Horse Health AI is a Golden Dragon AI DeepTech project for portable, non-invasive, multimodal animal health screening and longitudinal health intelligence.

I designed the system around a compact multimodal sensor architecture combining RGB Computer Vision, multi-wavelength NIR, radiometric thermal imaging, structured/polarized optical sensing, and motion and geometry data.

Rather than analyzing each signal independently, the AI architecture is designed to fuse complementary modalities and evaluate visible anatomy, gait and behavior, thermal patterns, vascular and perfusion-related features, tissue-related optical signals, movement, and individual history.

A key part of the architecture is the Individual Digital Baseline. Each animal can become its own longitudinal reference, allowing new scans to be compared not only with validated population-level knowledge but also with that animal's historical normal patterns.

The software architecture includes synchronized acquisition, calibration, multimodal registration, per-modality AI models, longitudinal baseline modeling, multimodal embeddings and fusion, veterinary knowledge/RAG, explainable anomaly localization, confidence and uncertainty assessment, and mobile and research interfaces.

The project is designed as a screening and decision-support system, not as a replacement for veterinary diagnosis. Veterinary expertise, clinically confirmed data and validation are explicit parts of the development roadmap.

The initial platform is focused on horses, with potential expansion to other animal species following species-specific validation.