Incorporating AI Into Global ROP Care

Advances in imaging and machine learning offer new opportunities to standardize diagnosis and improve access worldwide.

By: Amy Song, BA, Noelle Whitestone, MHA, Karyn E. Jonas, MSN, J. Peter Campbell, MD, MPH, R.V. Paul Chan, MD, MSc, MBA, FACS

Retinal Physician September 1, 2025 Vol 22, Issue September 2025 Page(s): 13-15

Clinical Report: Incorporating AI Into Global ROP Care

Overview

Artificial intelligence (AI) offers promising advancements in the diagnosis and management of retinopathy of prematurity (ROP), particularly in low-resource settings. AI systems have demonstrated high sensitivity and accuracy in detecting ROP, potentially improving access to care and standardizing diagnoses across diverse clinical environments.

Background

Retinopathy of prematurity (ROP) is a significant cause of childhood blindness, particularly affecting premature infants. The disease's management is often hindered by a lack of trained personnel and resources, especially in low-income and middle-income countries. The integration of AI into ROP care could address these challenges by enhancing diagnostic accuracy and facilitating remote screening, thereby improving outcomes for affected infants.

Data Highlights

No specific numerical data provided in the article.

Key Findings

AI systems can standardize ROP diagnosis and improve access to care in underserved regions.
The i-ROP deep learning system achieved 100% sensitivity for detecting more than mild ROP in telemedicine programs.
AI algorithms developed in China and India have demonstrated high accuracy in identifying ROP and recommending treatment options.
Variability in ROP diagnosis among clinicians highlights the need for standardized diagnostic tools.
Implementation of AI in ROP care faces regulatory and infrastructural challenges that must be addressed for successful integration.

Clinical Implications

Healthcare providers should consider the potential of AI technologies to enhance ROP screening and diagnosis, particularly in resource-limited settings. Training and infrastructure development will be crucial for the effective implementation of these systems in clinical practice.

Conclusion

The incorporation of AI into ROP care presents a significant opportunity to improve diagnostic accuracy and access to treatment, particularly in low-resource environments. Addressing implementation challenges will be essential for realizing these benefits.

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