ArticleFrontiers in public health2022
Validating automated eye disease screening AI algorithm in community and in-hospital scenarios.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 17 citations in OpenAlex.
- Real-World Performance of Artificial Intelligence in Diabetic Retinopathy Screening: A Systematic Review.Cureus · 2026Review
- Artificial intelligence framework for multi-pathology risk assessment from retinal fundus images: deep learning approach to 15-disease screening.Frontiers in medicine · 2026Article
- Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections.NPJ digital medicine · 2025Article
- Diagnostic utility of artificial intelligence software through non-mydriatic digital retinography in the screening of diabetic retinopathy: an overview of reviews.Eye (London, England) · 2025Article
- Benchmarking HEp-2 cell segmentation methods in indirect immunofluorescence images - standard models to deep learning.Computers in biology and medicine · 2025Article
- The utilization of artificial intelligence in glaucoma: diagnosis versus screening.Frontiers in ophthalmology · 2024Review
- Inter-rater reliability in labeling quality and pathological features of retinal OCT scans: A customized annotation software approach.PloS one · 2024Article
- A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis.Scientific reports · 2023Article
- Corneal Sub-Basal Nerve Plexus in Non-Diabetic Small Fiber Polyneuropathies and the Diagnostic Role of In Vivo Corneal Confocal Microscopy.Journal of clinical medicine · 2023Review
- Artificial intelligence in colposcopic examination: A promising tool to assist junior colposcopists.Frontiers in medicine · 2023Article
- Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023).International journal of ophthalmology · 2023Article
- Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy.Frontiers in endocrinology · 2022Review
- Factors affecting HPV infection in U.S. and Beijing females: A modeling study.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To assess the accuracy and robustness of the AI algorithm for detecting referable diabetic retinopathy (RDR), referable macular diseases (RMD), and glaucoma suspect (GCS) from fundus images in community and in-hospital screening scenarios. Methods: We collected two color fundus image datasets, namely, PUMCH (556 images, 166 subjects, and four camera models) and NSDE (534 images, 134 subjects, and two camera models). The AI algorithm generates the screening report after taking fundus images. The images were labeled as RDR, RMD, GCS, or none of the three by 3 licensed ophthalmologists. The resulting labels were treated as "ground truth" and then were used to compare against the AI screening reports to validate the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the AI algorithm. Results: On the PUMCH dataset, regarding the prediction of RDR, the AI algorithm achieved overall results of 0.950 ± 0.058, 0.963 ± 0.024, and 0.954 ± 0.049 on sensitivity, specificity, and AUC, respectively. For RMD, the overall results are 0.919 ± 0.073, 0.929 ± 0.039, and 0.974 ± 0.009. For GCS, the overall results are 0.950 ± 0.059, 0.946 ± 0.016, and 0.976 ± 0.025. Conclusion: The AI algorithm can work robustly with various fundus camera models and achieve high accuracies for detecting RDR, RMD, and GCS.
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