Maximize healthy living years.
We’re building compact, room‑temperature magnetocardiography (MCG) using NV‑diamond quantum sensors and advanced AI denoising to deliver contact‑free, clinically meaningful cardiac insights.
Why it matters
- Contact‑free monitoring without gels, wires, or shielded rooms.
- AI reveals PQRST complexes and arrhythmia signatures from weak magnetic signals.
- Solid‑state path to portability and bedside use.
Mission
Extend healthspan by making advanced cardiac sensing simple, accessible, and comfortable. We merge quantum‑grade magnetic sensing with robust, privacy‑preserving AI to deliver earlier, clearer insights at the point of care.
Design principles
- Comfort first: contact‑free, minimal setup
- Signal fidelity through physics‑informed ML
- Deployable in real‑world environments
Technology
Our stack couples NV‑center magnetometry with modulation and lock‑in detection, then applies deep denoising and anomaly detection to extract clinically relevant waveforms from ambient noise.
NV‑Diamond Sensors
Optically addressable NV centers detect sub‑nT magnetic fields at room temperature.
AI Denoising
Deep models separate signal from environmental noise to reveal PQRST morphology.
Unshielded
Modulation strategies + lock‑in techniques improve SNR in typical clinical spaces.
Solutions
Bedside Monitoring
Comfortable, repeatable readings for wards and step‑down units without electrode placement.
Triage & Rapid Assessment
Fast, contact‑free screening to prioritize cases and reduce throughput friction.
Research & Discovery
New biomarkers from magnetic signatures enable fresh insights into cardiac physiology.
Company Partners
Healthspan AI, Inc. is led by experienced operators in applied physics, RF systems, and AI‑driven medical devices.
Contact
Interested in partnering or piloting? Join the waitlist and we’ll reach out.
Clinical & research collaborations
We’re inviting partners across cardiology, emergency care, and biomedical research.
- Signal characterization studies
- Workflow and UX validation
- Model generalization and bias audits