ArticleBMC ophthalmology2019
The diagnostic accuracy of an intelligent and automated fundus disease image assessment system with lesion quantitative function (SmartEye) in diabetic patients.
Article in BMC ophthalmology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Early retinal disease detection from fundus images using deep neural networks.Scientific reports · 2026Article
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- Ophthalmology specialty training for general practitioners in China: current status and the Shenzhen model.BMC primary care · 2026Observational
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- Development and application of an artificial intelligence-based quantitative system for white without pressure in myopic retinopathy.Frontiers in cell and developmental biology · 2026Article
- Quantitative analysis of retinal microvascular changes in gestational hypertension and preeclampsia using artificial intelligence technology.Frontiers in cell and developmental biology · 2026Article
- Artificial intelligence-based quantification of retinal microvascular biomarkers from fundus photography of chronic kidney disease: a case-control study.Frontiers in medicine · 2026Article
- Artificial intelligence-quantified fundus tessellated density and its association with myopia severity in Chinese children: a cross-sectional study.Frontiers in cell and developmental biology · 2026Article
- Predicting myopia risk using a machine learning model based on fundus imageomics.Scientific reports · 2025Article
- Correlation between atherogenic index of plasma and retinal vessels in the fundus: a cross-sectional study.European journal of medical research · 2025Article
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- Diabetic retinopathy detection via exudates and hemorrhages segmentation using iterative NICK thresholding, watershed, and ChiScientific reports · 2025Article
- The Associations Between Myopia and Fundus Tessellation in School Children: A Comparative Analysis of Macular and Peripapillary Regions Using Deep Learning.Translational vision science & technology · 2025Article
- Quantitative evaluation of retinal vascular parameters among highland and lowland native children using artificial intelligence.Frontiers in cell and developmental biology · 2025Article
- Article
- Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence.BMJ open ophthalmology · 2024Article
- RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis.NPJ digital medicine · 2024Article
- Quantitative Assessment of Fundus Tessellated Density in Highly Myopic Glaucoma Using Deep Learning.Translational vision science & technology · 2024Article
- Distinctive Imaging Characteristics of Retinal and Cerebral Vessels between Central and Branch Retinal Vein Occlusion by MRI and AI-Based Image Analyzer.Diagnostics (Basel, Switzerland) · 2024Article
- Quantitative analysis of retinal vascular parameters changes in school-age children with refractive error using artificial intelligence.Frontiers in medicine · 2024Article
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9 authors.
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Abstract
backgroundWith the diabetes mellitus (DM) prevalence increasing annually, the human grading of retinal images to evaluate DR has posed a substantial burden worldwide. SmartEye is a recently developed fundus image processing and analysis system with lesion quantification function for DR screening. It is sensitive to the lesion area and can automatically identify the lesion position and size. We reported the diabetic retinopathy (DR) grading results of SmartEye versus ophthalmologists in analyzing images captured with non-mydriatic fundus cameras in community healthcare centers, as well as DR lesion quantitative analysis results on different disease stages.
methodsThis is a cross-sectional study. All the fundus images were collected from the Shanghai Diabetic Eye Study in Diabetics (SDES) program from Apr 2016 to Aug 2017. 19,904 fundus images were acquired from 6013 diabetic patients. The grading results of ophthalmologists and SmartEye are compared. Lesion quantification of several images at different DR stages is also presented.
resultsThe sensitivity for diagnosing no DR, mild NPDR (non-proliferative diabetic retinopathy), moderate NPDR, severe NPDR, PDR (proliferative diabetic retinopathy) are 86.19, 83.18, 88.64, 89.59, and 85.02%. The specificity are 63.07, 70.96, 64.16, 70.38, and 74.79%, respectively. The AUC are PDR, 0.80 (0.79, 0.81); severe NPDR, 0.80 (0.79, 0.80); moderate NPDR, 0.77 (0.76, 0.77); and mild NPDR, 0.78 (0.77, 0.79). Lesion quantification results showed that the total hemorrhage area, maximum hemorrhage area, total exudation area, and maximum exudation area increase with DR severity.
conclusionsSmartEye has a high diagnostic accuracy in DR screening program using non-mydriatic fundus cameras. SmartEye quantitative analysis may be an innovative and promising method of DR diagnosis and grading.
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