for reduced visual acuity, and had a normal or corrected to normal visual acuity of 20/25 or better in either eye.
Exclusion criteria for all three subgroups were (i) any diagnosis of neuro-muscular disorder likely to affect oculomotor control (i.e. previous stroke, Parkinson's disease, muscular dystrophy, or multiple sclerosis), and (ii) pathological nystagmus. Their ages ranged from 41 to 84 years old (see Table 1), however, all patients with AMD were 50 years or older, by definition.
The subjects gave informed consent in writing and had an ophthalmologic examination, including Goldmann perimetry (size V and III target) or Macular Integrity Assessment (MAIA; CentreVue) to assess the visual field, and multi-modal imaging including non-dilated color fundus photography (CFP; Canon CR6-5NM non-mydriatic camera, Japan), near infrared (NIR; Heidelberg Spectralis, Germany), and optical coherence tomography (OCT; Heidelberg Spectralis, Germany) imaging. Behavioral eye movement data were collected following ocular examination, with a break of at least 15 minutes to ensure no ongoing aftereffects from the imaging.
The study was reviewed and approved by the human research ethics committees of the Royal Victorian Eye and Ear Hospital and the Bionics Institute, and conducted in accordance with the Declaration of Helsinki, with all participants providing informed consent.
Guadron L., Titchener S.A., Abbott C.J., Ayton L.N., van Opstal J., Petoe M.A, & Goossens J. (2023). The Saccade Main Sequence in Patients With Retinitis Pigmentosa and Advanced Age-Related Macular Degeneration. Investigative Ophthalmology & Visual Science, 64(3), 1.
Publication 2023
Atrophy Cerebrovascular Accident Diagnosis Ethics Committees, Research Eye Disorders Eye Movements Geographic Atrophy Healthy Volunteers Homo sapiens Macula Lutea Multiple Sclerosis Muscular Dystrophy Mydriatics Myopathy Parkinson Disease Pathologic Nystagmus Patients Perimetry Tomography, Optical Coherence Vision Visual Acuity
2
ARMD Grading using Fundus Images
The assessment of ARMD was done by fundus images and graded by the modified Wisconsin Age-Related Maculopathy Grading Classification Scheme [28 (link),29 (link)]. Early ARMD was defined by the presence of numerous small (<63 µm) or intermediate (63–125 µm) drusen. Intermediated ARMD was characterized by either extensive drusen of small or intermediate size, or any large size (≥125 µm) drusen. Late ARMD was characterized by the presence of geographic atrophy or exudative macular degeneration or both. All fundus images were captured by an ophthalmic digital imaging system (CR6–45NM; Canon USA, Inc) and digital camera (EOS 10D; Canon USA, Inc). These images were graded by at least two trained examiners. If the first two examiners did not agree on a diagnosis, a third senior examiner could adjudicate any disagreements.
Chen Y.Y, & Chen Y.J. (2023). The Relationship between Dietary Calcium and Age-Related Macular Degeneration. Nutrients, 15(3), 671.
Publication 2023
Age-Related Macular Degeneration Diagnosis Fingers Geographic Atrophy
3
Plasma Metabolomics in Retinal Disorders
To assess the utility of our results when exploring systemic metabolic profiles of retinal disorders, we collected metabolic abundances from plasma of 351 participants. Of these 205 were AMD patients, while the rest were age-matched controls without AMD. AMD patients were divided into three sub-disease categories according to their retinal diagnosis, patients with choroidal neovascularization (CNV), geographic atrophy (GA) and patients with CNV and GA (mixed). This data consisted of 127 CNV, 45 GA and 33 Mixed AMD patients. The metabolic measurements were processed in 12 batches. The participants presented an average age of 78.2 years (sd = 7.31) with 54% females and 46% males. For each sample, we received abundances for 1403 metabolites, 431 of which were discarded a priori given that they were not defined as specific metabolites by Metabolon Inc.. Metabolic missingness rate was 14% (sd = 27%) among all samples with a similar missingness rate between healthy individuals (13.8%) and all the AMD cases (CNV 13.7%, GA 13.5%, Mixed 14.4%). Missingness varied across metabolites (Table S3).
Bonelli R., Woods S.M., Lockwood S., Bishop P.N., Khan K.N., Bahlo M., Ansell B.R, & Fruttiger M. (2023). Spatial distribution of metabolites in the retina and its relevance to studies of metabolic retinal disorders. Metabolomics, 19(2), 10.
Publication 2023
Choroidal Neovascularization Diagnosis Females Geographic Atrophy Males Metabolic Profile Patients Plasma Retina Retinal Diseases
4
Cascading Neural Networks for Automated AMD Severity Assessment
Those images deemed to be of good quality were used to train three individual disease state neural networks: 1) an Advanced AMD classifier, 2) a Drusen size classifier and 3) a pigmentary abnormalities classifier (see below for details). An important component of the current study was to determine the optimal arrangement of these three component classifiers that would most accurately reproduce the 5-step Simplified Severity Scale, so in addition to building a model that utilized the three component classifiers reading any given image simultaneously, we also created a number of models that utilized more complex interconnecting component classifiers. The best outcome was achieved using a “cascading” neural network hierarchy, where individual neural networks, each focused on one component of the AREDS clinical variable labels, feed data into each other. The architecture of cascading CNNs is shown in Figure 2. In the first pass, the image was interrogated by the Advanced AMD classifier and if the image was identified as having features of aAMD; either geographic atrophy or neovascularization, no further analysis was performed. If aAMD was not detected the image was then passed through to the Drusen Size and Pigmentary Abnormality classifiers. We further identified that the drusen size network had difficulty differentiating between none/small and medium drusen (see results). With reference to the AREDS 5-step Simplified Severity Scale (AREDS 18)6 (link) the finding of medium drusen is only relevant if large drusen are not detected in either eye. To determine drusen size, all images were therefore first passed through a binary CNN which classified images as either “non-large v large drusen”. Images were only then passed to a second binary “none\small v medium drusen” classifier if no large drusen were detected. The results generated by the three disease classifiers were then used to calculate an individual’s score on the 5-step severity scale as illustrated in Supplementary Figure 2.

