ArticleThe British journal of ophthalmology2021
Automated detection of early-stage ROP using a deep convolutional neural network.
Article in The British journal of ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 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
- Optimizing Field-of-View for Type-1 Retinopathy of Prematurity via Multiple Instance Learning.Translational vision science & technology · 2026Article
- Review of Retinopathy of Prematurity Management in the Anti-VEGF Era: Evolving Global Paradigms, Persistent Challenges and Our AI-Assisted Future.Clinical & experimental ophthalmology · 2025Review
- Deep learning applications for diabetic retinopathy and retinopathy of prematurity diseases diagnosis: a systematic review.International journal of ophthalmology · 2025Review
- 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
- Assessing spectral effectiveness in color fundus photography for deep learning classification of retinopathy of prematurity.Journal of biomedical optics · 2024Article
- Multi-risk factors joint prediction model for risk prediction of retinopathy of prematurity.The EPMA journal · 2024Article
- Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system.The EPMA journal · 2024Article
- Multicenter Validation of Deep Learning Algorithm ROP.AI for the Automated Diagnosis of Plus Disease in ROP.Translational vision science & technology · 2023Article
- Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review.BMJ open ophthalmology · 2023Review
- Research hotspots and trends in retinopathy of prematurity from 2003 to 2022: a bibliometric analysis.Frontiers in pediatrics · 2023Article
- Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.Frontiers in cell and developmental biology · 2023Review
- Comparison of RetCam and Smartphone-Based Photography for Retinopathy of Prematurity Screening.Diagnostics (Basel, Switzerland) · 2022Article
- Deep Learning for the Diagnosis of Stage in Retinopathy of Prematurity: Accuracy and Generalizability across Populations and Cameras.Ophthalmology. Retina · 2021Article
- Retinopathy of prematurity screening: A narrative review of current programs, teleophthalmology, and diagnostic support systems.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
- Performance of deep-learning artificial intelligence algorithms in detecting retinopathy of prematurity: A systematic review.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
BACKGROUND/
aimTo automatically detect and classify the early stages of retinopathy of prematurity (ROP) using a deep convolutional neural network (CNN).
methodsThis retrospective cross-sectional study was conducted in a referral medical centre in Taiwan. Only premature infants with no ROP, stage 1 ROP or stage 2 ROP were enrolled. Overall, 11 372 retinal fundus images were compiled and split into 10 235 images (90%) for training, 1137 (10%) for validation and 244 for testing. A deep CNN was implemented to classify images according to the ROP stage. Data were collected from December 17, 2013 to May 24, 2019 and analysed from December 2018 to January 2020. The metrics of sensitivity, specificity and area under the receiver operating characteristic curve were adopted to evaluate the performance of the algorithm relative to the reference standard diagnosis.
resultsThe model was trained using fivefold cross-validation, yielding an average accuracy of 99.93%±0.03 during training and 92.23%±1.39 during testing. The sensitivity and specificity scores of the model were 96.14%±0.87 and 95.95%±0.48, 91.82%±2.03 and 94.50%±0.71, and 89.81%±1.82 and 98.99%±0.40 when predicting no ROP versus ROP, stage 1 ROP versus no ROP and stage 2 ROP, and stage 2 ROP versus no ROP and stage 1 ROP, respectively.
conclusionsThe proposed system can accurately differentiate among ROP early stages and has the potential to help ophthalmologists classify ROP at an early stage.
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