AI-Powered Wound Detection & Documentation System
Advanced computer vision system revolutionizing wound care management through automated detection, precise measurements, and AI-driven documentation.
Key Results
Overview
Regional Medical Center needed to modernize their wound care assessment process. Traditional manual measurements were inconsistent, time-consuming, and prone to human error. Our team developed a comprehensive AI solution that uses computer vision to automatically detect wounds, perform precise measurements, segment wound tissue types, and generate standardized clinical documentation.
The Challenge
Manual wound assessment was time-consuming, subjective, and lacked standardized documentation across nursing staff
Problem Statement
The nursing staff spent an average of 15-20 minutes per patient documenting wound assessments. Measurement consistency varied between practitioners, making it difficult to track healing progress accurately. The lack of standardization created challenges in care coordination and insurance reimbursement.
The Solution
Developed an AI-powered computer vision system for automated wound detection, measurement, segmentation, and standardized documentation
Our Approach
Collected and annotated 10,000+ wound images with clinical supervision
Trained custom YOLOv8 model for wound detection with 97% accuracy
Implemented U-Net architecture for multi-class tissue segmentation
Developed proprietary measurement algorithms using depth sensing
Built mobile app with offline capability for point-of-care use
Integrated with existing EHR system via FHIR standards
Ensured full HIPAA compliance and data encryption
Implementation Timeline
Phase 1: Research & Data Collection
3 monthsCollaborated with wound care specialists to collect diverse wound imagery and establish clinical requirements. Built annotated dataset with tissue types, wound boundaries, and measurement ground truth.
Phase 2: Model Development
4 monthsTrained and optimized deep learning models for detection, segmentation, and measurement. Achieved clinical-grade accuracy through iterative refinement with medical feedback.
Phase 3: Mobile Application
3 monthsDeveloped cross-platform mobile app with intuitive interface for nurses. Implemented offline mode, image capture optimization, and real-time inference.
Phase 4: EHR Integration & Deployment
2 monthsIntegrated with Epic EHR system, conducted clinical validation studies, trained nursing staff, and deployed across 5 hospital locations.
Technologies Used
This technology has transformed our wound care program. Our nurses love how fast and easy it is, and the consistent documentation has improved our quality metrics significantly.
Dr. Sarah Martinez
Chief Medical Officer, Regional Medical Center