ArticleTranslational vision science & technology2020
Deep Neural Network for Scleral Spur Detection in Anterior Segment OCT Images: The Chinese American Eye Study.
Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.
- Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging.Frontiers in ophthalmology · 2022Pooled it
- Comparison of Biometric Measurements Between Anterior Segment Hyperparallel and Swept-Source Optical Coherence Tomography.Translational vision science & technology · 2026Article
- Quantitative Ciliary Body Imaging Using Image-Registered Swept-Source AS-OCT and Ultrasound Biomicroscopy.Translational vision science & technology · 2026Article
- Risk prediction in primary angle closure suspects: current perspectives.Annals of translational medicine · 2026Article
- Deep learning-based normative database of anterior chamber dimensions for angle closure assessment: the Singapore Chinese Eye Study.The British journal of ophthalmology · 2025Article
- Artificial Intelligence for Optical Coherence Tomography in Glaucoma.Translational vision science & technology · 2025Review
- Article
- Diagnostic criteria of anterior segment swept-source optical coherence tomography to detect gonioscopic angle closure.The British journal of ophthalmology · 2024Article
- Automated expert-level scleral spur detection and quantitative biometric analysis on the ANTERION anterior segment OCT system.The British journal of ophthalmology · 2024Article
- Agreement and Reproducibility of Anterior Chamber Angle Measurements between CASIA2 Built-In Software and Human Graders.Journal of clinical medicine · 2023Article
- Classification of Angle Closure Severity by Hierarchical Cluster Analysis of Ocular Biometrics in the Dark and Light.Translational vision science & technology · 2023Article
- Comprehensive assessment of the anterior segment in refraction corrected OCT based on multitask learning.Biomedical optics express · 2023Article
- Reproducibility of deep learning based scleral spur localisation and anterior chamber angle measurements from anterior segment optical coherence tomography images.The British journal of ophthalmology · 2023Article
- Optimal number and orientation of anterior segment OCT images to measure ocular biometric parameters in angle closure eyes: the Chinese American Eye Study.The British journal of ophthalmology · 2023Article
- Automatic measurement of anterior chamber angle parameters in AS-OCT images using deep learning.Biomedical optics express · 2023Article
- Anterior segment biometric measurements explain misclassifications by a deep learning classifier for detecting gonioscopic angle closure.The British journal of ophthalmology · 2023Article
- A Mechanistic Model of Aqueous Humor Flow to Study Effects of Angle Closure on Intraocular Pressure.Translational vision science & technology · 2023Article
- Performance of the Deep Neural NetworkDiagnostics (Basel, Switzerland) · 2022Article
- Development of Cumulative Order-Preserving Image Transformation Based Variational Autoencoder for Anterior Segment Optical Coherence Tomography Images.Translational vision science & technology · 2022Article
- Development of a β-Variational Autoencoder for Disentangled Latent Space Representation of Anterior Segment Optical Coherence Tomography Images.Translational vision science & technology · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Purpose: To develop a deep neural network that detects the scleral spur in anterior segment optical coherence tomography (AS-OCT) images. Methods: Participants in the Chinese American Eye Study, a population-based study in Los Angeles, California, underwent complete ocular examinations, including AS-OCT imaging with the Tomey CASIA SS-1000. One human expert grader provided reference labels of scleral spur locations in all images. A convolutional neural network (CNN)-based on the ResNet-18 architecture was developed to detect the scleral spur in each image. Performance of the CNN model was assessed by calculating prediction errors, defined as the difference between the Cartesian coordinates of reference and CNN-predicted scleral spur locations. Prediction errors were compared with intragrader variability in detecting scleral spur locations by the reference grader. Results: The CNN was developed using a training dataset of 17,704 images and tested using an independent dataset of 921 images. The mean absolute prediction errors of the CNN model were 49.27 ± 42.07 µm for X-coordinates and 47.73 ± 39.70 µm for Y-coordinates. The mean absolute intragrader variability was 52.31 ± 47.75 µm for X-coordinates and 45.88 ± 45.06 µm for Y-coordinates. Distributions of prediction errors for the CNN and intragrader variability for the reference grader were similar for X-coordinates ( Conclusions: A deep neural network can detect the scleral spur on AS-OCT images with performance similar to that of a human expert grader. Translational Relevance: Deep learning methods that automate scleral spur detection can facilitate qualitative and quantitative assessments of AS-OCT images.
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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.