ArticleScientific reports2021
Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 50 citations in OpenAlex.
- Automated FAZ segmentation and diabetic retinopathy classification using OCTA images.BMC ophthalmology · 2025Article
- Semantic segmentation of the avascular zone of the fovea in optical coherence tomography angiography: evaluation of techniques and applications in ocular diseases.International journal of retina and vitreous · 2025Article
- Deep learning for retinal non-perfusion and foveal avascular zone analysis in wide-field OCTA in diabetic retinopathy.Scientific reports · 2025Article
- Low rank based FAZ segmentation in OCTA images.Scientific reports · 2025Article
- Automated Foveal Avascular Zone Segmentation in Optical Coherence Tomography Angiography Across Multiple Eye Diseases Using Knowledge Distillation.Bioengineering (Basel, Switzerland) · 2025Article
- Retinal microvascular changes in patients with pancreatitis and their clinical significance.Scientific reports · 2024Article
- Non-Invasive Retinal Vessel Analysis as a Predictor for Cardiovascular Disease.Journal of personalized medicine · 2024Review
- Ocular microvascular alteration in patients with myocardial infarction-a new OCTA study.Scientific reports · 2024Article
- Enhancing foveal avascular zone analysis for Alzheimer's diagnosis with AI segmentation and machine learning using multiple radiomic features.Scientific reports · 2024Article
- Estimation of foveal avascular zone area from a B-scan OCT image using machine learning algorithms.PloS one · 2024Article
- OCT angiography and its retinal biomarkers [Invited].Biomedical optics express · 2023Review
- Cardiac Adipose Tissue Segmentation via Image-Level Annotations.IEEE journal of biomedical and health informatics · 2023Article
- A Reliable Criterion for the Correct Delimitation of the Foveal Avascular Zone in Diabetic Patients.Journal of personalized medicine · 2023Article
- Optical Coherence Tomography Angiography in Retinal Vascular Disorders.Diagnostics (Basel, Switzerland) · 2023Review
- Using Artificial Intelligence to Analyse the Retinal Vascular Network: The Future of Cardiovascular Risk Assessment Based on Oculomics? A Narrative Review.Ophthalmology and therapy · 2023Review
- 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 with a Dataset Created Using Kanno Saitama Macro, a Self-Made Automatic Foveal Avascular Zone Extraction Program.Journal of clinical medicine · 2022Article
- A lightweight deep learning model for automatic segmentation and analysis of ophthalmic images.Scientific reports · 2022Article
- Deep Learning Image Analysis of Optical Coherence Tomography Angiography Measured Vessel Density Improves Classification of Healthy and Glaucoma Eyes.American journal of ophthalmology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device's built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device's built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland-Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of - 0.005 to 0.026 mm
Indexed as
Identifiers
What OpenQuestion holds
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.