Deolhonope provides an AI-powered mobile application for diabetic foot self-examination. The platform uses a smartphone's camera and vibration to guide patients through regular assessments, analyzing data to detect potential complications and alert healthcare providers for early intervention.
Funding
Funding not disclosed
Founders
Product
Problem
Diabetic patients are at high risk of developing foot complications, which can lead to severe outcomes if not detected early. Traditional methods for monitoring foot health are often infrequent and require clinical visits, limiting proactive self-care and timely intervention.
Solution
Deolhonope offers an AI-powered mobile application designed for diabetic foot self-examination. The platform utilizes a smartphone's camera and vibration capabilities to guide patients through regular foot assessments. Proprietary algorithms analyze visual and haptic data to identify potential issues, promoting patient self-care and enabling early detection of complications. The system also facilitates communication with healthcare providers by alerting relevant units to concerning findings, thereby supporting proactive management of diabetic foot health.
Target Audience
The primary users are individuals diagnosed with diabetes who need to monitor their foot health, and healthcare providers or institutions managing diabetic patient care.
Features
- AI-driven analysis of smartphone camera imagery for visual anomaly detection in diabetic feet.
- Integration of smartphone haptic feedback to guide tactile examination and patient awareness.
- Automated alerts to healthcare providers for identified concerning results, facilitating timely intervention.
- User-friendly mobile interface designed for patient self-administration of foot examinations.
- Secure data transmission and storage protocols to ensure patient privacy and data integrity.
- Machine learning models continuously refined to improve diagnostic accuracy and detection capabilities.