SynthesisEye (London, England)2023
Artificial intelligence for the diagnosis of retinopathy of prematurity: A systematic review of current algorithms.
Synthesis in Eye (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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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.
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Who cites it
23 citing papers in PubMed.
- Cost-effectiveness of community-based artificial intelligence screening for retinopathy of prematurity.Journal of global health · 2026Article
- Artificial intelligence in pediatric myopia - a narrative review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Clinical Performance and Implementation of AI-Enabled Paediatric Ophthalmic Screening, Triage, Diagnosis, and Surveillance in Primary, Community, and Referral-Linked Pathways: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Automated diagnosis of early-stage retinopathy of prematurity.Eye (London, England) · 2026Article
- Deep learning algorithms for timely diagnosis of retinopathy of prematurity requiring treatment.Eye (London, England) · 2026Article
- Automatic Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Learning.Beyoglu eye journal · 2026Article
- Artificial intelligence and robotic surgery in clinical medicine: progress, challenges, and future directions.Future science OA · 2025Review
- Validation of a screening score model to predict the development of retinopathy of prematurity.Scientific reports · 2025Article
- Enhanced performance in automated diabetic retinopathy diagnosis achieved through Voronoi diagrams and artificial intelligence.Scientific reports · 2025Article
- Improved linear growth after routine zinc supplementation in preterm very low birth weight infants.Pediatric research · 2025Article
- Re-identification of patients from imaging features extracted by foundation models.NPJ digital medicine · 2025Article
- Application of Artificial Intelligence in Retinopathy of Prematurity From 2010 to 2023: A Bibliometric Analysis.Health science reports · 2025Article
- AI-Enabled Screening for Retinopathy of Prematurity in Low-Resource Settings.JAMA network open · 2025Article
- Accuracy of Artificial Intelligence Versus Clinicians in Real-Life Case Scenarios of Retinopathy of Prematurity.Cureus · 2025Article
- Artificial Intelligence and Machine Learning in Ocular Oncology, Retinoblastoma (ArMOR): Experience with a Multiracial Cohort.Cancers · 2024Article
- A review on retinopathy of prematurity.Medical hypothesis, discovery & innovation ophthalmology journal · 2024Review
- Intelligent diagnosis of retinal vein occlusion based on color fundus photographs.International journal of ophthalmology · 2024Article
- Retinopathy of prematurity: from oxygen management to molecular manipulation.Molecular and cellular pediatrics · 2023Review
- Screening and surveillance for retinopathy of prematurity: A Wilson and Jungner framework approach.Journal of global health · 2023Article
- Latest Trends in Retinopathy of Prematurity: Research on Risk Factors, Diagnostic Methods and Therapies.International journal of general medicine · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesWith the increasing survival of premature infants, there is an increased demand to provide adequate retinopathy of prematurity (ROP) services. Wide field retinal imaging (WFDRI) and artificial intelligence (AI) have shown promise in the field of ROP and have the potential to improve the diagnostic performance and reduce the workload for screening ophthalmologists. The aim of this review is to systematically review and provide a summary of the diagnostic characteristics of existing deep learning algorithms. SUBJECT/
methodsTwo authors independently searched the literature, and studies using a deep learning system from retinal imaging were included. Data were extracted, assessed and reported using PRISMA guidelines.
resultsTwenty-seven studies were included in this review. Nineteen studies used AI systems to diagnose ROP, classify the staging of ROP, diagnose the presence of pre-plus or plus disease, or assess the quality of retinal images. The included studies reported a sensitivity of 71%-100%, specificity of 74-99% and area under the curve of 91-99% for the primary outcome of the study. AI techniques were comparable to the assessment of ophthalmologists in terms of overall accuracy and sensitivity. Eight studies evaluated vascular severity scores and were able to accurately differentiate severity using an automated classification score.
conclusionArtificial intelligence for ROP diagnosis is a growing field, and many potential utilities have already been identified, including the presence of plus disease, staging of disease and a new automated severity score. AI has a role as an adjunct to clinical assessment; however, there is insufficient evidence to support its use as a sole diagnostic tool currently.
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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.