ArticleKorean journal of ophthalmology : KJO2023
Deep Learning-based Prediction of Axial Length Using Ultra-widefield Fundus Photography.
Article in Korean journal of ophthalmology : KJO, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Stereo-Vision-Inspired Spectral Disparity Learning for Choroidal Tumor Thickness Prediction.Translational vision science & technology · 2026Article
- Peripheral Retinal Vessel Density in Children and Adolescents with Myopia: A Deep Learning Assessment.Ophthalmology and therapy · 2026Article
- 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
- 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
- Refractive development III: Variations in emmetropia and ametropia.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025Article
- Application of artificial intelligence in myopia prevention and control.Pediatric investigation · 2025Review
- Deep learning-based automatic image quality assessment in ultra-widefield fundus photographs.BMJ open · 2025Article
- Artificial Intelligence in Optometry: Current and Future Perspectives.Clinical optometry · 2025Review
- Prediction of Axial Length From Macular Optical Coherence Tomography Using Deep Learning Model.Translational vision science & technology · 2024Article
- The long and short of it: a comprehensive assessment of axial length estimation in myopic eyes from ocular and demographic variables.Eye (London, England) · 2024Article
- Deep learning-based estimation of axial length using macular optical coherence tomography images.Frontiers in medicine · 2023Article
- Individualized Estimation of Baseline Retinal Nerve Fiber Layer Thickness Using Conditional Variational Autoencoder.Ophthalmology scienceArticle
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6 authors.
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Abstract
purposeTo develop a deep learning model that can predict the axial lengths of eyes using ultra-widefield (UWF) fundus photography.
methodsWe retrospectively enrolled patients who visited the ophthalmology clinic at the Seoul National University Hospital between September 2018 and December 2021. Patients with axial length measurements and UWF images taken within 3 months of axial length measurement were included in the study. The dataset was divided into a development set and a test set at an 8:2 ratio while maintaining an equal distribution of axial lengths (stratified splitting with binning). We used transfer learning-based on EfficientNet B3 to develop the model. We evaluated the model's performance using mean absolute error (MAE), R-squared (R2), and 95% confidence intervals (CIs). We used vanilla gradient saliency maps to illustrate the regions predominantly used by convolutional neural network.
resultsIn total, 8,657 UWF retinal fundus images from 3,829 patients (mean age, 63.98 ±15.25 years) were included in the study. The deep learning model predicted the axial lengths of the test dataset with MAE and R2 values of 0.744 mm (95% CI, 0.709-0.779 mm) and 0.815 (95% CI, 0.785-0.840), respectively. The model's accuracy was 73.7%, 95.9%, and 99.2% in prediction, with error margins of ±1.0, ±2.0, and ±3.0 mm, respectively.
conclusionsWe developed a deep learning-based model for predicting the axial length from UWF images with good performance.
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