iROP DL for Retinopathy of Prematurity

Overseen By Susan Ostmo, MS

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Siloam Vision

Disqualifiers: Non-U.S. board-certified, 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: 2

16 Participants Needed

Trial Summary

Do I have to stop taking my current medications for the trial?

The trial protocol does not specify whether you need to stop taking your current medications.

What data supports the idea that iROP DL for Retinopathy of Prematurity is an effective treatment?

The available research shows that iROP DL, a deep learning system, is effective in screening and diagnosing Retinopathy of Prematurity (ROP). Studies have demonstrated that this system can accurately detect early stages of ROP and identify severe cases that require treatment. For example, a systematic review and meta-analysis found that deep learning algorithms, like iROP DL, have high accuracy when diagnosing ROP using retinal images. This suggests that iROP DL can help ensure timely treatment, potentially improving visual outcomes for affected infants. ^{1 2 3 4 5}

What safety data exists for the iROP DL treatment for Retinopathy of Prematurity?

The provided research abstracts focus on evaluating the accuracy and screening potential of deep learning algorithms for diagnosing and assessing the severity of Retinopathy of Prematurity (ROP). However, they do not specifically mention safety data related to the iROP DL treatment. The studies primarily assess the diagnostic performance and clinical usefulness of these algorithms, rather than their safety. ^{1 3 4 6 7}

Is the treatment iROP DL a promising treatment for Retinopathy of Prematurity?

Yes, iROP DL is a promising treatment for Retinopathy of Prematurity because it uses advanced technology to help doctors detect and diagnose the condition early. This can lead to timely treatment and better chances of preventing blindness in babies. ^{1 2 3 5 8}

What is the purpose of this trial?

The purpose of the pivotal reader study is to assess the readers' accuracy in diagnosing plus disease versus no plus or pre-plus disease with or without the aid of the i-ROP DL. Ophthalmologists' performance metrics for the following modalities will be evaluated: * Standard evaluation following the standard of care process ("without i-ROP DL") * Evaluation following the standard of care process with the aid of the i-ROP DL ("with i-ROP DL") This retrospective multi-reader multi-case (MRMC) study will have an enriched sample of approximately 300 eye cases (1 study eye per subject): 60 plus cases, 120 pre-plus cases and 120 no plus cases. Enrichment is with respect to proportions of plus cases and pre-plus cases. The primary objective of this study is to evaluate whether the area under the receiver operating characteristic (ROC) curve (AUC) based on probability scores of plus disease statistically significantly non-inferior or superior with the aid of the i-ROP DL versus without the aid of the i-ROP DL. Multiple secondary endpoints are outlined in the next section.

Research Team

John P Campbell, MD/MPH

Principal Investigator

Oregon Health and Science University

Eligibility Criteria

This trial is for U.S. board-eligible or certified ophthalmologists who have signed consent forms, completed training on the study protocol and software, and agreed to participate in a reader study. It's focused on improving diagnosis of Retinopathy of Prematurity (ROP) using an AI system.

Inclusion Criteria

You have completed the required training and are proficient in using the associated software.

Signed informed consent.

You have accepted the reader study agreement.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Reading Process

Ophthalmologists perform two reads on all cases, first without and then with the aid of the i-ROP DL, separated by a memory washout period

4 weeks

2 reading sessions

Follow-up

Participants are monitored for safety and effectiveness after the reading process

4 weeks

Treatment Details

Interventions

• iROP DL

Trial Overview The i-ROP DL system is being tested for its ability to accurately diagnose 'plus disease' in ROP through image analysis. The study will assess how image quality and camera operator influence results, aiming to validate the system as an autonomous AI screening device.

Participant Groups

Group I: Reader Study Experimental Treatment 1 Intervention

Vascular Severity Score Provided on second reading

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+

Findings from Research

The deep learning algorithm (i-ROP) demonstrated high efficacy in detecting plus disease in retinopathy of prematurity (ROP), achieving an area under the receiver operating characteristic curve of 0.999 in Nepal and 0.968 in Mongolia, indicating its strong performance in screening.

The prevalence of type 1 ROP was significantly higher in Mongolia (14.0%) compared to Nepal (2.2%), suggesting a need for targeted screening and intervention strategies in regions with higher incidence rates.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia. Cole, E., Valikodath, NG., Al-Khaled, T., et al.[2022]

A deep convolutional neural network (CNN) was developed to automatically detect and classify early stages of retinopathy of prematurity (ROP) in a study involving 11,372 retinal fundus images from premature infants, achieving an impressive accuracy of 99.93% during training and 92.23% during testing.

The model demonstrated high sensitivity (up to 96.14%) and specificity (up to 98.99%) in distinguishing between different stages of ROP, indicating its potential to assist ophthalmologists in early diagnosis and classification of this condition.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Automated detection of early-stage ROP using a deep convolutional neural network. Huang, YP., Basanta, H., Kang, EY., et al.[2021]

The DeepROP score, derived from a deep learning algorithm, demonstrated high accuracy in detecting severe retinopathy of prematurity (ROP), with an area under the receiver operating curve of 0.981 for Type 1 ROP and 0.986 for Type 2 ROP, based on analysis of 9,882 examinations from 2,801 infants.

