Wound Severity Detection - Improving Outcomes Through AI

Problem/Solution

• 30M traumatic wounds annually causing pain, disability, death
• Lack of wound severity assessment leads to treatment delays
  
• Our app uses AI to instantly classify wounds and guide next steps

Stage 1

• Patient takes wound photo
• AI analyzes wound characteristics
• Classifies severity and suggests first aid or seek care ASAP

Stage 2

• Tracks healing progress through periodic wound images
• Alerts for complications and enables remote monitoring
• Improves outcomes through early intervention

Benefits

• Reduces patient panic and provides instant guidance
• Lowers burden on healthcare system
• Cuts costs through remote care and monitoring

Visuals

• App demo
• Wound healing timeline
• Stats on wound prevalence and complications

How we built it

• Used PyTorch to build and train wound image classification models
• Integrated computer vision and AI models into a user-friendly mobile app

Challenges we ran into

• Collecting and curating a large wound image dataset
• Achieving high accuracy for fine-grained wound severity classifications
• Optimizing models for real-time performance on mobile devices
• Building clinical trust in AI-powered recommendations

Accomplishments we're proud of

• Effective wound severity classifier
• Ultra-fast image analysis
• Intuitive web interface requiring minimal user input

What we learned

• Importance of diverse, clinical-grade training data
• Strategies to compress AI models without losing accuracy
  
• Value of clinician feedback in designing transparent AI systems
• How to build and validate medical AI responsibly

What's next for Wound Severity Detection

• Expand wound classification for different injury types
  
• Enhance personalization based on patient medical history
• Scale deployment across healthcare systems
• Enable proactive wound care and preventative interventions

Built With

• github
• python
• pytorch
• streamlit
• torch