AfyaWave offers an AI‑powered ultrasonography platform that enables frontline clinicians to perform maternal imaging anywhere they are needed. The tablet‑based system guides users through scans, improving diagnostic accuracy and expanding access to quality prenatal care for millions of women. Currently the platform supports a network reaching over 4.4 million people, integrating directly into existing maternal health workflows.
Funding
Funding not disclosed
Founders
Product
Problem
Frontline clinicians in low‑resource settings often lack access to specialized ultrasound expertise, leading to missed or delayed detection of maternal health issues during prenatal care.
Solution
AfyaWave provides a tablet‑based ultrasonography platform that embeds artificial intelligence directly into the imaging workflow. The AI guides clinicians step‑by‑step through each scan, offering real‑time feedback on probe positioning and image quality to ensure diagnostically useful views. By automating key aspects of image acquisition and interpretation, the system enables more accurate and timely prenatal assessments without requiring a sonographer. The platform is designed for use in community health settings, expanding the reach of quality maternal imaging to an estimated 4.4 million people. Data from scans are stored securely and can be reviewed remotely by specialists, supporting continuous care and outcome monitoring.
Target Audience
Primary users are frontline maternal health providers such as community health workers, midwives, and primary‑care clinicians operating in low‑resource or remote settings.
Features
- Tablet interface with integrated AI that provides live guidance on probe placement and image optimization
- Automated quality checks that flag suboptimal frames and suggest corrective actions during the scan
- Pre‑trained models for common obstetric measurements (e.g., fetal biometry, placenta location) to assist diagnosis
- Secure cloud storage and remote specialist review workflow for tele‑consultation and second opinions
- Low‑power hardware and offline‑first design suitable for environments with limited connectivity
- Intuitive user experience requiring minimal training, enabling use by community health workers