Digital Twin in Healthcare: A Study for Chronic Wound Management

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Healthcare
• Digital Health

Background:

• Digital twin technology, though established, is emerging in healthcare applications.
• Data-driven models and digital twins enhance clinical decision support for chronic wound care.
• Current chronic wound management can benefit from advanced predictive and monitoring tools.

Purpose of the Study:

• To propose and detail the implementation of a digital twin for chronic wound management.
• To leverage artificial intelligence (AI) for tracking and predicting wound healing.
• To enhance clinical decision-making and patient outcomes in chronic wound care.

Main Methods:

• Development of a digital twin system comprising data collection, processing, and AI models.
• Utilization of a novel AI pipeline for monitoring chronic wound healing progression.
• Simulation of the wound healing process using a virtual representation (digital twin).

Main Results:

• The digital twin effectively simulates and replicates the chronic wound healing process.
• The AI-driven wound-healing prediction model provides guidance for treatment strategies.
• Early identification of non-healing wounds is facilitated by comparing actual wounds with their digital twins.

Conclusions:

• The proposed digital twin system enables personalized and tailored treatments for chronic wounds.
• This approach supports proactive identification of treatment plan deviations and facilitates timely interventions.
• Digital twin technology in healthcare holds significant potential for improving chronic wound management and patient outcomes.