What We’re Building

We’re developing a vision–language platform that integrates:

Deep learning segmentation

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AI path-planning algorithms

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Physician-in-the-loop interfaces

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EMR-compatible data pipelines

For the Technically Curious

If you’re interested in the technology behind RetinaAIM:

Segmentation Engine:

A deep-learning model trained on expertly annotated retinal images automatically identifies treatment-eligible and exclusion zones, applying dynamic safety margins around sensitive anatomical structures.

Optimization Engine:

A geometric planning system computes spatial boundaries and distributes treatment points evenly across permissible regions while preventing overlap or encroachment into protected zones.

Interface Layer:

A physician-in-the-loop environment enables real-time visualization, adjustment, and trajectory editing through graphical and language-based input, with optional gaze and motion-tracking for intraoperative control.

Feedback Loop:

An adaptive learning framework continuously improves system performance by incorporating physician modifications and anonymized outcome metrics, refining both segmentation precision and treatment-path efficiency over time.

This hybrid system—AI precision guided by clinician oversight—represents the next generation of intelligent laser planning.

We are constantly iterating—integrating physician feedback, benchmarking against real surgical data, and preparing for formal clinical validation.