ArticleEye (London, England)2024
Development and validation of a deep learning model to predict axial length from ultra-wide field images.
Article in Eye (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Association between sleep and myopia in children and adolescents: a systematic review and meta-analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Pooled it
- Quantitative color fundus photography parameters as potential biomarkers of axial length progression: evidence from a machine learning cohort study.Frontiers in cell and developmental biology · 2026Article
- Predicting myopia risk using a machine learning model based on fundus imageomics.Scientific reports · 2025Article
- Network architecture optimization for ophthalmic ultrasound image detection based on modular ablation of multi-version YOLO.Biomedical engineering online · 2025Article
- Foundation models in ophthalmology: a preliminary study on AI-assisted diagnosis of myopic maculopathy and posterior staphyloma using ultra-widefield fundus images.BMJ open ophthalmology · 2025Article
- Application of artificial intelligence in myopia prevention and control.Pediatric investigation · 2025Review
- Open ultrawidefield fundus image dataset with disease diagnosis and clinical image quality assessment.Scientific data · 2024Article
- Diabetic Retinopathy Assessment through Multitask Learning Approach on Heterogeneous Fundus Image Datasets.Ophthalmology scienceArticle
- Individualized Estimation of Baseline Retinal Nerve Fiber Layer Thickness Using Conditional Variational Autoencoder.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo validate the feasibility of building a deep learning model to predict axial length (AL) for moderate to high myopic patients from ultra-wide field (UWF) images.
methodsThis study included 6174 UWF images from 3134 myopic patients during 2014 to 2020 in Eye and ENT Hospital of Fudan University. Of 6174 images, 4939 were used for training, 617 for validation, and 618 for testing. The coefficient of determination (R
resultsThe model predicted AL with high accuracy. Evaluating performance of R
conclusionsIt is feasible to utilize deep learning models to predict AL for moderate to high myopic patients with UWF 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.