ArticleCommunications biology2020
Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images.
Article in Communications biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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
48 citing papers in PubMed, 1 synthesis or guideline pooled it, 98 citations in OpenAlex.
- Diagnostic accuracy of deep learning using ultra-widefield fundus imaging for retinal detachment: a systematic review and meta-analysis.BMC ophthalmology · 2026Pooled it
- The Underutilized Ocular Fundus in Emergency Departments: Current Progress and Future Prospect of Imaging and Artificial Intelligence for Patient Care.Ophthalmology science · 2026Review
- Artificial Intelligence for In-Flight Detection of Space-Related Ocular Trauma: Bridging Diagnostic Gaps in Microgravity.Vision (Basel, Switzerland) · 2026Review
- ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound.Scientific data · 2026Article
- A Comparative Analysis of Deep Convolutional Networks for Automated Diagnosis of Retinal Detachment in Dogs.Veterinary ophthalmology · 2026Article
- Shaping the future of myopia: artificial intelligence for vitreoretinal complications of high and pathologic myopia.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Artificial Intelligence in Vitreoretinal Surgery: A Systematic Review of Current Applications and Future Directions.Ophthalmology and therapy · 2026Review
- Cross-modal fusion of OCT and OCT angiography enface for improved diagnostics of diabetic retinopathy.Frontiers in medicine · 2026Article
- LightMG-Net: an efficient lightweight deep neural network for multiclass grading of retinal detachment using handcrafted statistical mechanisms.Scientific reports · 2025Article
- Deep Learning Algorithm Prognosticating Retinal Tears and Detachments From Optical Coherence Tomography.Translational vision science & technology · 2025Article
- ACM-Assessor: An Artificial Intelligence System for Assessing Angle Closure Mechanisms in Ultrasound Biomicroscopy.Bioengineering (Basel, Switzerland) · 2025Article
- Deep learning detection of retinal detachment: Optical coherence tomography staging and estimation of duration of macular detachment.PloS one · 2025Article
- Deep learning for multi-type infectious keratitis diagnosis: A nationwide, cross-sectional, multicenter study.NPJ digital medicine · 2024Article
- Analysis of the performance of the CorneAI for iOS in the classification of corneal diseases and cataracts based on journal photographs.Scientific reports · 2024Article
- Deep learning system for screening AIDS-related cytomegalovirus retinitis with ultra-wide-field fundus images.Heliyon · 2024Article
- Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia.Journal of translational medicine · 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
- Application and visualization study of an intelligence-assisted classification model for common eye diseases using B-mode ultrasound images.Frontiers in neuroscience · 2024Article
- Artificial intelligence in individualized retinal disease management.International journal of ophthalmology · 2024Review
Corrections and comments
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Authors and funding
20 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially before macular involvement. Manual retinal detachment screening is time-consuming and labour-intensive, which is difficult for large-scale clinical applications. In this study, we developed a cascaded deep learning system based on the ultra-widefield fundus images for automated retinal detachment detection and macula-on/off retinal detachment discerning. The performance of this system is reliable and comparable to an experienced ophthalmologist. In addition, this system can automatically provide guidance to patients regarding appropriate preoperative posturing to reduce retinal detachment progression and the urgency of retinal detachment repair. The implementation of this system on a global scale may drastically reduce the extent of vision impairment resulting from retinal detachment by providing timely identification and referral.
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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.