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147 results for “Retinal diseases”
M3-OCTA:Leveraging Multimodal Fusion for Enhanced Diagnosis of Multiple Retinal Diseases in Ultra-wide OCTA
<p>Ultra-wide optical coherence tomography angiography (UW-OCTA) is an emerging imaging technique that offers significant advantages over traditional OCTA by providing an exceptionally wide scanning range of up to 24 x 20 mm^{2}, covering both the anterior and posterior regions of the retina. However, the currently accessible UW-OCTA datasets suffer from limited comprehensive hierarchical information and corresponding disease annotations. To address this limitation, we have curated the pioneering M3OCTA dataset, which is the first multimodal (i.e., multilayer), multi-disease, and widest field-of-view UW-OCTA dataset. Furthermore, the effective utilization of multi-layer ultra-wide ocular vasculature information from UW-OCTA remains underdeveloped. To tackle this challenge, we propose the first cross-modal fusion framework that leverages multi-modal information for diagnosing multiple diseases. Through extensive experiments conducted on our openly available M3OCTA dataset, we demonstrate the effectiveness and superior performance of our method, both in fixed and varying modalities settings. The construction of the M3OCTA dataset, the first multimodal OCTA dataset encompassing multiple diseases, aims to advance research in the ophthalmic image analysis community.</p> <p>Our proposed M3OCTA is the first multi-modal based ultra-wide retinal OCTA dataset, involving 1637 scans from 1046 eyes of 620 individuals imaged in Zigong First People’s Hospital through 24×20 scan mode. Specifically, 1067 scans contains choroid large vessel image; images of 1310 scans from 496 people are labeled as six classes in multi-label setting, including healthy, diabetic retinopathy (DR), diabetic macular edema (DME), Retinal Vein Occlusion (RVO), Hypertension (HBP) and Vitreous Hemorrhage (VH), and then split into train, validation and test set as 6:2:2. The remaining unlabeled data are only used in the pretraining step. Details of our M3OCTA and other public ones are listed in Table.1. Compared with others, M3OCTA dataset demonstrates superiorities in several aspects including the number of modalities, number of patients, image resolution, and FOV.</p> <p> </p> <p><strong>You can request this dataset through signing the attached agreement. The download link will send to you. </strong></p>
Data from: Signature of altered retinal microstructures and electrophysiology in schizophrenia spectrum disorders is associated with disease severity and polygenic risk
<p>This dataset contains supporting data for the publication: </p> <p>Boudriot, E.<em> et al.</em> Signature of altered retinal microstructures and electrophysiology in schizophrenia spectrum disorders is associated with disease severity and polygenic risk. <em>Biological Psychiatry</em><span> </span><a href="https://doi.org/10.1016/j.biopsych.2024.04.014">https://doi.org/10.1016/j.biopsych.2024.04.014</a></p> <p> </p> <p>Files:</p> <ul> <li><em>clinical.csv </em>contains data from clinical assessment and polygenic risk scores for schizophrenia</li> <li><em>ophthalmic_examination.csv </em>contains data on spherical equivalent, intraocular pressure and visual acuity</li> <li><em>oct.csv </em>contains segmentation output from Iowa Reference Algorithms</li> <li><em>erg.csv </em>contains pre-processed ERG data for the four ERG conditions</li> <li><em>mri.csv </em>contains ICV-corrected MRI volumes</li> </ul>
Evolution of retinal degeneration and prediction of disease activity in relapsing and progressive multiple sclerosis
