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Updated: Aug 3, 2025
Retinal Pathophysiological Evaluation in a Rat Model
Published on: May 6, 2022
Epidemiologic Evaluation of Retinopathy of Prematurity Severity in a Large Telemedicine Program in India Using
Mallory A deCampos-Stairiker1, Aaron S Coyner1, Aditi Gupta1
1Ophthalmology, Oregon Health & Science University, Portland, Oregon.
Insights
Retinopathy of prematurity (ROP) severity significantly decreased in South Indian infants over five years, indicating improved neonatal care. Artificial intelligence (AI) effectively tracked these epidemiological changes in ROP.
Area of Science:
- Ophthalmology
- Neonatology
- Artificial Intelligence
Background:
- Retinopathy of prematurity (ROP) epidemiology is influenced by neonatal care standards, oxygen management, and mortality rates.
- Assessing changes in ROP epidemiology requires reliable methods to evaluate disease severity over time.
Purpose of the Study:
- To evaluate the utility of an artificial intelligence (AI) algorithm in assessing ROP severity.
- To analyze changes in ROP epidemiology in South Indian infants over a five-year period using AI.
Main Methods:
- A retrospective cohort study analyzed ROP screening data from 3093 infants across South India's Aravind Eye Care System (AECS).
- Retinal images and clinical data were collected during two distinct periods (2015-2017 and 2019-2020) for tele-ROP screening.
- An AI algorithm assessed ROP severity (Vascular Severity Score - VSS) from fundus images, comparing outcomes between matched cohorts.
Main Results:
- A significant decrease in moderate (type 2) or worse ROP and treatment-requiring ROP (TR-ROP) was observed between the two periods (60.9% to 17.1% and 16.8% to 5.1%, respectively).
- The median AI-derived Vascular Severity Score (VSS) also decreased significantly from 2.9 to 2.4.
- These findings were consistent in infants matched for birthweight and gestational age.
Conclusions:
- The study demonstrates a substantial decline in moderate to severe ROP among South Indian infants, suggesting significant advancements in primary ROP prevention.
- AI-based ROP severity assessment proved effective as an epidemiological tool for monitoring temporal trends in the disease.
- Improvements in neonatal care likely contributed to the observed reduction in ROP incidence and severity.
Purpose:
Epidemiological changes in retinopathy of prematurity (ROP) depend on neonatal care, neonatal mortality, and the ability to carefully titrate and monitor oxygen. We evaluate whether an artificial intelligence (AI) algorithm for assessing ROP severity in babies can be used to evaluate changes in disease epidemiology in babies from South India over a 5-year period.
Design:
Retrospective cohort study.
Participants:
Babies (3093) screened for ROP at neonatal care units (NCUs) across the Aravind Eye Care System (AECS) in South India.
Methods:
Images and clinical data were collected as part of routine tele-ROP screening at the AECS in India over 2 time periods: August 2015 to October 2017 and March 2019 to December 2020. All babies in the original cohort were matched 1:3 by birthweight (BW) and gestational age (GA) with babies in the later cohort. We compared the proportion of eyes with moderate (type 2) or treatment-requiring (TR) ROP, and an AI-derived ROP vascular severity score (from retinal fundus images) at the initial tele-retinal screening exam for all babies in a district, VSS), in the 2 time periods.
Main Outcome Measures:
Differences in the proportions of type 2 or worse and TR-ROP cases, and VSS between time periods.
Results:
Among BW and GA matched babies, the proportion [95% confidence interval {CI}] of babies with type 2 or worse and TR-ROP decreased from 60.9% [53.8%-67.7%] to 17.1% [14.0%-20.5%] (P < 0.001) and 16.8% [11.9%-22.7%] to 5.1% [3.4%-7.3%] (P < 0.001), over the 2 time periods. Similarly, the median [interquartile range] VSS in the population decreased from 2.9 [1.2] to 2.4 [1.8] (P < 0.001).
Conclusions:
In South India, over a 5-year period, the proportion of babies developing moderate to severe ROP has dropped significantly for babies at similar demographic risk, strongly suggesting improvements in primary prevention of ROP. These results suggest that AI-based assessment of ROP severity may be a useful epidemiologic tool to evaluate temporal changes in ROP epidemiology.
Financial Disclosure(S):
Proprietary or commercial disclosure may be found after the references.
