ArticleFrontiers in cell and developmental biology2021
Deep Learning-Based Estimation of Axial Length and Subfoveal Choroidal Thickness From Color Fundus Photographs.
Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 31 citations in OpenAlex.
- Stereo-Vision-Inspired Spectral Disparity Learning for Choroidal Tumor Thickness Prediction.Translational vision science & technology · 2026Article
- Axial Length Estimation Using Metadata From Swept-Source Optical Coherence Tomography Angiography.Investigative ophthalmology & visual science · 2026Observational
- Deep Learning Prediction of Retinal Thickness from Near-Infrared Fundus Photography: Toward Decentralized Quantitative Assessment of Diabetic Macular Edema.Journal of personalized medicine · 2026Article
- Association between fundus sex index and hypertension, diabetes mellitus, and smoking in the Kumejima study.Japanese journal of ophthalmology · 2026Observational
- Peripheral Retinal Vessel Density in Children and Adolescents with Myopia: A Deep Learning Assessment.Ophthalmology and therapy · 2026Article
- Association between fundus tessellated density and subfoveal choroidal thickness in children with different refractive statuses.Frontiers in medicine · 2026Article
- Predicting myopia risk using a machine learning model based on fundus imageomics.Scientific reports · 2025Article
- Refractive error and axial length prediction using fundus parameters of normal eyes in the Kumejima population study.Japanese journal of ophthalmology · 2025Observational
- Refractive development III: Variations in emmetropia and ametropia.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025Article
- Age prediction using fundus parameters of normal eyes from the Kumejima population study.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- Prediction of Axial Length From Macular Optical Coherence Tomography Using Deep Learning Model.Translational vision science & technology · 2024Article
- Relationship between fundus sex index obtained using color fundus parameters and body height or axial length in the Kumejima population.Japanese journal of ophthalmology · 2024Observational
- Convolutional Neural Network-Based Prediction of Axial Length Using Color Fundus Photography.Translational vision science & technology · 2024Article
- Development and validation of a deep learning model to predict axial length from ultra-wide field images.Eye (London, England) · 2024Article
- Regional choroidal thickness estimation from color fundus images based on convolutional neural networks.Heliyon · 2024Article
- Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era.Medical & biological engineering & computing · 2024Article
- Predicting central choroidal thickness from colour fundus photographs using deep learning.PloS one · 2024Article
- Classification of Color Fundus Photographs Using Fusion Extracted Features and Customized CNN Models.Healthcare (Basel, Switzerland) · 2023Article
- Deep Learning-based Prediction of Axial Length Using Ultra-widefield Fundus Photography.Korean journal of ophthalmology : KJO · 2023Article
- Three-year changes in sex judgment using color fundus parameters in elementary school students.PloS one · 2023Observational
Corrections and comments
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Authors and funding
15 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to develop an automated computer-based algorithm to estimate axial length and subfoveal choroidal thickness (SFCT) based on color fundus photographs. In the population-based Beijing Eye Study 2011, we took fundus photographs and measured SFCT by optical coherence tomography (OCT) and axial length by optical low-coherence reflectometry. Using 6394 color fundus images taken from 3468 participants, we trained and evaluated a deep-learning-based algorithm for estimation of axial length and SFCT. The algorithm had a mean absolute error (MAE) for estimating axial length and SFCT of 0.56 mm [95% confidence interval (CI): 0.53,0.61] and 49.20 μm (95% CI: 45.83,52.54), respectively. Estimated values and measured data showed coefficients of determination of
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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.