ArticleBiomedical engineering online2022
Application of artificial intelligence-based dual-modality analysis combining fundus photography and optical coherence tomography in diabetic retinopathy screening in a community hospital.
Article in Biomedical engineering online, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 27 citations in OpenAlex.
- Artificial intelligence in diabetic retinopathy: from automated screening to risk-stratified care.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- MultiRetNet: A Lightweight Explainable AI Approach to Diabetic Retinopathy Grading and DME Detection Using Fundus-OCT Fusion.Journal of imaging · 2026Article
- Automated Report Generation in Ophthalmology: Integrating Artificial Intelligence, Multimodal Imaging, and Clinical Data.Ophthalmology and therapy · 2026Review
- Prospective real-world implementation of deep learning systems in healthcare: a systematic review guided by implementation science.NPJ digital medicine · 2026Article
- Development and application of an artificial intelligence-based quantitative system for white without pressure in myopic retinopathy.Frontiers in cell and developmental biology · 2026Article
- The AI-Augmented Ophthalmologist for chronic ocular diseases: a patient-centered framework for human-AI collaboration.Frontiers in cell and developmental biology · 2026Review
- Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.Journal of diabetes and metabolic disorders · 2025Review
- The role of artificial intelligence in the diagnosis of diabetic retinopathy through retinal lesion features: a narrative review.Quantitative imaging in medicine and surgery · 2025Review
- The efficacy of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis.International journal of retina and vitreous · 2025Article
- Fundus Image Deep Learning Study to Explore the Association of Retinal Morphology with Age-Related Macular Degeneration Polygenic Risk Score.Biomedicines · 2024Article
- Hybrid deep learning models for the screening of Diabetic Macular Edema in optical coherence tomography volumes.Scientific reports · 2024Article
- Retinal vascular arcade angle as a biomarker for visual improvement after epiretinal membrane surgery.Eye (London, England) · 2024Article
- 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.The British journal of ophthalmology · 2024Article
- Recognition of eye diseases based on deep neural networks for transfer learning and improved D-S evidence theory.BMC medical imaging · 2024Article
- The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use.Journal of Korean medical science · 2023Review
- Application effect of an artificial intelligence-based fundus screening system: evaluation in a clinical setting and population screening.Biomedical engineering online · 2023Article
- Development and Validation of a Deep Learning System for the Provision of a District-Wide Diabetes Retinal Screening Service.Clinical & experimental ophthalmologyArticle
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundTo assess the feasibility and clinical utility of artificial intelligence (AI)-based screening for diabetic retinopathy (DR) and macular edema (ME) by combining fundus photos and optical coherence tomography (OCT) images in a community hospital.
methodsFundus photos and OCT images were taken for 600 diabetic patients in a community hospital. Ophthalmologists graded these fundus photos according to the International Clinical Diabetic Retinopathy (ICDR) Severity Scale as the ground truth. Two existing trained AI models were used to automatically classify the fundus images into DR grades according to ICDR, and to detect concomitant ME from OCT images, respectively. The criteria for referral were DR grades 2-4 and/or the presence of ME. The sensitivity and specificity of AI grading were evaluated. The number of referable DR cases confirmed by ophthalmologists and AI was calculated, respectively.
resultsDR was detected in 81 (13.5%) participants by ophthalmologists and in 94 (15.6%) by AI, and 45 (7.5%) and 53 (8.8%) participants were diagnosed with referable DR by ophthalmologists and by AI, respectively. The sensitivity, specificity and area under the curve (AUC) of AI for detecting DR were 91.67%, 96.92% and 0.944, respectively. For detecting referable DR, the sensitivity, specificity and AUC of AI were 97.78%, 98.38% and 0.981, respectively. ME was detected from OCT images in 49 (8.2%) participants by ophthalmologists and in 57 (9.5%) by AI, and the sensitivity, specificity and AUC of AI were 91.30%, 97.46% and 0.944, respectively. When combining fundus photos and OCT images, the number of referrals identified by ophthalmologists increased from 45 to 75 and from 53 to 85 by AI.
conclusionAI-based DR screening has high sensitivity and specificity and may feasibly improve the referral rate of community DR.
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