Hybrid AI models for predicting heat distribution in complex tissue structures with bioheat transfer simulation

Area of Science:

• Biomedical Engineering
• Computational Biology
• Medical Physics

Background:

• Accurate thermal behavior prediction in biological tissues is vital for medical treatments like hyperthermia and thermal ablation.
• Existing models often lack the precision and speed required for real-time applications in tissue engineering and thermal therapies.
• Understanding thermal effects is crucial for optimizing treatment outcomes and patient safety.

Purpose of the Study:

• To develop and validate a novel deep learning-enhanced bioheat transfer model for precise thermal effect prediction in engineered tissue constructs.
• To integrate a Fractional Legendre wavelet approach for enhanced predictive accuracy and computational efficiency.
• To assess the model's performance across various tissue types and thermal load conditions.

Main Methods:

• A multi-phase bioheat transfer model incorporating blood perfusion, thermal conductivity, and metabolic heat generation was developed.
• A deep learning framework was integrated with a Fractional Legendre wavelet approach for enhanced predictive capabilities.
• Experimental validation was performed on a 5 cm³ tissue construct with temperature monitoring under a controlled heat source.

Main Results:

• The model accurately predicted temperature gradients, ranging from 37°C to 48°C in experimental validation.
• Achieved a mean absolute error of 2.5°C, with prediction errors below 0.4°C across different tissue types and power inputs (10W-30W).
• Demonstrated a 15% increase in prediction speed compared to conventional methods, enabling real-time capabilities.

Conclusions:

• The deep learning-enhanced bioheat transfer model offers a significant advancement in predicting thermal behavior in biological tissues.
• The model's accuracy, speed, and versatility make it highly suitable for real-time thermal therapy planning, tumor ablation, and tissue engineering.
• This approach holds promise for improving the precision and efficacy of various medical treatments involving thermal manipulation.