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. 2023 Aug;130(8):837-843.
doi: 10.1016/j.ophtha.2023.03.026. Epub 2023 Apr 6.

Epidemiologic Evaluation of Retinopathy of Prematurity Severity in a Large Telemedicine Program in India Using Artificial Intelligence

Mallory A deCampos-Stairiker  1 , Aaron S Coyner  1 , Aditi Gupta  1 , Minn Oh  1 , Parag K Shah  2 , Prema Subramanian  2 , Narendran Venkatapathy  2 , Praveer Singh  3 , Jayashree Kalpathy-Cramer  3 , Michael F Chiang  4 , R V Paul Chan  5 , J Peter Campbell  6
Affiliations

Epidemiologic Evaluation of Retinopathy of Prematurity Severity in a Large Telemedicine Program in India Using Artificial Intelligence

Mallory A deCampos-Stairiker et al. Ophthalmology. 2023 Aug.

Abstract

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.

Keywords: Artificial intelligence; Epidemiology; Longitudinal analysis; Retinopathy of prematurity; Telemedicine.

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Figures

Figure 1:
Figure 1:. Proportion of eyes with type 2 or worse retinopathy of prematurity (A), or treatment-requiring ROP (B) in a South Indian telemedicine program in two time periods.
Among all districts, for babies of the same birthweight and gestational age, the proportion of eyes with type 2 or worse ROP or TR-ROP was significantly lower in the later period.
Figure 2:
Figure 2:. Changes in district-level retinopathy of prematurity severity measured using an artificial intelligence-derived vascular severity score (VSS).
In the later period, the VSS was significantly lower in the overall population (p < 0.001), as well as within Districts D (p < 0.001) and E (p < 0.001).
Figure 3:
Figure 3:. Scatterplot demonstrating birthweight and gestational age of babies diagnosed with treatment-requiring retinopathy of prematurity in the two time periods.
Adoption of the intermediate guidelines would result in 39% fewer examinations required.
See this image and copyright information in PMC

References

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