N23 Health provides a point‑of‑care breast cancer screening system that combines a low‑cost, smartphone‑connected ultrasound probe with an on‑device AI model that classifies lesions in under 100 ms. The solution enables non‑radiologist health workers in low‑resource settings to perform accurate, offline screenings with diagnostic performance comparable to mammography at a fraction of the cost.
Funding
Funding not disclosed
Founders
Product
Problem
Breast cancer remains the leading cancer among women, with disproportionately high mortality in low‑ and middle‑income countries due to limited access to affordable, high‑quality screening. Conventional mammography requires expensive, stationary equipment and radiology expertise, creating a bottleneck for early detection in resource‑constrained settings.
Solution
N23 Health delivers a point‑of‑care breast cancer screening platform that combines a smartphone‑connected ultrasound probe with a purpose‑built mobile application. The app presents a streamlined workflow that can be mastered by non‑radiologist health workers after a brief, focused training session. On‑device deep‑learning models analyze each scan in under 100 ms, delivering diagnostic scores that have demonstrated higher area‑under‑curve performance than standard mammography. Because the AI runs locally, the system operates without network connectivity, preserving data privacy and enabling deployment in remote clinics. The hardware is priced to be roughly 25 × cheaper than conventional mammography units, supporting large‑scale screening programs in underserved communities.
Target Audience
The primary customers are public‑health agencies, NGOs, and low‑resource clinics that deploy community health workers for breast cancer triage in LMICs.
Features
- Smartphone‑compatible, low‑cost ultrasound probe with integrated beam‑forming and real‑time image streaming
- Edge AI inference engine optimized for ARM processors, delivering sub‑100 ms classification of malignant vs. benign lesions
- Minimalist UI that guides operators through acquisition steps, reducing training time by 10‑100× compared with traditional ultrasound workflows
- Open APIs and SDKs that allow third‑party developers to build custom analytics, reporting, or integration layers
- Offline‑first architecture with encrypted local storage and optional secure upload to health‑system back‑ends via FHIR‑compatible endpoints
- Proven diagnostic performance (AUC ≈ 0.95) that exceeds conventional mammography benchmarks in peer‑reviewed studies
- Modular hardware kit (probe, mounting accessories, protective case) designed for rugged field conditions and easy sterilization