ArticleJournal of personalized medicine2021
Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Accuracy of AI Models in Detecting Different Inherited Retinal Diseases: A Systematic Review and Meta-Analysis.Translational vision science & technology · 2025Pooled it
- RetinoMamba-AnatomyQuery: Anatomy-Guided Query Learning for Efficient Vision Impairment Screening from Retinal Fundus Images.Life (Basel, Switzerland) · 2026Article
- Development of a cloud-based estimator for analysing the disc-fovea angle in fundus images using a stacking ensemble model.BMJ open ophthalmology · 2026Article
- AI-Based Visual Prognosis in Full-Thickness Macular Hole Surgery Using the ILM Flap Technique.Beyoglu eye journal · 2026Article
- An artificial intelligence cloud platform for OCT-based retinal anomalies screening system in real clinical environments.NPJ digital medicine · 2025Article
- Performance and reliability evaluation of an improved machine learning-based pure-tone audiometry with automated masking.World journal of otorhinolaryngology - head and neck surgery · 2025Article
- Detection of Ocular Surface Squamous Neoplasia Using Artificial Intelligence With Anterior Segment Optical Coherence Tomography.American journal of ophthalmology · 2025Article
- Artificial intelligence in the diagnosis of uveal melanoma: advances and applications.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- Empowering Portable Age-Related Macular Degeneration Screening: Evaluation of a Deep Learning Algorithm for a Smartphone Fundus Camera.BMJ open · 2024Article
- Development and validation of a deep learning model to predict axial length from ultra-wide field images.Eye (London, England) · 2024Article
- Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography.BMC medical informatics and decision making · 2024Article
- Deep learning-based algorithm for the detection of idiopathic full thickness macular holes in spectral domain optical coherence tomography.International journal of retina and vitreous · 2024Article
- Detection and diagnosis of diabetic eye diseases using two phase transfer learning approach.PeerJ. Computer science · 2024Article
- A deep-learning pipeline for the diagnosis and grading of common blinding ophthalmic diseases based on lesion-focused classification model.Frontiers in artificial intelligence · 2024Article
- A Fully Unsupervised Deep Learning Framework for Non-Rigid Fundus Image Registration.Bioengineering (Basel, Switzerland) · 2022Article
- Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models.Diagnostics (Basel, Switzerland) · 2022Article
- An Artificial-Intelligence-Based Automated Grading and Lesions Segmentation System for Myopic Maculopathy Based on Color Fundus Photographs.Translational vision science & technology · 2022Article
- Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases.JAMA network open · 2022Article
- Trends in the Approval and Quality Management of Artificial Intelligence Medical Devices in the Republic of Korea.Diagnostics (Basel, Switzerland) · 2022Review
- A Study on the Correlation Between Age-Related Macular Degeneration and Alzheimer's Disease Based on the Application of Artificial Neural Network.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The use of retinal images, such as fundus photographs, is a promising approach for the development of AI-based diagnostic platforms. Retinal pathologies usually occur in a broad spectrum of eye diseases, including neovascular or dry age-related macular degeneration, epiretinal membrane, rhegmatogenous retinal detachment, retinitis pigmentosa, macular hole, retinal vein occlusions, and diabetic retinopathy. Here, we report a fundus image-based AI model for differential diagnosis of retinal diseases. We classified retinal images with three convolutional neural network models: ResNet50, VGG19, and Inception v3. Furthermore, the performance of several dense (fully connected) layers was compared. The prediction accuracy for diagnosis of nine classes of eight retinal diseases and normal control was 87.42% in the ResNet50 model, which added a dense layer with 128 nodes. Furthermore, our AI tool augments ophthalmologist's performance in the diagnosis of retinal disease. These results suggested that the fundus image-based AI tool is applicable for the medical diagnosis process of retinal diseases.
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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.