Cost-effectiveness of community-based artificial intelligence screening for retinopathy of prematurity

Background Retinopathy of prematurity (ROP) is one of the leading causes of childhood blindness globally, but particularly in middle-income countries, where neonatal survival has improved faster than access to specialist ophthalmic care. Although telemedicine and artificial intelligence (AI)-assisted screening show promise for improving access to ROP screening, their economic value within decentralised health systems remains uncertain. To explore this issue, we evaluated the cost-effectiveness of community-based…

By the Truvace deskOctober 15, 20262 min read1 sourcePeer-reviewedTRV-2026-1386· cite & verify

Cost-effectiveness of community-based artificial intelligence screening for retinopathy of prematurity — "CRE-003 OphaGo - Retinal Imaging Adapter - Huced Despro ITS" by Djoko Kuswanto is marked with CC0 1.0. To view the terms, visit https://creativecommons.org/publicdomain/zero/1.0/.

In brief

Researchers built a decision tree model for a hypothetical cohort of 100,000 preterm infants eligible for retinopathy of prematurity screening in China. They compared community-based AI-assisted screening, community-based telemedicine screening, and bedside screening at specialist facilities, using separate urban and rural parameters and a societal cost perspective.

The model suggests AI-assisted screening could widen access to early eye care while lowering costs, which matters where neonatal survival has outpaced specialist ophthalmic capacity. These are modelled estimates for a hypothetical cohort rather than observed outcomes from a deployed programme, so real-world results may differ.

Main points

Telemedicine screening dominated bedside screening, meaning it was less costly and more effective in the base case.
AI-assisted screening further dominated telemedicine, with lifetime costs of USD 1,507 per infant in urban and USD 2,581 in rural areas.
Modelled health gains were 29.7138 QALYs in urban and 29.5835 QALYs in rural settings under AI-assisted screening.
Incremental cost-effectiveness ratios against bedside screening were USD -7,307 per QALY gained in urban and USD -3,688 in rural settings.
Results were robust across scenario and sensitivity analyses.

The gain

In a decision-tree model of 100,000 preterm infants, community-based AI-assisted ROP screening had lower lifetime societal costs and more QALYs than telemedicine or bedside screening in both urban and rural China.

The rundown

The background notes that retinopathy of prematurity is a leading cause of childhood blindness globally, especially in middle-income countries where neonatal survival has improved faster than access to specialist ophthalmic care.

Although telemedicine and AI-assisted screening were seen as promising for access, their economic value within decentralised health systems was uncertain, which motivated this evaluation.

The authors report that AI-assisted screening stopped being cost-effective only at extremely low treatment-requiring ROP incidence, and conclude it is likely the most cost-effective strategy in China, potentially a scalable model for similar resource-constrained regions.