Artificial Intelligence for ROP Screening and to Assess Quality of Care: Progress and Challenges.

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

3 citing papers in PubMed.

1. Prediction of retinopathy of prematurity using machine learning models.Pediatric research · 2026
   Article
2. Retinopathy of prematurity in India - what can we learn from the polio legacy?The Lancet regional health. Southeast Asia · 2023
   Review
3. Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population.Frontiers in pediatrics · 2023
   Article

Corrections and comments

• Comment on
  Applications of Artificial Intelligence for Retinopathy of Prematurity Screening.2021

Authors and funding

3 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

Indexed as