ArticleTranslational vision science & technology2022
A Multicenter Clinical Study of the Automated Fundus Screening Algorithm.
Article in Translational vision science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- AI for glaucoma, Are we reporting well? a systematic literature review of DECIDE-AI checklist adherence.Eye (London, England) · 2025Pooled it
- Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections.NPJ digital medicine · 2025Article
- Benchmarking HEp-2 cell segmentation methods in indirect immunofluorescence images - standard models to deep learning.Computers in biology and medicine · 2025Article
- Grading of diabetic retinopathy using a pre-segmenting deep learning classification model: Validation of an automated algorithm.Acta ophthalmologica · 2025Article
- Automated Classification of Physiologic, Glaucomatous, and Glaucoma-Suspected Optic Discs Using Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Development and research status of intelligent ophthalmology in China.International journal of ophthalmology · 2024Review
- Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review.BMJ open diabetes research & care · 2023Article
- Artificial intelligence in ophthalmological practice: when ideal meets reality.BMJ open ophthalmology · 2023Article
- 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
- 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
17 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To evaluate the effectiveness of automated fundus screening software in detecting eye diseases by comparing the reported results against those given by human experts. Results: There were 1585 subjects who completed the procedure and yielded qualified images. The prevalence of referable diabetic retinopathy (RDR), glaucoma suspect (GCS), and referable macular diseases (RMD) were 20.4%, 23.2%, and 49.0%, respectively. The overall sensitivity values for RDR, GCS, and RMD diagnosis are 0.948 (95% confidence interval [CI], 0.918-0.967), 0.891 (95% CI, 0.855-0.919), and 0.901 (95% CI-0.878, 0.920), respectively. The overall specificity values for RDR, GCS, and RMD diagnosis are 0.954 (95% CI, 0.915-0.965), 0.993 (95% CI-0.986, 0.996), and 0.955 (95% CI-0.939, 0.968), respectively. Methods: We prospectively enrolled 1743 subjects at seven hospitals throughout China. At each hospital, an operator records the subjects' information, takes fundus images, and submits the images to the Image Reading Center of Zhongshan Ophthalmic Center, Sun Yat-Sen University (IRC). The IRC grades the images according to the study protocol. Meanwhile, these images will also be automatically screened by the artificial intelligence algorithm. Then, the analysis results of automated screening algorithm are compared against the grading results of IRC. The end point goals are lower bounds of 95% CI of sensitivity values that are greater than 0.85 for all three target diseases, and lower bounds of 95% CI of specificity values that are greater than 0.90 for RDR and 0.85 for GCS and RMD. Conclusions: Automated fundus screening software demonstrated a high sensitivity and specificity in detecting RDR, GCS, and RMD from color fundus imaged captured using various cameras. Translational Relevance: These findings suggest that automated software can improve the screening effectiveness for eye diseases, especially in a primary care context, where experienced ophthalmologists are scarce.
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