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Diabetic retinopathy remains one of the leading causes of preventable vision loss worldwide. Early detection is crucial, but traditional screening methods have limitations in accuracy and accessibility. In this context, combining structural images obtained through optical coherence tomography (OCT) with vascular images from OCT angiography (OCTA) opens a promising avenue for computer-assisted diagnosis. The fusion of both modalities allows simultaneous capture of retinal architecture and microvascular alterations, two key aspects in the progression of the disease.
The most advanced approaches employ bidirectional attention networks that learn to correlate information from OCT B-scans with OCTA enface projections, significantly improving classification performance compared to unimodal models. A particularly relevant aspect is that OCTA generated through image translation from the OCT itself eliminates the need for specific equipment, reducing costs and facilitating implementation in resource-limited settings. This not only improves sensitivity and generalization across different datasets, but also demonstrates that comparable clinical performance can be achieved without additional hardware.
Behind these capabilities lies a technological ecosystem that goes beyond the algorithm. For such a system to function in a real hospital environment, a custom software platform is required to integrate image preprocessing, artificial intelligence model execution, and result visualization. At Q2BSTUDIO we develop custom applications that connect these modules with existing healthcare information systems, ensuring secure and efficient workflows. Additionally, the AWS and Azure cloud services infrastructure enables scaling of large data volumes processing without compromising latency, a critical factor in diagnoses requiring rapid responses.
Artificial intelligence for businesses applied to this domain is not limited to binary classification. Models can be trained to identify different severity levels, segment lesions, or predict progression. At Q2BSTUDIO we implement AI for business solutions that range from architecture selection to production deployment, using MLOps techniques to maintain traceability and reproducibility. We also explore the use of AI agents capable of interacting with radiologists, suggesting regions of interest or alerting about anomalous findings, enriching collaborative diagnosis.
The management of this sensitive data introduces cybersecurity requirements that cannot be overlooked. Protection of health information, compliance with regulations such as HIPAA or GDPR, and conducting penetration tests (pentesting) are integral parts of any solution handling patient images. In our projects we apply access controls, encryption, and continuous auditing to mitigate risks.
In parallel, business intelligence services with Power BI allow clinical managers to visualize screening system performance indicators: detection rates, waiting times, distribution by severity levels, etc. Integrating these dashboards with the diagnostic platform facilitates informed decision-making and resource optimization.
Ultimately, OCT-OCTA fusion represents a substantial advance in the fight against diabetic retinopathy, but its true potential is achieved when combined with a robust, scalable, and secure software ecosystem. From custom application development to artificial intelligence model implementation and cloud management, at Q2BSTUDIO we accompany healthcare organizations in the digital transformation of their diagnostic processes, helping cutting-edge technology reach more patients efficiently and reliably.
