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Observational Study of Retinopathy of Prematurity (ROP) and AI Diagnosis

This study is looking at Retinopathy of Prematurity (ROP), an eye condition affecting premature babies, which can lead to blindness if not caught early. Researchers are not testing a new treatment, but instead are evaluating how well an artificial intelligence (AI) system can diagnose ROP by looking at eye images. You or your baby would be eligible if your baby is hospitalized in a participating Neonatal Intensive Care Unit (NICU) and meets standard criteria for ROP screening. The goal is to improve how ROP is diagnosed and managed using AI and image data. This study is currently recruiting and aims to include 2000 eye exams from 375 babies.

Study design
This is an observational study, meaning no new interventions are given. It aims to enroll 2000 eye exams from 375 participants.
What's involved
Eye exams are standard of care and would be performed regardless of participation. The study will collect wide-angle retinal images during these exams.
Compensation
Not stated in the trial record.
Follow-up
The diagnostic accuracy of the AI system will be evaluated at 4 years.

AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.

NCT04420156

Clinical and Genetic Analysis of ROP

Recruiting
Not specifiedUp to 1Observational
Oregon Health and Science University
~2,000 participants
Updated 2022-04-20 on ClinicalTrials.gov
What's tested:No intervention administered.

At a glance

Recruiting sites
3 of 5 listed sites are recruiting right now
RecruitingSuspended, closed, or not yet open
What they're measuring
Evaluate diagnostic accuracy of an AI system for ROP diagnosis
Measured over 4 years

Conditions

Retinopathy of Prematurity

Explore related trials

Retinopathy of Prematurity

NCT04420156

Where you'd take part

This study runs at 5 sites. They're the same protocol — you choose where, and that choice sets who your contact draft is addressed to.

  • Oregon Health & Science University

    Portland, Oregonstudy coordinator listed

    Recruiting

  • Stanford University

    Palo Alto, Californiastudy coordinator listed

    Not yet recruiting

  • University of Illinois Chicago

    Chicago, Illinoisstudy coordinator listed

    Recruiting

  • University of Utah

    Salt Lake City, Utahstudy coordinator listed

    Not yet recruiting

  • William Beaumont Hospital

    Royal Oak, Michiganstudy coordinator listed

    Recruiting

Sites open and close at different times, so the status above is per site — it can differ from the study's overall status.

Study leadership

  • John P Campbell, M.D. · PRINCIPAL_INVESTIGATOR · Oregon Health and Science University

Who to contact

John P Campbell, M.D.
[email protected]
503-494-7891
Email the study team

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Eligibility criteria

Inclusion

All infants hospitalized at participating Neonatal Intensive Care Units will be eligible for the study if they meet plublished criteria for requiring ROP screening examination, or if they are transferred to the study center for specialized ophthalmic care. These eligibility criteria are identical at each study center, and match what is done in standard clinical practice according to national guidelines published jointly by the American Academy of Pediatrics, American Academy of Ophthalmology, and American Associatioin for Pediatric Ophthalmology and Strabismus (AAP-AAO, Pediatrics, 2013).

Exclusion

Patients will be excluded if they have structural ocular anomalies, or if they are considered unstable for examintion by their attending neonatologist.

What this trial measures

  • Evaluate diagnostic accuracy of an AI system for ROP diagnosis4 years

    Premature babies are examined for retinopathy of prematurity (ROP), a potentially blinding diesese. As a standard of care, retinal images are taken during ROP examinations. This research group has collected a repository of images over the past 9 years and with those images, the investigators have developed an artificial intelligence (AI) system that has the ability to diagnose severe ROP with high accuracy. The primary outcome measure in continuing to recruit subjects for this study is to collect more images to improve the existing AI system and expand the ability to diagnose ROP.