ReviewCurrent opinion in ophthalmology2020
Artificial intelligence for retinopathy of prematurity.
Review in Current opinion in ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis 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
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Artificial intelligence in pediatrics: a bibliometric analysis of global output, networks, and frontiers [2016-2025].Translational pediatrics · 2026Article
- A practical imaging marker to determine the posterior zone II location in premature infants.International journal of ophthalmology · 2026Article
- Prediction of Retinopathy of Prematurity and Treatment in Very Low Birth Weight Infants Using Machine Learning on Nationwide Non-Imaging Clinical Data.Neonatology · 2026Article
- Applications of generative adversarial networks in the diagnosis, prognosis, and treatment of ophthalmic diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Review
- Development and validation of machine learning classifiers for predicting treatment-needed retinopathy of prematurity.BMC medical informatics and decision making · 2025Article
- Diagnostic Accuracy of Pediatrician-performed Digital Retinal Imaging with 3nethra neo for ROP Screening.Indian journal of pediatrics · 2025Article
- The use of neural networks to determine factors affecting the severity and extent of retinopathy in preterm infants.International journal of retina and vitreous · 2025Article
- Accuracy of Artificial Intelligence Versus Clinicians in Real-Life Case Scenarios of Retinopathy of Prematurity.Cureus · 2025Article
- A deep learning-assisted automatic measurement of tear meniscus height on ocular surface images and its application in myopia control.Frontiers in bioengineering and biotechnology · 2025Article
- Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review.Ophthalmology and therapy · 2024Review
- Automatic Detection of 30 Fundus Diseases Using Ultra-Widefield Fluorescein Angiography with Deep Experts Aggregation.Ophthalmology and therapy · 2024Article
- The incidence of retinopathy of prematurity in neonates in Germany in 2019; a nationwide epidemiological cohort study.European journal of pediatrics · 2024Article
- A review on retinopathy of prematurity.Medical hypothesis, discovery & innovation ophthalmology journal · 2024Review
- DeepQuality improves infant retinopathy screening.NPJ digital medicine · 2023Article
- Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.Frontiers in cell and developmental biology · 2023Review
- Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants.Yonsei medical journal · 2022Article
- Synthetic Medical Images for Robust, Privacy-Preserving Training of Artificial Intelligence: Application to Retinopathy of Prematurity Diagnosis.Ophthalmology science · 2022Article
- Foundational Considerations for Artificial Intelligence Using Ophthalmic Images.Ophthalmology · 2022Article
- Prediction of the Fundus Tessellation Severity With Machine Learning Methods.Frontiers in medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
purpose of reviewIn this article, we review the current state of artificial intelligence applications in retinopathy of prematurity (ROP) and provide insight on challenges as well as strategies for bringing these algorithms to the bedside. RECENT
findingsIn the past few years, there has been a dramatic shift from machine learning approaches based on feature extraction to 'deep' convolutional neural networks for artificial intelligence applications. Several artificial intelligence for ROP approaches have demonstrated adequate proof-of-concept performance in research studies. The next steps are to determine whether these algorithms are robust to variable clinical and technical parameters in practice. Integration of artificial intelligence into ROP screening and treatment is limited by generalizability of the algorithms to maintain performance on unseen data and integration of artificial intelligence technology into new or existing clinical workflows. SUMMARY: Real-world implementation of artificial intelligence for ROP diagnosis will require massive efforts targeted at developing standards for data acquisition, true external validation, and demonstration of feasibility. We must now focus on ethical, technical, clinical, regulatory, and financial considerations to bring this technology to the infant bedside to realize the promise offered by this technology to reduce preventable blindness from ROP.
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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.