aceCost-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…
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 findings come from a decision tree model of a hypothetical cohort, so they are projected estimates rather than outcomes measured in a real screening programme.
Evidence
- Peer-reviewedJournal of Global Health2026-10-02
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Truvace Impact Record TRV-2026-1386, v1: “Cost-effectiveness of community-based artificial intelligence screening for retinopathy of prematurity.” Truvace, 2026-10-15. /record/TRV-2026-1386 (accessed at citation time). sha256 f68dca4e9a67b8d8…
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