Retinopathy of prematurity (ROP) remains one of the leading preventable causes of childhood blindness, especially in regions where access to specialized ophthalmologists is limited. Early detection of the Plus sign (retinal vascular dilation and tortuosity) is critical for initiating treatment, but its manual assessment is subjective and prone to variability. In this context, automated systems based on artificial intelligence offer a real opportunity to extend diagnostic coverage, although their validation in African settings is still scarce.
Recent research shows that combining two approaches —image classification and vessel segmentation— can enhance accuracy in ROP detection. While RGB classifiers are highly sensitive and can identify suspicious cases, they tend to generate false positives. On the other hand, segmentation models, by precisely delineating retinal vessels, offer superior specificity. By integrating both methods through probabilistic ensembles or sequential strategies (such as OR screening and AND confirmation), a balance is achieved that reduces over-referral without losing sensitivity. This complementary approach is key to building robust tools that work across diverse populations.
In the field of healthcare software development, implementing these solutions requires a solid technical architecture. At Q2BSTUDIO, our experience in artificial intelligence for businesses allows us to design medical image processing pipelines that integrate classification and segmentation into scalable workflows. Additionally, our custom applications are optimized for clinical environments where latency, privacy, and interoperability are critical. We use AWS and Azure cloud services to ensure secure deployment and processing of large data volumes, while our AI agents facilitate the automation of repetitive tasks, such as image review.
Cybersecurity also plays a fundamental role: protecting patient data and ensuring model integrity is a priority. Likewise, business intelligence capabilities (with Power BI) allow healthcare centers to monitor performance metrics and make informed decisions. By combining these services, Q2BSTUDIO helps institutions deploy ROP detection systems that are not only accurate but also sustainable and aligned with healthcare regulations. Prospective validation at multiple sites and collaboration with ophthalmologists are the natural next steps to bring these technologies into real clinical practice.
