Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Role of Deep Learning–Quantified Hyperreflective Foci for the Prediction of Geographic Atrophy Progression

8 Apr 2020

Abstract excerpt

PURPOSE: To quantitatively measure hyperreflective foci (HRF) during the progression of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) using deep learning (DL) and investigate the association with local and global growth of GA. METHODS: Eyes with GA were prospectively included. Spectral-domain optical coherence tomography (SDOCT) and fundus autofluorescence images were acquired every...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Glaucoma and retinal disorders
Share to Retinal Diseases and Treatments
Share to Retinal Imaging and Analysis

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 32277942
DOI: 10.1016/j.ajo.2020.03.042

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Role of Deep Learning–Quantified Hyperreflective Foci for the Prediction of Geographic Atrophy ProgressionPMID 32277942Read ↗

Machine-learning and Automatically Segmented Retinal Biomarkers Generate Spatial Heatmaps Predictive for Standard and Low Luminance Visual Acuity in Geographic AtrophySemantic proximity Read ↗

Structural OCT and OCT angiography biomarkers associated with the development and progression of geographic atrophy in AMDSemantic proximity Read ↗

Sequence of Morphological Changes Preceding Atrophy in Intermediate AMD Using Deep LearningSemantic proximity Read ↗

Deep Learning-Based Detection of Reticular Pseudodrusen in Age-Related Macular Degeneration on Optical Coherence TomographySemantic proximity Read ↗

Hyperreflective Choroidal Foci Characterization Supports the Existence of Different Clinical Phenotypes of Geographic Atrophy.Semantic proximity Read ↗

Geographic atrophy in age-related macular degeneration: phenotypic characterisation for clinical trial consideration.Semantic proximity Read ↗

Geographic atrophy: where we are now and where we are goingSemantic proximity Read ↗

Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning.Semantic proximity Read ↗

Incidence and progression of geographic atrophy: observations from a population-based cohort.Semantic proximity Read ↗

Characterisation and clinical impact of hypertransmission alterations in neovascular age-related macular degeneration using en face OCTSemantic proximity Read ↗

PHENOTYPIC CHARACTERIZATION OF PREDICTORS FOR DEVELOPMENT AND PROGRESSION OF GEOGRAPHIC ATROPHY USING OPTICAL COHERENCE TOMOGRAPHY.Semantic proximity Read ↗

Deep Learning-Based SD-OCT Layer Segmentation Quantifies Outer Retina Changes in Patients With Biallelic RPE65 Mutations Undergoing Gene Therapy.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.