Aging, a key risk factor for chronic diseases, varies among individuals due to genetic, lifestyle, and environmental influences. Existing methods to assess aging often focus on single biomarkers, providing limited insights. This invention addresses the need for an accurate, holistic approach to estimate biological age (BA), which reflects an individual’s true physiological state and helps detect chronic diseases early.
Core Features
The innovation uses a multi-modal AI model incorporating:
- Facial Images: Reflect external aging due to environmental exposure.
- Retinal Fundus Images: Offer insights into vascular and brain health.
- Tongue Images: Indicate oral and gastrointestinal health. These images are processed using a Transformer-based architecture with cross-attention mechanisms to integrate data from all modalities. The model predicts BA by evaluating structural and functional changes in the body.
Benefits
- High Accuracy: Estimates BA within two years of an individual's actual age for healthy participants.
- Disease Detection: Identifies deviations between chronological and biological age (Age-Diff) to predict risks of chronic diseases such as diabetes, cardiovascular disease, and stroke.
- Non-Invasive: Uses easily accessible images, eliminating the need for costly or invasive procedures.
- Holistic Health Insight: Combines multiple indicators for a comprehensive understanding of health.
Impact
This innovation promises to transform healthcare by:
- Enabling large-scale, cost-effective screening for chronic disease risks.
- Providing actionable insights for personalized interventions.
- Encouraging proactive health management and reducing healthcare costs. The approach may significantly improve societal health outcomes, especially in aging populations, through early disease detection and prevention.
This AI-powered tool is a groundbreaking step toward precision medicine and a healthier society.