ArticleEye (London, England)2022
Automated detection of retinal exudates and drusen in ultra-widefield fundus images based on deep learning.
Article in Eye (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 33 citations in OpenAlex.
- A systematic review on deep learning techniques for diabetic retinopathy classification in retinal fundus images.iScience · 2026Article
- Attention-Based Multimodal Deep Learning for Uveal Melanoma Classification Using Ultra-Widefield Fundus Images and Ocular Ultrasound.Ophthalmology science · 2026Article
- Automatic diagnosis of age-related macular degeneration using machine learning and image processing techniques.Scientific reports · 2026Article
- Artificial intelligence in the diagnosis of uveal melanoma: advances and applications.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- Automatic Detection of 30 Fundus Diseases Using Ultra-Widefield Fluorescein Angiography with Deep Experts Aggregation.Ophthalmology and therapy · 2024Article
- Deep Learning Detection of Early Retinal Peripheral Degeneration From Ultra-Widefield Fundus Photographs of Asymptomatic Young Adult (17-19 Years) Candidates to Airforce Cadets.Translational vision science & technology · 2024Article
- Artificial intelligence in ophthalmology: The path to the real-world clinic.Cell reports. Medicine · 2023Review
- Exudate identification in retinal fundus images using precise textural verifications.Scientific reports · 2023Article
- Development of a robust eye exam diagnosis platform with a deep learning model.Technology and health care : official journal of the European Society for Engineering and Medicine · 2023Article
- Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2022Review
- Artificial intelligence to detect malignant eyelid tumors from photographic images.NPJ digital medicine · 2022Article
- An overview of artificial intelligence in diabetic retinopathy and other ocular diseases.Frontiers in public health · 2022Review
- Comparison of deep learning systems and cornea specialists in detecting corneal diseases from low-quality images.iScience · 2021Article
Corrections and comments
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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
backgroundRetinal exudates and/or drusen (RED) can be signs of many fundus diseases that can lead to irreversible vision loss. Early detection and treatment of these diseases are critical for improving vision prognosis. However, manual RED screening on a large scale is time-consuming and labour-intensive. Here, we aim to develop and assess a deep learning system for automated detection of RED using ultra-widefield fundus (UWF) images.
methodsA total of 26,409 UWF images from 14,994 subjects were used to develop and evaluate the deep learning system. The Zhongshan Ophthalmic Center (ZOC) dataset was selected to compare the performance of the system to that of retina specialists in RED detection. The saliency map visualization technique was used to understand which areas in the UWF image had the most influence on our deep learning system when detecting RED.
resultsThe system for RED detection achieved areas under the receiver operating characteristic curve of 0.994 (95% confidence interval [CI]: 0.991-0.996), 0.972 (95% CI: 0.957-0.984), and 0.988 (95% CI: 0.983-0.992) in three independent datasets. The performance of the system in the ZOC dataset was comparable to that of an experienced retina specialist. Regions of RED were highlighted by saliency maps in UWF images.
conclusionsOur deep learning system is reliable in the automated detection of RED in UWF images. As a screening tool, our system may promote the early diagnosis and management of RED-related fundus diseases.
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