Background
Retinopathy of Prematurity (ROP) is a preventable cause of childhood blindness, increasingly burdensome in low- and middle-income countries where ROP-trained ophthalmologists are scarce. Plus disease, characterized by retinal vessel dilation and tortuosity, triggers treatment but is subjective and variable. Automated screening could extend specialist reach, yet African evidence remains limited.
Methods
We analyzed 121 Kenyan preterm infants, covering 237 eyes and 1,635 fundus images graded as No Plus, Pre-Plus, or Plus. Vessel annotations from two graders supported segmentation training. Eleven configurations were evaluated for eye-level Plus detection using patient-grouped nested cross-validation, including image classifiers, multiple-instance learning, multi-task segmentation-classification, and segment-then-classify pipelines.
Results
Vessel segmentation was feasible, achieving pooled Dice 0.533, IoU 0.368, sensitivity 0.623, and specificity 0.979 on held-out images. RGB classifiers were highly sensitive but over-referred, while segmentation-coupled models were more specific. Combining approaches improved performance: an OR-based screen achieved the highest sensitivity, an AND-based confirmation achieved the highest specificity, and a probability ensemble gave the best balanced performance, with sensitivity 0.692, specificity 0.914, and balanced accuracy 0.803, outperforming the vision classifier alone.
Conclusions
Classification and vessel segmentation are complementary for ROP Plus detection in Kenyan data. Classifiers support sensitive case-finding, while segmentation improves specificity and reduces over-referral. African ROP AI systems should use combined workflows and undergo prospective multi-site validation.
Blogger's Review: This study illustrates the synergy between image classification and vessel segmentation, highlighting the potential of AI technology to enhance screening for retinopathy of prematurity in resource-limited settings. By integrating multiple approaches, not only was sensitivity improved, but over-referral was effectively reduced, providing valuable insights for future clinical applications.