ArticleAnnals of translational medicine2022
Performance of the AIDRScreening system in detecting diabetic retinopathy in the fundus photographs of Chinese patients: a prospective, multicenter, clinical study.
Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Diabetic Retinopathy Screening Approaches in Developing Countries: A Systematic Review and Meta-Analysis.Turkish journal of ophthalmology · 2025Pooled it
- Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.Journal of medical Internet research · 2025Pooled it
- Clinical setting-dependent diagnostic accuracy of artificial intelligence and store-and-forward diabetic retinopathy screening: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections.NPJ digital medicine · 2025Article
- Reducing the workload of medical diagnosis through artificial intelligence: A narrative review.Medicine · 2025Review
- Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research.Ophthalmology and therapy · 2025Article
- Effects of artificial intelligence implementation on efficiency in medical imaging-a systematic literature review and meta-analysis.NPJ digital medicine · 2024Review
- Diabetes management in the era of artificial intelligence.Archives of medical sciences. Atherosclerotic diseases · 2024Article
- Artificial Intelligence and Diabetic Retinopathy: AI Framework, Prospective Studies, Head-to-head Validation, and Cost-effectiveness.Diabetes care · 2023Review
- Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review.BMJ open diabetes research & care · 2023Article
- Diagnostic test accuracy of artificial intelligence in screening for referable diabetic retinopathy in real-world settings: A systematic review and meta-analysis.PLOS global public health · 2023Article
- Prospective studies on artificial intelligence (AI)-based diabetic retinopathy screening.Annals of translational medicine · 2022Article
- Application of deep learning algorithms for diabetic retinopathy screening.Annals of translational medicine · 2022Article
- Artificial intelligence for diabetic retinopathy screening: beyond diagnostic accuracy.Annals of translational medicine · 2022Article
- Discriminative, generative artificial intelligence, and foundation models in retina imaging.Taiwan journal of ophthalmologyReview
Corrections and comments
- Commented on by
- Commented on by
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Diabetic retinopathy (DR) is the leading cause of blindness in the working-age population worldwide, and there is a large unmet need for DR screening in China. This observational, prospective, multicenter, gold standard-controlled study sought to evaluate the effectiveness and safety of the AIDRScreening system (v. 1.0), which is an artificial intelligence (AI)-enabled system that detects DR in the Chinese population based on fundus photographs. Methods: Participants with diabetes mellitus (DM) were recruited. Fundus photographs (field 1 and field 2) of 1 eye in each participant were graded by the AIDRScreening system (v. 1.0) to detect referable DR (RDR). The results were compared to those of the masked manual grading (gold standard) system by the Zhongshan Image Reading Center. The primary outcomes were the sensitivity and specificity of the AIDRScreening system in detecting RDR. The other outcomes evaluated included the system's diagnostic accuracy, positive predictive value, negative predictive value, diagnostic accuracy gain rate, and average diagnostic time gain rate. Results: Among the 1,001 enrolled participants with DM, 962 (96.1%) were included in the final analyses. The participants had a median age of 60.61 years (range: 20.18-85.78 years), and 48.2% were men. The manual grading system detected RDR in 399 (41.48%) participants. The AIDRScreening system had a sensitivity of 86.72% (95% CI: 83.39-90.05%) and a specificity of 96.09% (95% CI: 94.14-97.54%) in the detection of RDR, and a false-positive rate of 3.91%. The diagnostic accuracy gain rate of the AIDRScreening system was 16.57% higher than that of the investigator, while the average diagnostic time gain rate was -37.32% lower. Conclusions: The automated AIDRScreening system can detect RDR with high accuracy, but cannot detect maculopathy. The implementation of the AIDRScreening system may increase the efficiency of DR screening.
Indexed as
Identifiers
What OpenQuestion holds
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.