Complementary Roles of Image Classification and Vessel Segmentation in AI-Based Screening for Retinopathy of Prematurity Plus Disease in a Kenyan Preterm Cohort
arXiv:2607.05825v1 Announce Type: cross Abstract: Background. Retinopathy of prematurity (ROP) is a preventable cause of childhood blindness, with rising burden in low- and middle-income countries where ROP-trained ophthalmologists are scarce. Plus disease, marked by retinal vessel dilation and tortuosity, triggers treatment but is subjective and variable. Automated screening could extend specialist reach, but African evidence remains limited. Methods. We analysed 121 Kenyan preterm infants, covering 237 eyes and 1,635 fundus images graded as No Plus, Pre-Plus or Plus. Vessel annotations from
This research is emerging now due to advances in AI vision capabilities and a critical need for scalable healthcare solutions in underserved regions, particularly given the rising burden of preventable blindness.
AI-based screening for Retinopathy of Prematurity (ROP) addresses a significant healthcare access gap in low-income countries, potentially preventing childhood blindness and expanding specialist reach without relying on scarce human resources.
The development of AI for ROP screening introduces a paradigm shift in healthcare delivery, moving towards automated diagnostics that can augment or substitute for human specialists in specific contexts, particularly in remote regions.
- · AI developers
- · Healthcare providers in low-income countries
- · Preterm infants at risk of ROP
- · Medical technology companies
- · Traditional diagnostic methods
- · Regions lacking digital infrastructure for AI deployment
- · Ophthalmologists focused solely on manual screening
This AI model could enable earlier and more widespread detection of Plus disease in ROP, leading to improved patient outcomes.
Successful deployment could trigger similar AI-driven diagnostic solutions for other ophthalmic or medical conditions in resource-limited settings.
It might influence policy changes in global health, prioritizing AI integration for screening and diagnosis to address specialist shortages and health inequities.
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Read at arXiv cs.AI