ArticleAnnals of translational medicine2019
A deep learning system for identifying lattice degeneration and retinal breaks using ultra-widefield fundus images.
Article in Annals of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 57 citations in OpenAlex.
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- LightMG-Net: an efficient lightweight deep neural network for multiclass grading of retinal detachment using handcrafted statistical mechanisms.Scientific reports · 2025Article
- Pixel-Level Segmentation of Retinal Breaks in Ultra-Widefield Fundus Images with a PraNet-Based Machine Learning Model.Sensors (Basel, Switzerland) · 2025Article
- Ultra-wide-field fundus photography and AI-based screening and referral for multiple ocular fundus diseases.Cell reports. Medicine · 2025Article
- Development and evaluation of a deep learning system for screening real-world multiple abnormal findings based on ultra-widefield fundus images.Frontiers in medicine · 2025Article
- Deep learning system for screening AIDS-related cytomegalovirus retinitis with ultra-wide-field fundus images.Heliyon · 2024Article
- Deep Learning-Based Automated Detection of Retinal Breaks and Detachments on Fundus Photography.Translational vision science & technology · 2024Article
- Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images.Diagnostics (Basel, Switzerland) · 2024Article
- Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus images.International journal of ophthalmology · 2024Review
- Insights into artificial intelligence in myopia management: from a data perspective.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Review
- Diagnosis ofDiagnostics (Basel, Switzerland) · 2023Review
- Artificial intelligence in ophthalmology: The path to the real-world clinic.Cell reports. Medicine · 2023Review
- Semi-automated quantification of vitreal hyperreflective foci in SD-OCT and their relevance in patients with peripheral retinal breaks.BMC ophthalmology · 2023Article
- Deep Learning for the Detection of Multiple Fundus Diseases Using Ultra-widefield Images.Ophthalmology and therapy · 2023Article
- Intelligent Diagnosis of Multiple Peripheral Retinal Lesions in Ultra-widefield Fundus Images Based on Deep Learning.Ophthalmology and therapy · 2023Article
- Which data subset should be augmented for deep learning? a simulation study using urothelial cell carcinoma histopathology images.BMC bioinformatics · 2023Article
- Joint conditional generative adversarial networks for eyelash artifact removal in ultra-wide-field fundus images.Frontiers in cell and developmental biology · 2023Article
- Spotlight on Lattice Degeneration Imaging Techniques.Clinical ophthalmology (Auckland, N.Z.) · 2023Review
- Automated analysis of vessel morphometry in retinal images from a Danish high street optician setting.PloS one · 2023Article
- Deep learning for ultra-widefield imaging: a scoping review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022Article
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Authors and funding
17 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLattice degeneration and/or retinal breaks, defined as notable peripheral retinal lesions (NPRLs), are prone to evolving into rhegmatogenous retinal detachment which can cause severe visual loss. However, screening NPRLs is time-consuming and labor-intensive. Therefore, we aimed to develop and evaluate a deep learning (DL) system for automated identifying NPRLs based on ultra-widefield fundus (UWF) images.
methodsA total of 5,606 UWF images from 2,566 participants were used to train and verify a DL system. All images were classified by 3 experienced ophthalmologists. The reference standard was determined when an agreement was achieved among all 3 ophthalmologists, or adjudicated by another retinal specialist if disagreements existed. An independent test set of 750 images was applied to verify the performance of 12 DL models trained using 4 different DL algorithms (InceptionResNetV2, InceptionV3, ResNet50, and VGG16) with 3 preprocessing techniques (original, augmented, and histogram-equalized images). Heatmaps were generated to visualize the process of the best DL system in the identification of NPRLs.
resultsIn the test set, the best DL system for identifying NPRLs achieved an area under the curve (AUC) of 0.999 with a sensitivity and specificity of 98.7% and 99.2%, respectively. The best preprocessing method in each algorithm was the application of original image augmentation (average AUC =0.996). The best algorithm in each preprocessing method was InceptionResNetV2 (average AUC =0.996). In the test set, 150 of 154 true-positive cases (97.4%) displayed heatmap visualization in the NPRL regions.
conclusionsA DL system has high accuracy in identifying NPRLs based on UWF images. This system may help to prevent the development of rhegmatogenous retinal detachment by early detection of NPRLs.
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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.