The “cascading” architecture, where the patient-level image set is split into left and right eye images. These images then progress through an aAMD detection neural network and if negative, through the Drusen Size and Pigmentary Abnormality detection neural networks. The Simplified Severity Score is then calculated depending on the image-level results, which is then aggregated back to the patient-level.

Xie L., Vaghefi E., Yang S., Han D., Marshall J, & Squirrell D. (2023). Automation of Macular Degeneration Classification in the AREDS Dataset, Using a Novel Neural Network Design. Clinical Ophthalmology (Auckland, N.Z.), 17, 455-469.
Publication 2023
Congenital Abnormality Geographic Atrophy Pathologic Neovascularization Patients Pigmentation
5
Macular Atrophy Subtypes Characterization
The study included patients with macular atrophy secondary to EMAP, DTGA, or nDTGA. Diagnosis of EMAP was defined when all the following criteria were met: (1) presence of macular atrophy with symmetric, polycyclic borders, major vertical axis; (2) pseudodrusen-like deposits surrounding the atrophic lesion; (3) peripheral paving-stone degeneration; (4) age under 55 years at the onset of visual symptoms (either nyctalopia, reduced vision or methamorphopsias).
Patients diagnosed with macular atrophy due to AMD were sub-classified on the basis of their appearance on FAF imaging as described by Holz et al.4 In brief, patients were assigned to the DTGA group if the atrophic area met all of the following criteria: (1) age of at least 55 years at symptom onset; (2) diffuse increased AF signal extending beyond the edges of the atrophy; (3) coalescent, greyish, lobular aspect.
AMD patients with macular atrophy aspect not matching criteria (2) or (3) were assigned to the nDTGA group (Fig. 1).

Multimodal imaging of each condition. Blue-light fundus autofluorescence and optical coherence tomography of (A, D) Extensive macular atrophy with pseudodrusen-like appearance, (B, E) diffuse-trickling geographic atrophy and (C, F) non-diffuse-trickling geographic atrophy.

