Automated Screening for Retinopathy of Prematurity

Age: < 18

Sex: Any

Trial Phase: Academic

Sponsor: Siloam Vision

Disqualifiers: Age over 39 weeks, Congenital abnormality, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Approved in 1 Jurisdiction This treatment is already approved in other countries

Compensation: Varies

Number of Visits: Unknown

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a new system called i-ROP DL (Imaging and Informatics in Retinopathy of Prematurity Deep Learning) that assists doctors in detecting retinopathy of prematurity (ROP), a serious eye condition in newborns. The trial aims to evaluate how effectively this system identifies more than mild ROP by analyzing images taken in the NICU. Researchers use a large dataset of images from a previous telemedicine study for comparison. Newborns weighing less than 1251 grams at birth who qualify for ROP screening may be suitable candidates for this trial. As an unphased trial, it offers a unique opportunity to advance medical technology for newborn eye care.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.

What prior data suggests that the i-ROP DL system is safe for autonomous ROP screening?

Research has shown that the i-ROP DL system, which uses artificial intelligence to screen for retinopathy of prematurity (ROP), is generally safe. One study found that the system accurately identified cases needing attention with few false alarms. It has been tested across various groups and consistently detected ROP accurately.

Although the studies did not focus on safety like drug trials, the i-ROP DL system only analyzes images and does not involve direct treatment, indicating low risk for participants. Previous research has confirmed its effectiveness, making it a promising tool for detecting ROP. For those considering participation in a trial with this system, current evidence suggests it is well-tolerated and safe for screening. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the iROP DL treatment because it uses advanced artificial intelligence to automatically screen for retinopathy of prematurity (ROP), a serious eye condition in premature infants. Unlike traditional methods that rely heavily on manual examination by specialists, iROP DL leverages deep learning algorithms to efficiently and accurately detect ROP, potentially improving early detection and treatment. This innovative approach could streamline the screening process, reduce the workload on healthcare professionals, and ensure more consistent and timely diagnoses, ultimately leading to better outcomes for infants at risk.

What evidence suggests that the i-ROP DL system is effective for ROP screening?

Research has shown that the i-ROP DL system, which uses advanced computer technology, holds promise for detecting retinopathy of prematurity (ROP) in newborns. One study found that this system accurately identifies severe cases of ROP, effectively distinguishing when the condition is present or absent. Another study confirmed the system's ability to predict the potential severity of ROP, enabling early intervention by doctors. Overall, the i-ROP DL system has demonstrated potential in effectively screening for ROP, aiding in early detection and possibly improving outcomes. Participants in this trial will undergo assessment with the i-ROP DL system to evaluate its effectiveness in screening for ROP. ^{2 3 4 6 7}

Who Is on the Research Team?

John P Campbell, MD/MPH

Principal Investigator

Oregon Health and Science University

Are You a Good Fit for This Trial?

This trial is for babies born prematurely who are in the NICU and need screening for a serious eye condition called Retinopathy of Prematurity (ROP). The study will use images from previous research to test a new automated system.

Inclusion Criteria

Eligible subjects under protocol e-ROP, defined as Infants with birth weight (BW) less than 1251 g meeting current ROP screening

All examinations will be eligible for inclusion regardless of the e-ROP label for image quality

Cases are collected during the telemedicine-based remote digital fundus imaging (RDFI-TM) evaluations

Exclusion Criteria

My baby was in the NICU for retinopathy of prematurity that is getting better or was treated.

Major ocular or systemic congenital abnormality

Significant media opacity precluding visualization of the retina

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Image Acquisition and Analysis

Digital images are acquired by a technician and analyzed by the i-ROP DL system to detect more than mild ROP

6 months

Weekly image acquisition

Follow-up

Participants are monitored for safety and effectiveness after image analysis, with follow-up appointments scheduled based on screening results

6 months

Weekly follow-up appointments

What Are the Treatments Tested in This Trial?

Interventions

• iROP DL

Trial Overview The i-ROP DL system, an automated device designed to detect severe ROP in premature infants using images, is being tested. Its accuracy will be compared with standard image-based diagnoses.

How Is the Trial Designed?

Group I: eROP data Experimental Treatment 1 Intervention

iROP DL is already approved in United States for the following indications:

Approved in United States as i-ROP DL for:

• Detection of more-than-mild retinopathy of prematurity (ROP)

Who Is Running the Clinical Trial?

Siloam Vision

Lead Sponsor

Trials

2

Recruited

1,300+

National Eye Institute (NEI)

Collaborator

Trials

572

Recruited

1,320,000+

Citations

1. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC8364291/

Deep Learning for the Diagnosis of Stage in Retinopathy ... The purpose of this study was to implement a convolutional neural network (CNN) for binary detection of stage 1–3 in ROP and evaluate its generalizability ...

2. ophthalmologyscience.org ophthalmologyscience.org/article/S2666-9145(22)00011-2/fulltext

Improved Training Efficiency for Retinopathy of Prematurity ... Improved training efficiency for retinopathy of prematurity deep learning models using comparison versus class labels.

3. jamanetwork.com jamanetwork.com/journals/jamanetworkopen/fullarticle/2793479

A Deep Learning Model to Predict the Occurrence and ... This prognostic study validates a deep learning system to predict the occurrence and severity of retinopathy of prematurity (ROP) before 45 ...

4. aao.org aao.org/education/editors-choice/autonomous-deep-learning-system-shows-potential-de

An Autonomous Deep-Learning System Shows Potential ... Outcomes. The iROP DL system showed good sensitivity and specificity in detecting mtm ROP and type 1 ROP in both the SUNDROP and ACES cohorts.

5. dl.acm.org dl.acm.org/doi/10.1145/3596223

Deep Learning-assisted Retinopathy of Prematurity (ROP) ... The ROP classification algorithm uses OD as a reference point to determine the degree and progression of a disease based on the extent of blood vessels. In ...

6. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC7891847/

Imaging in Retinopathy of Prematurity - PMC Pretests and posttests were administered to ophthalmology residents in the US and Canada, and results showed improved diagnostic accuracy and reliability of ROP ...

7. sciencedirect.com sciencedirect.com/science/article/pii/S258975002300050X

Development and international validation of custom- ... Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed through interval screening by paediatric ophthalmologists.