Uncertainty analysis and optimization for mild moxibustion.

Area of Science:

• Integrative and Complementary Medicine
• Biomedical Engineering
• Thermal Medicine

Background:

• Mild moxibustion treatments face uncertainties in operating parameters like temperature and distance.
• These uncertainties cause skin temperature fluctuations, leading to pain or reduced efficacy.
• Current methods often overlook these parameter uncertainties.

Purpose of the Study:

• To develop an uncertainty optimization design for mild moxibustion operating parameters.
• To maximize thermal penetration for enhanced efficacy while maintaining safe skin surface temperatures.
• To address the limitations of existing treatments by considering parameter variability.

Main Methods:

• Employed an interval model to quantify uncertainties in operating parameters.
• Developed a high-precision surrogate model using a radial basis neural network (RBNN).
• Formulated a nonlinear interval model and transformed uncertainty optimization into a deterministic problem using reliability-based possibility degree of interval (RPDI), solved via genetic algorithm.

Main Results:

• The proposed method significantly improved thermal penetration during mild moxibustion.
• Skin surface temperature requirements were successfully met, ensuring patient comfort.
• Optimized parameters led to enhanced therapeutic efficacy.

Conclusions:

• The uncertainty optimization design effectively enhances mild moxibustion efficacy.
• This approach provides a robust method for managing parameter uncertainties in thermal therapies.
• It offers a pathway to more predictable and effective mild moxibustion treatments.