ArticleEBioMedicine2018
Automated retinopathy of prematurity screening using deep neural networks.
Article in EBioMedicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis that pooled it.
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Who cites it
61 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for the diagnosis of retinopathy of prematurity: A systematic review of current algorithms.Eye (London, England) · 2023Pooled it
- Review
- Artificial intelligence in pediatrics: a bibliometric analysis of global output, networks, and frontiers [2016-2025].Translational pediatrics · 2026Article
- Deep learning algorithms for timely diagnosis of retinopathy of prematurity requiring treatment.Eye (London, England) · 2026Article
- AI models in clinical neonatology: a review of modeling approaches and a consensus proposal for standardized reporting of model performance.Pediatric research · 2025Review
- Prediction of retinopathy of prematurity development and treatment need with machine learning models.BMC ophthalmology · 2025Article
- AI-Enabled Screening for Retinopathy of Prematurity in Low-Resource Settings.JAMA network open · 2025Article
- Deep learning applications for diabetic retinopathy and retinopathy of prematurity diseases diagnosis: a systematic review.International journal of ophthalmology · 2025Review
- Computer-aided detection of retinopathy of prematurity severity assessment via vessel tortuosity measurement in preterm infants' fundus images.Eye (London, England) · 2024Article
- Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities.International ophthalmology clinics · 2024Review
- Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning.Eye (London, England) · 2024Article
- An Interpretable System for Screening the Severity Level of Retinopathy in Premature Infants Using Deep Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Retinal Image Dataset of Infants and Retinopathy of Prematurity.Scientific data · 2024Article
- Assessing spectral effectiveness in color fundus photography for deep learning classification of retinopathy of prematurity.Journal of biomedical optics · 2024Article
- Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence.Journal of personalized medicine · 2024Review
- Multi-risk factors joint prediction model for risk prediction of retinopathy of prematurity.The EPMA journal · 2024Article
- Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.Medicina (Kaunas, Lithuania) · 2024Review
- Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics.Scientific reports · 2024Article
- Automatic zoning for retinopathy of prematurity with a key area location system.Biomedical optics express · 2024Article
- Development and validation of a semi-supervised deep learning model for automatic retinopathy of prematurity staging.iScience · 2024Article
1 more citing papers are in PubMed but not listed here.
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9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRetinopathy of prematurity (ROP) is the leading cause of childhood blindness worldwide. Automated ROP detection system is urgent and it appears to be a safe, reliable, and cost-effective complement to human experts.
methodsAn automated ROP detection system called DeepROP was developed by using Deep Neural Networks (DNNs). ROP detection was divided into ROP identification and grading tasks. Two specific DNN models, i.e., Id-Net and Gr-Net, were designed for identification and grading tasks, respectively. To develop the DNNs, large-scale datasets of retinal fundus images were constructed by labeling the images of ROP screenings by clinical ophthalmologists.
findingsOn the test dataset, the Id-Net achieved a sensitivity of 96.62%(95%CI, 92.29%-98.89%) and a specificity of 99.32% (95%CI, 96.29%-9.98%) for ROP identification while the Gr-Net attained sensitivity and specificity values of 88.46% (95%CI, 96.29%-99.98%) and 92.31% (95%CI, 81.46%-97.86%), respectively, on the ROP grading task. On another 552 cases, the developed DNNs outperformed some human experts. In a clinical setting, the sensitivity and specificity values of DeepROP for ROP identification were 84.91% (95%CI, 76.65%-91.12%) and 96.90% (95%CI, 95.49%-97.96%), respectively, whereas the corresponding measures for ROP grading were 93.33%(95%CI, 68.05%-99.83%) and 73.63%(95%CI, 68.05%-99.83%), respectively.
interpretationWe constructed large-scale ROP datasets with adequate clinical labels and proposed novel DNN models. The DNN models can directly learn ROP features from big data. The developed DeepROP is potential to be an efficient and effective system for automated ROP screening. FUND: National Natural Science Foundation of China under Grant 61432012 and U1435213.
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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.