ArticleNPJ digital medicine2024
RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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The trial behind it
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Who cites it
23 citing papers in PubMed.
- Development of a deep learning model to predict cycloplegic myopia and pre- myopia: based on a large-scale screening of school-aged children.Eye (London, England) · 2026Article
- A decade of artificial intelligence research in ophthalmology: Global trends and transferable insights for medical AI.PLOS digital health · 2026Article
- Early Detection of Glaucoma and Diabetic Retinopathy in Low-Resource Settings: Barriers and Solutions.Journal of clinical medicine · 2026Review
- Agentic AI in global health equity for high altitude populations.NPJ digital medicine · 2026Review
- FunduSegmenter: Leveraging the RETFound Foundation Model for Joint Optic Disc and Optic Cup Segmentation in Retinal Fundus Images.Translational vision science & technology · 2026Article
- A three-dimensional multi-modal foundation model for optical coherence tomography.Nature biomedical engineering · 2026Article
- Comparison of RETFound and a Supervised Convolutional Neural Network for Detection of Referable Glaucoma from Fundus Photographs.Ophthalmology science · 2026Article
- A Novel Multimodal Implementation of a Foundation Artificial Intelligence Model Using Optic Nerve Head Fundus Photographs and OCT Imaging for Glaucoma Detection.Ophthalmology science · 2026Article
- Artificial intelligence-integrated multimodal retinal imaging for early detection and risk stratification of systemic vascular and neurodegenerative diseases.Frontiers in neurology · 2026Article
- Can a Natural Image-Based Foundation Model Outperform a Retina-Specific Model in Detecting Ocular and Systemic Diseases?Ophthalmology science · 2026Article
- Predicting myopia risk using a machine learning model based on fundus imageomics.Scientific reports · 2025Article
- Letter to the Editor concerning "Age-based prediction of non-routine discharge after anterior cervical discectomy and fusion using machine learning" by G. Mastrokostas, et al. (Eur Spine J [2025]: doi: 10.1007/s00586-025-09303-z).European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- REECAP: Contrastive learning of retinal aging reveals genetic loci linking morphology to eye disease.medRxiv : the preprint server for health sciences · 2025Article
- Reply to: Utilizing foundation models for developing clinical tools.NPJ digital medicine · 2025Article
- Matters arising: Utilizing foundation models for developing clinical tools.NPJ digital medicine · 2025Article
- Performance of General-Purpose Vision Language Models and Ophthalmology Foundation Models in Glaucoma Detection and Function Prediction.Translational vision science & technology · 2025Article
- AI-informed retinal biomarkers predict 10-year risk of onset of multiple hematological malignancies.European journal of cancer (Oxford, England : 1990) · 2025Article
- 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
- Comparison of Foundation and Supervised Learning-Based Models for Detection of Referable Glaucoma from Fundus Photographs.medRxiv : the preprint server for health sciences · 2025Article
- Revolutionizing diabetic retinopathy screening and management: The role of artificial intelligence and machine learning.World journal of clinical cases · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Visual impairments and blindness are major public health concerns globally. Effective eye disease screening aided by artificial intelligence (AI) is a promising countermeasure, although it is challenged by practical constraints such as poor image quality in community screening. The recently developed ophthalmic foundation model RETFound has shown higher accuracy in retinal image recognition tasks. This study developed an RETFound-enhanced deep learning (DL) model for multiple-eye disease screening using real-world images from community screenings. Our results revealed that our DL model improved the sensitivity and specificity by over 15% compared with commercial models. Our model also shows better generalisation ability than AI models developed using traditional processes. Additionally, decision curve analysis underscores the higher net benefit of employing our model in both urban and rural settings in China. These findings indicate that the RETFound-enhanced DL model can achieve a higher net benefit in community-based screening, advocating its adoption in low- and middle-income countries to address global eye health challenges.
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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.