ArticleNPJ digital medicine2024
The performance of a deep learning system in assisting junior ophthalmologists in diagnosing 13 major fundus diseases: a prospective multi-center clinical trial.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04723160 (A Prospective, Multicenter, Blinded Reading, Self Controlled, Superiority Priority Clinical Trial of Assisted Fundus Image Diagnosis Software for the Diagnosis of Multiple Eye Fundus Diseases), which is not on this map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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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.
A Prospective, Multicenter, Blinded Reading, Self Controlled, Superiority Priority Clinical Trial of Assisted Fundus Image Diagnosis Software for the Diagnosis of Multiple Eye Fundus Diseases
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Healthcare analytics perspectives on artificial intelligence-based retinopathy of prematurity screening: a bibliometric mapping study.Frontiers in research metrics and analytics · 2026Pooled it
- Evaluating an AI-assisted triage workflow for retinal diseases.PLOS digital health · 2026Article
- Evaluating Artificial Intelligence as a First-Pass Reader in Fundus Photograph Screening: A Multireader Workflow Validation Study.Diagnostics (Basel, Switzerland) · 2026Article
- Integrated Implementation Strategies to Promote the Use of AI-Assisted Diagnostic Software for Lung Nodule Screening in China: Process Evaluation Based on the RE-AIM Framework.JMIR formative research · 2026Article
- Evaluating the diagnostic performance of artificial intelligence-assisted decision-making software for pulmonary nodules in a resource-limited setting.Journal of clinical imaging science · 2026Article
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- ROFI: a deep learning-based ophthalmic sign-preserving and reversible patient face anonymizer.NPJ digital medicine · 2025Article
- Enhancing diagnostic accuracy in rare and common fundus diseases with a knowledge-rich vision-language model.Nature communications · 2025Article
- Ultra-wide-field fundus photography and AI-based screening and referral for multiple ocular fundus diseases.Cell reports. Medicine · 2025Article
- Integrated visual and text-based analysis of ophthalmology clinical cases using a large language model.Scientific reports · 2025Article
- Impact of Artificial Intelligence on the Knowledge, Attitude, and Performance of Ophthalmology Residents: A Systematic Review.Journal of ophthalmic & vision research · 2025Article
- EfficientNetB0-Based End-to-End Diagnostic System for Diabetic Retinopathy Grading and Macular Edema Detection.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Deep learning-based classification of multiple fundus diseases using ultra-widefield images.Frontiers in cell and developmental biology · 2025Article
- Large language models for disease diagnosis: a scoping review.NPJ artificial intelligence · 2025Review
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- Integrating artificial intelligence in strabismus management: current research landscape and future directions.Experimental biology and medicine (Maywood, N.J.) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI)-based diagnostic systems have been reported to improve fundus disease screening in previous studies. This multicenter prospective self-controlled clinical trial aims to evaluate the diagnostic performance of a deep learning system (DLS) in assisting junior ophthalmologists in detecting 13 major fundus diseases. A total of 1493 fundus images from 748 patients were prospectively collected from five tertiary hospitals in China. Nine junior ophthalmologists were trained and annotated the images with or without the suggestions proposed by the DLS. The diagnostic performance was evaluated among three groups: DLS-assisted junior ophthalmologist group (test group), junior ophthalmologist group (control group) and DLS group. The diagnostic consistency was 84.9% (95%CI, 83.0% ~ 86.9%), 72.9% (95%CI, 70.3% ~ 75.6%) and 85.5% (95%CI, 83.5% ~ 87.4%) in the test group, control group and DLS group, respectively. With the help of the proposed DLS, the diagnostic consistency of junior ophthalmologists improved by approximately 12% (95% CI, 9.1% ~ 14.9%) with statistical significance (P < 0.001). For the detection of 13 diseases, the test group achieved significant higher sensitivities (72.2% ~ 100.0%) and comparable specificities (90.8% ~ 98.7%) comparing with the control group (sensitivities, 50% ~ 100%; specificities 96.7 ~ 99.8%). The DLS group presented similar performance to the test group in the detection of any fundus abnormality (sensitivity, 95.7%; specificity, 87.2%) and each of the 13 diseases (sensitivity, 83.3% ~ 100.0%; specificity, 89.0 ~ 98.0%). The proposed DLS provided a novel approach for the automatic detection of 13 major fundus diseases with high diagnostic consistency and assisted to improve the performance of junior ophthalmologists, resulting especially in reducing the risk of missed diagnoses. ClinicalTrials.gov NCT04723160.
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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.