<p><span>Retinal optical coherence tomography has been identified as biomarker for disease progression in relapsing-remitting multiple sclerosis (RRMS), while the dynamics of retinal atrophy in progressive MS are less clear. We investigated retinal layer thickness changes in RRMS, </span><span>primary and secondary progressive MS (PPMS, SPMS)</span><span>, and their prognostic value for disease activity. Here, we analyzed 2651 OCT measurements of 195 RRMS, 87 SPMS, 125 PPMS patients, and 98 controls from five German MS centers after quality control. Peripapillary and macular retinal nerve fiber layer (pRNFL, mRNFL) thickness </span><span>predicted</span><span> future relapses in all MS and RRMS patients while mRNFL</span><span> and </span><span>ganglion cell-inner plexiform layer (GCIPL) </span><span>thickness predicted </span><span>future </span><span>MRI activity </span><span>in RRMS (mRNFL, GCIPL) and PPMS (GCIPL). mRNFL thickness </span><span>predicted </span><span>future disability progression </span><span>in PPMS.</span><span> </span><span>However, thickness change rates were subject to considerable amounts of measurement variability. In conclusion, retinal degeneration, most pronounced of pRNFL and GCIPL, occurs in all subtypes. Using the current state of technology, longitudinal assessments of retinal thickness may not be suitable on a single patient level.</span></p>
Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0
<p>Retinal Fundus Multi-disease Image Dataset (RFMiD 2.0) is an auxiliary dataset to our previously published dataset. RFMiD 2.0 is a more challenging dataset to research society to develop the computer-based disease diagnosis system. Diabetic Retinopathy, cataracts, and refractive error in the eye are leading diseases that may lead to permanent vision loss more frequently. Therefore, developing an AI-based model to classify these diseases is useful for ophthalmologists. This dataset consists of 860 images of frequently and rarely observed 51 diseases. However, some images are labeled with multiple diseases. This dataset is useful for the research and development of AI-based medical healthcare systems in ophthalmology. </p>
Topological characterization of the retinal microvascular network visualized by portable fundus camera- effects of chronic disease (TREND2) database
<p><strong>Introduction</strong></p> <p><strong>T</strong>opological characterization of the <strong>R</strong>etinal microvascular n<strong>E</strong>twork visualized by portable fu<strong>ND</strong>us camera (<strong>TREND 2</strong>) is a database of digital color eye fundus images created as an addition to TREND database (https://zenodo.org/badge/DOI/10.5281/zenodo.4521044.svg).</p> <p>TREND 2 databse was created by medical professionals of the Faculty of Medicine of the University of Montenegro in 2023.</p> <p> </p> <p><strong>Purpose</strong></p> <p>1) to provide a standard that defines normal and abnormal retinal anatomy and microvascular geometry as it appears when visualized by the portable fundus camera</p> <p>2) to help the development of new methods for stratification of the risk for the development of various eye diseases, as well as systemic diseases that affect microvasculature</p> <p>3) to aid the development of biomarkers of accelerated aging</p> <p>4) to provide a standard that can be used to develop software for segmentation of retinal microvasculature, grading the quality of retinal digital images, and computer-aided diagnosis of systemic and chronic diseases.</p> <p>All color digital images were acquired with a hand-held portable, non-mydriatic MiiS HORUS Scope DEC 200 with 45º FOV and 2560 X 1920 pixel resolution.</p> <p> </p> <p><strong>Data</strong></p> <p>The TREND public database contains 28 color fundus images of old subjects (20 images from subjects with one or more chronic diseases such as type 2 diabetes mellitus, hypertension or Alzheimer's dementia- O_CD group, and 8 images from subjects with no chronic diseases- O_NCD group). Each image is associated with a corresponding binarized image of the manually segmented microvascular network.