ArticleThe British journal of ophthalmology2024
Application of artificial intelligence system for screening multiple fundus diseases in Chinese primary healthcare settings: a real-world, multicentre and cross-sectional study of 4795 cases.
Article in The British journal of ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04592068 (Deep Learning-Based Automated Classification of Multi-Retinal Disease From Fundus Photography), which is not on this map. Cited by 21 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Deep Learning-Based Automated Classification of Multi-Retinal Disease From Fundus Photography
Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review.BMC primary care · 2025Pooled it
- Artificial intelligence in pathologic myopia: a review of clinical research studies.Frontiers in medicine · 2025Pooled it
- Clinical validation of artificial intelligence algorithms for the detection of different central-involved retinal pathologies and glaucoma from non-mydriatic images.Frontiers in artificial intelligence · 2026Trial
- Evaluating an AI-assisted triage workflow for retinal diseases.PLOS digital health · 2026Article
- Impact of artificial intelligence on the availability, accessibility, acceptability and quality of ophthalmic disease screening services: a scoping review.The British journal of ophthalmology · 2026Article
- Traditional Chinese Medicine Syndrome Differentiation of Adult Patients With Type 2 Diabetes and Metabolic Syndrome: Protocol for a Cross-Sectional Study.JMIR research protocols · 2026Article
- Prospective real-world implementation of deep learning systems in healthcare: a systematic review guided by implementation science.NPJ digital medicine · 2026Article
- Artificial intelligence in preventive care in primary health care settings: a scoping review.Archives of medical sciences. Atherosclerotic diseases · 2026Article
- Artificial intelligence and digital health equity: a post-pandemic evidence synthesis and implementation safeguards framework.Frontiers in digital health · 2026Review
- Review
- Development and multicenter validation of an AI driven model for quantitative meibomian gland evaluation.NPJ digital medicine · 2025Article
- Prevalence of Myopia and Axial Length Distribution in China: The Wuhu Children and Adolescents Eye Study.Investigative ophthalmology & visual science · 2025Article
- Application of artificial intelligence in myopia prevention and control.Pediatric investigation · 2025Review
- Application research of artificial intelligence software in the analysis of thyroid nodule ultrasound image characteristics.PloS one · 2025Article
- Study on the prevalence of diabetic retinopathy in Pudong New Area and the predictive value of estimated glucose disposal rate: a cross-sectional study.Frontiers in endocrinology · 2025Article
- Diagnostic challenges in high myopia: identification of sight-threatening complications and the role of artificial intelligence.Frontiers in ophthalmology · 2025Review
- Predicting the Degree of Coronary Artery Stenosis Through Retinal Vascular Characteristics and Minimal Clinical Information.International journal of general medicine · 2025Article
- Bias in medical AI: Implications for clinical decision-making.PLOS digital health · 2024Review
- Article
- Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system.The EPMA journal · 2024Article
Corrections and comments
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Authors and funding
21 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
aimsThis study evaluates the performance of the Airdoc retinal artificial intelligence system (ARAS) for detecting multiple fundus diseases in real-world scenarios in primary healthcare settings and investigates the fundus disease spectrum based on ARAS.
methodsThis real-world, multicentre, cross-sectional study was conducted in Shanghai and Xinjiang, China. Six primary healthcare settings were included in this study. Colour fundus photographs were taken and graded by ARAS and retinal specialists. The performance of ARAS is described by its accuracy, sensitivity, specificity and positive and negative predictive values. The spectrum of fundus diseases in primary healthcare settings has also been investigated.
resultsA total of 4795 participants were included. The median age was 57.0 (IQR 39.0-66.0) years, and 3175 (66.2%) participants were female. The accuracy, specificity and negative predictive value of ARAS for detecting normal fundus and 14 retinal abnormalities were high, whereas the sensitivity and positive predictive value varied in detecting different abnormalities. The proportion of retinal drusen, pathological myopia and glaucomatous optic neuropathy was significantly higher in Shanghai than in Xinjiang. Moreover, the percentages of referable diabetic retinopathy, retinal vein occlusion and macular oedema in middle-aged and elderly people in Xinjiang were significantly higher than in Shanghai.
conclusionThis study demonstrated the dependability of ARAS for detecting multiple retinal diseases in primary healthcare settings. Implementing the AI-assisted fundus disease screening system in primary healthcare settings might be beneficial in reducing regional disparities in medical resources. However, the ARAS algorithm must be improved to achieve better performance. TRIAL REGISTRATION NUMBER: NCT04592068.
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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.