A senior grader (M.B.P.) confirmed the diagnosis of cases that met the requirements of one of the three groups.
Exclusion criteria were: minimum follow-up of less than 12 months, high media opacity, fixation inadequate for high-quality imaging, systemic or other ocular conditions known to cause chorioretinal atrophy, history of neovascular complications, or administration of intravitreal anti-VEGF agents. When both eyes of the same patients were eligible for inclusion in the study, only a single eye was randomly selected.
Antropoli A., Arrigo A., Bianco L., Berni A., Lamberto L.F., Saladino A., Bandello F, & Battaglia Parodi M. (2023). Quantitative multimodal imaging of extensive macular atrophy with pseudodrusen and geographic atrophy with diffuse trickling pattern. Scientific Reports, 13, 1822.
Publication 2023
Anetoderma Anti-Anxiety Agents Atrophy Calculi Diagnosis Epistropheus Geographic Atrophy Light Low Vision Nyctalopia Pathologic Neovascularization Patients Tomography, Optical Coherence Transposition of the Great Arteries, Dextro-Looped 1 Vascular Endothelial Growth Factors Vision

Top products related to «Geographic Atrophy»

1
Spectralis hra oct by Heidelberg Engineering
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The Spectralis HRA+OCT is a multimodal imaging device that combines high-resolution fundus imaging with optical coherence tomography (OCT) technology. It allows for the simultaneous acquisition of detailed images of the retina, choroid, and optic nerve.
2
Trc nw6 by Topcon
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The TRC-NW6S is a retinal camera designed for non-mydriatic digital imaging of the eye fundus. It captures high-quality retinal images without the need for pupil dilation.
3
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The Spectralis is an optical coherence tomography (OCT) imaging device developed by Heidelberg Engineering. It captures high-resolution, cross-sectional images of the retina and optic nerve using near-infrared light. The Spectralis provides detailed structural information about the eye, which can aid in the diagnosis and management of various eye conditions.
4
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Eos 10d by Canon
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The EOS 10D is a digital single-lens reflex (DSLR) camera manufactured by Canon. It features a 6.3-megapixel CMOS sensor and can capture images at a resolution of 3,072 x 2,048 pixels. The camera supports a wide range of ISO sensitivities, from 100 to 3,200, and has a 7-point autofocus system. The EOS 10D is capable of continuous shooting at a rate of 3 frames per second.
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The Cirrus HD-OCT is a diagnostic imaging device developed by Zeiss. It utilizes optical coherence tomography (OCT) technology to capture high-resolution, cross-sectional images of the eye's internal structures, including the retina, optic nerve, and anterior segment. The device provides detailed visualization of these structures, enabling healthcare professionals to assess and monitor various ocular conditions.
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Eos 10d digital camera by Canon
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The Canon EOS 10D is a digital single-lens reflex (DSLR) camera. It features a 6.3-megapixel CMOS sensor, a 7-point autofocus system, and a maximum ISO sensitivity of 3200. The camera can capture images in JPEG and RAW file formats and supports a range of interchangeable Canon EF lenses.
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Trc 50dx retinal camera by Topcon
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More about "Geographic Atrophy"

Geographic Atrophy (GA) is an advanced form of age-related macular degeneration (AMD), a progressive eye condition characterized by the gradual loss of retinal pigment epithelium and photoreceptors in the macula.
This deterioration can lead to central vision impairment and, in severe cases, blindness.
Researchers studying GA can leverage innovative tools like PubCompare.ai, which utilizes AI-driven comparisons of the latest literature, preprints, and patents to identify the most effective protocols and products.
This platform enables researchers to optimize the reproducibility and accuracy of their GA studies, taking their research to the next level.
Spectral-domain optical coherence tomography (SD-OCT) imaging techniques, such as Spectralis HRA+OCT and Cirrus HD-OCT, play a crucial role in the diagnosis and monitoring of GA.
These advanced imaging modalities allow clinicians and researchers to visualize the structural changes in the retina associated with GA.
Similarly, fundus photography using devices like the TRC-NW6S and the TRC-50DX retinal camera can provide valuable insights into the progression of the disease.
In addition to imaging techniques, researchers may also leverage tools like RNAlater, a reagent used for the stabilization and preservation of RNA in tissue samples, to support their GA studies.
Furthermore, statistical analysis software like Stata 15 can be employed to rigorously analyze the data collected during GA research.
By combining the power of PubCompare.ai's AI-driven literature comparisons with cutting-edge imaging technologies and robust data analysis tools, researchers can drive their GA studies to new heights, ultimately improving our understanding of this debilitating condition and paving the way for more effective treatments.