AI-Powered Diagnostic Surveillance for Public Health
SAND (Surveillance Analysis and Networked Diagnostics) uses Artificial Intelligence and Computer Vision to digitize Rapid Diagnostic Test (RDT) interpretation, improve result accuracy, and provide real-time disease surveillance across healthcare networks.
The Platform
What is SAND?
SAND is an AI-powered diagnostic ecosystem designed to support healthcare providers, and public health programs. Using a mobile application, healthcare workers can scan Rapid Diagnostic Tests (RDTs) for diseases such as:
Artificial Intelligence automatically analyzes the test image, detects control and test lines, determines the result with confidence scoring, and securely stores the information within a centralized surveillance platform.
- Hepatitis A
- Hepatitis B
- Hepatitis C
- HIV
- Malaria
Capabilities
Key Features
AI-Powered Result Interpretation
Computer vision algorithms analyze RDT kits and identify positive, negative, or invalid results.
Confidence Scoring
Every prediction includes an AI confidence score to assist quality assurance.
Real-Time Data Collection
Results are synchronized to a centralized cloud platform.
Disease Surveillance
Monitor disease trends, hotspots, and testing activities through interactive dashboards.
Mobile & Web Access
Field workers use the mobile application while administrators access analytics through the web portal.
Secure Data Management
Role-based access controls and centralized data governance.
The Challenge
Why SAND Matters
Traditional manual interpretation of diagnostic kits can lead to:
- Human error
- Delayed reporting
- Inconsistent record keeping
- Limited visibility into disease trends
The SAND Approach
SAND addresses these challenges by combining AI, mobile technology, and surveillance analytics into a single integrated platform.
Transform Diagnostic Surveillance
Discover how AI can improve disease detection and public health monitoring.