</p> <table> <caption>Inclusion and Exclusion Criteria</caption> <thead> <tr> <th scope="col">O_NCD group</th> <th scope="col">O_CD group</th> </tr> </thead> <tbody> <tr> <td><strong>Inclusion Criteria</strong></td> <td><strong>Inclusion Criteria</strong></td> </tr> <tr> <td>- at least 56 years old</td> <td>- at least 56 years old</td> </tr> <tr> <td> <p>- no current acute disease</p> <p>- no history of alcohol, or drug abuse, or psychiatric disease</p> </td> <td> <p>- no current acute disease</p> <p>- no history of alcohol, or drug abuse, or psychiatric disease</p> </td> </tr> <tr> <td> <p>- no history of alcohol, or drug abuse, or psychiatric disease</p> </td> <td>- no history of alcohol, or drug abuse, or psychiatric disease</td> </tr> <tr> <td>- negative history of any chronic disease</td> <td> <p>- controlled hypertension (blood pressure<140/90 mmHg), and/or</p> <p>- controlled type 2 diabetes mellitus, and/or</p> <p>- Alzheimer's dementia</p> </td> </tr> <tr> <td><strong>Exclusion Criteria</strong></td> <td><strong>Exclusion Criteria</strong></td> </tr> <tr> <td> <p>- presence of opacities of the transparent media in both eyes affecting</p> <p>- myopia ≥5 diopters</p> </td> <td> <p>- presence of opacities of the transparent media in both eyes affecting</p> <p>- myopia ≥5 diopters</p> </td> </tr> </tbody> </table> <p><strong>Files:</strong></p> <p>1_OLD WITH CHRONIC DISEASE_RAW (20 images in tif format)</p> <p>2_OLD WITH CHRONIC DISEASE_SEGMENTED (20 images in png format)</p> <p>3_OLD WITH NO CHRONIC DISEASE_RAW (8 images in tif format)</p> <p>4_OLD WITH NO CHRONIC DISEASE SEGMENTED (8 images in png format)</p> <p>5_ASSOCIATED DATA (xslx format)</p> <p>6_RETINAL PATHOLOGY (docx format)</p> <p> </p> <p> </p>
RQC for the Prevention of Alzheimer's Disease and Retinal Amyloid-β
ClinicalTrials.gov study NCT06470061. IPD Sharing: YES. Countries: 1. Publications: 7.
Effect of IVIG on Cerebral and Retinal Amyloid in Mild Cognitive Impairment Due to Alzheimer Disease
ClinicalTrials.gov study NCT03319810. IPD Sharing: NO. Countries: 1. Publications: 1.
Code from: The retinal age gap: An affordable and highly accessible biomarker for population-wide disease screening across the globe
Open the record for dataset details and reuse information.
Multiscale Entropy Analysis of Retinal Signals Reveals Reduced Complexity in a Mouse Model of Alzheimer's Disease
<p>MEA recordings from wild-type and 5xFAD mice retinas used for the analyses in the manuscript "Multiscale Entropy Analysis of Retinal Signals Reveals Reduced Complexity in a Mouse Model of Alzheimer's Disease".</p>
OCT and OCT-Angiography Biomarkers of Treatment Response to Dexamethasone Implant in Macular Edema Due to Retinal Vascular Diseases - DME and RVO
ClinicalTrials.gov study NCT06332690. IPD Sharing: UNDECIDED. Countries: 1. Publications: 15.
Retinal Neurodegenerative Signs in Alzheimer's Diseases
ClinicalTrials.gov study NCT01555827. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Measurement of Retinal Nerve Fiber Layer Thickness - a Biomarker for the Early Detection of Alzheimer's Disease?
ClinicalTrials.gov study NCT02051244. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Study of Retinal Findings in People With Signs and Symptoms of Alzheimer s Disease Enrolled in 09-M-0198
ClinicalTrials.gov study NCT02226835. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Intravitreal Adalimumab in Inherited and Degenerative Retinal Diseases
ClinicalTrials.gov study NCT07348588. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Characterization of Retinal Disease Progression in Eyes With Non Proliferative Diabetic Retinopathy in Diabetes Type 2 Using Non-invasive Procedures (CHART)
ClinicalTrials.gov study NCT04636307. IPD Sharing: YES. Countries: 3. Publications: 1.
Phase I Trial of Gene Vector to Patients With Retinal Disease Due to RPE65 Mutations
ClinicalTrials.gov study NCT00481546. IPD Sharing: Not stated. Countries: 1. Publications: 17.
Non-damaging Retinal Laser Therapy With PASCAL Laser for Macular Diseases
ClinicalTrials.gov study NCT01975103. IPD Sharing: Not stated. Countries: 1. Publications: 2.
CD160 Expression in Retinal Vessels is Associated With Retinal Neovascular Diseases
ClinicalTrials.gov study NCT03940664. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Rod and Cone Mediated Function in Retinal Disease
ClinicalTrials.gov study NCT02617966. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Characterization of Retinal Vascular Disease in Eyes With Mild to Moderate NPDR in Diabetes Type 2
ClinicalTrials.gov study NCT03696810. IPD Sharing: NO. Countries: 1. Publications: 1.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.