ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2019
Automated OCT angiography image quality assessment using a deep learning algorithm.
Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 44 citations in OpenAlex.
- An artificial intelligence-driven image quality assessment system for whole-body [European journal of nuclear medicine and molecular imaging · 2023Trial
- Automated Assessment of OCT Angiography Image Quality Using the Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights Data Set.Ophthalmology science · 2026Article
- Image Quality as an Important Confounder in Quantitative OCT Angiography: A Review with Quantitative Synthesis.Medical sciences (Basel, Switzerland) · 2026Review
- Automated anatomically guided quality assessment for OCTA via multi-region analysis and statistical calibration.Biomedical optics express · 2026Article
- The AROMA Dataset for Automatic Detection of Artifact Type and Severity in Retinal Optical Coherence Tomography Angiography.Ophthalmic research · 2026Article
- Widefield OCT angiography.Progress in retinal and eye research · 2025Review
- Retinal Imaging as a Window into Cardiovascular Health: Towards Harnessing Retinal Analytics for Precision Cardiovascular Medicine.Journal of cardiovascular development and disease · 2025Review
- Detection of Disease Features on Retinal OCT Scans Using RETFound.Bioengineering (Basel, Switzerland) · 2024Article
- Impact of lens autofluorescence and opacification on retinal imaging.BMJ open ophthalmology · 2024Article
- Inter-rater reliability in labeling quality and pathological features of retinal OCT scans: A customized annotation software approach.PloS one · 2024Article
- Detection of systemic cardiovascular illnesses and cardiometabolic risk factors with machine learning and optical coherence tomography angiography: a pilot study.Eye (London, England) · 2023Article
- Retest variability and patient reliability indices of quantitative fundus autofluorescence in age-related macular degeneration: a MACUSTAR study report.Scientific reports · 2023Article
- Convolutional Neural Network-Based Automated Quality Assessment of OCT and OCT Angiography Image Maps in Individuals With Neurodegenerative Disease.Translational vision science & technology · 2023Article
- Reliability of Retinal Layer Annotation with a Novel, High-Resolution Optical Coherence Tomography Device: A Comparative Study.Bioengineering (Basel, Switzerland) · 2023Article
- Optical Coherence Tomography Angiography of the Intestine: How to Prevent Motion Artifacts in Open and Laparoscopic Surgery?Life (Basel, Switzerland) · 2023Article
- Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2023Review
- Deep learning for quality assessment of optical coherence tomography angiography images.Scientific reports · 2022Article
- Towards standardizing retinal optical coherence tomography angiography: a review.Light, science & applications · 2022Review
- Your mileage may vary: impact of data input method for a deep learning bone age app's predictions.Skeletal radiology · 2022Article
- Assessment of Artifacts in Swept-Source Optical Coherence Tomography Angiography for Glaucomatous and Normal Eyes.Translational vision science & technology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo expedite and to standardize the process of image quality assessment in optical coherence tomography angiography (OCTA) using a specialized deep learning algorithm (DLA).
methodsTwo hundred randomly chosen en-face macular OCTA images of the central 3 × 3 mm
resultsTraining accuracy was 97%, validation accuracy 100%, and cross entropy 0.12. A total of 90% (18/20) of the OCTA images with insufficient image quality and 90% (18/20) with sufficient image quality were correctly classified by the DLA. Mean IPS was 0.88 ± 0.21, and mean SPS was 0.84 ± 0.19. Discrimination between both groups was highly significant (p < 0.001). Sensitivity of the DLA was 90.0%, specificity 90.0%, and accuracy 90.0%. Coefficients of variation were 0.96 ± 1.9% (insufficient quality) and 1.14 ± 1.6% (sufficient quality).
conclusionsDeep learning (DL) appears to be a potential approach to automatically distinguish between sufficient and insufficient OCTA image quality. DL may contribute to establish image quality standards in this recent imaging modality.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.