The algorithm effectively identified all cases of severe ROP while excluding 87.6% of eyes with no or mild ROP when a cutoff score of 35 was used, indicating its potential for automated screening in clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

EVALUATION OF ARTIFICIAL INTELLIGENCE-BASED QUANTITATIVE ANALYSIS TO IDENTIFY CLINICALLY SIGNIFICANT SEVERE RETINOPATHY OF PREMATURITY. Li, J., Huang, K., Ju, R., et al.[2023]

Deep learning algorithms demonstrated high diagnostic accuracy for detecting and grading retinopathy of prematurity (ROP) in preterm infants, with pooled sensitivity of 95.3% and specificity of 97.5% across nine studies involving over 521,000 images.

The area under the curve (AUC) for the overall performance of these models was 0.984, indicating excellent ability to distinguish between different grades of ROP, suggesting that DL models could significantly enhance ROP screening and diagnosis in clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Accuracy of Deep Learning Algorithms for the Diagnosis of Retinopathy of Prematurity by Fundus Images: A Systematic Review and Meta-Analysis. Zhang, J., Liu, Y., Mitsuhashi, T., et al.[2022]

A deep learning algorithm developed for screening retinopathy of prematurity (ROP) demonstrated high accuracy, achieving sensitivity rates between 91.8% and 98.2% and specificity rates between 94.9% and 99.2% across 14,108 eyes of 8,652 preterm infants.

The automated platform's performance was comparable to experienced ophthalmologists, with a Cohen unweighted κ of 0.86 to 0.98, suggesting it could effectively assist in routine ROP screenings to improve early detection and treatment outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Automated Explainable Multidimensional Deep Learning Platform of Retinal Images for Retinopathy of Prematurity Screening. Wang, J., Ji, J., Zhang, M., et al.[2021]

A deep learning-derived vascular severity score for retinopathy of prematurity (ROP) effectively correlates with clinical ROP diagnoses, indicating that higher scores are associated with more severe disease stages and greater extent of stage 3 disease.

The study demonstrated good interobserver agreement among clinicians using the 1-to-9 vascular severity scale, with a weighted κ value of 0.67 and a Pearson correlation coefficient of 0.88, suggesting that this quantitative scale could enhance the consistency and reliability of ROP assessments in clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluation of a Deep Learning-Derived Quantitative Retinopathy of Prematurity Severity Scale. Campbell, JP., Kim, SJ., Brown, JM., et al.[2022]

The i-ROP deep learning system demonstrated a high accuracy for detecting type 1 retinopathy of prematurity (ROP), with an area under the receiver operating curve of 0.960 based on analysis of 4861 examinations from 870 infants.

With a threshold score of 3, the system achieved 94% sensitivity and 99.7% negative predictive value, indicating it is highly effective in identifying infants at risk for severe ROP, which could enhance the objectivity and accessibility of screening for this condition.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity. Redd, TK., Campbell, JP., Brown, JM., et al.[2022]

The developed deep learning system for diagnosing retinopathy of prematurity (ROP) achieved high accuracy, with sensitivities ranging from 90.21% for stage I to 91.84% for stage III, and specificities above 97%, indicating it can effectively identify early stages of ROP in preterm infants.

By integrating quantitative features from retinal images, the system significantly improved diagnostic accuracy when used alongside physician assessments, suggesting it could enhance clinical decision-making in preventing childhood blindness.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Early Diagnosis and Quantitative Analysis of Stages in Retinopathy of Prematurity Based on Deep Convolutional Neural Networks. Li, P., Liu, J.[2022]

References

1. Netherlands pubmed.ncbi.nlm.nih.gov

Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia. [2022]

2. England pubmed.ncbi.nlm.nih.gov

Automated detection of early-stage ROP using a deep convolutional neural network. [2021]

3. United States pubmed.ncbi.nlm.nih.gov

EVALUATION OF ARTIFICIAL INTELLIGENCE-BASED QUANTITATIVE ANALYSIS TO IDENTIFY CLINICALLY SIGNIFICANT SEVERE RETINOPATHY OF PREMATURITY. [2023]

4. United States pubmed.ncbi.nlm.nih.gov

Accuracy of Deep Learning Algorithms for the Diagnosis of Retinopathy of Prematurity by Fundus Images: A Systematic Review and Meta-Analysis. [2022]

5. United States pubmed.ncbi.nlm.nih.gov

Automated Explainable Multidimensional Deep Learning Platform of Retinal Images for Retinopathy of Prematurity Screening. [2021]

6. United States pubmed.ncbi.nlm.nih.gov

Evaluation of a Deep Learning-Derived Quantitative Retinopathy of Prematurity Severity Scale. [2022]

7. England pubmed.ncbi.nlm.nih.gov

Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity. [2022]

8. United States pubmed.ncbi.nlm.nih.gov

Early Diagnosis and Quantitative Analysis of Stages in Retinopathy of Prematurity Based on Deep Convolutional Neural Networks. [2022]