Purpose This study explores the integration of thermal modelling in biological systems, focusing on its impact on tissue engineering and regenerative medicine (TERM). It aims to analyze how thermal effects influence biological processes and investigate the role of machine learning (ML) and artificial intelligence (AI) in enhancing thermal modelling applications. Methods A comprehensive review of thermal modelling techniques was conducted, including finite element analysis (FEA) and multiscale modelling. The study also examined AI and ML applications in processing experimental data related to cryopreservation, hyperthermia treatments, and biomaterial design. The importance of standardized thermal measurement protocols and advanced sensing techniques, such as infrared thermography and fiber optic sensors, was also discussed. Results Findings indicate that AI and ML significantly improve the predictive accuracy of thermal models, optimizing thermal parameters for TERM applications. However, challenges persist in experimental validation due to biological variability and the need for large, high-quality datasets for AI training. The study highlights the potential of integrating real-time monitoring systems with computational models to enhance thermal process precision. Conclusion Integrating AI with thermal modelling offers promising advancements in TERM by improving precision, adaptability, and therapeutic efficacy. Future research should focus on overcoming experimental validation challenges, refining predictive models, and developing scalable AI-driven thermal optimization techniques. These efforts will enhance the stability and functionality of engineered biological constructs, ensuring reliable TERM applications. Lay Summary. This research explores how thermal modelling simulating heat transfer can improve TERM. Temperature plays a crucial role in cell survival, biomaterial stability, and therapies such as cryopreservation (freezing cells for storage) and hyperthermia (using heat to target diseased cells). The study highlights the role of AI and ML in optimizing thermal conditions for TERM applications. AI can analyze complex data, predict temperature distributions, and enhance treatment precision. Advanced measurement tools like infrared thermography and fiber optic sensors further improve accuracy. Challenges remain, such as the need for high-quality experimental data to validate AI models and the complexity of biological systems. Future research should focus on refining predictive models, integrating real-time temperature monitoring, and ensuring sustainable, scalable thermal processes. By combining AI, advanced thermal modelling, and precise temperature control, TERM can become more efficient and effective, improving tissue engineering outcomes and medical treatments.

Thermal Modeling in Regenerative Medicine: Applications and Challenges in Tissue Engineering

期刊论文
发表日期：

2025年9月23日

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Ravikumar Jayabal

摘要

Purpose

This study explores the integration of thermal modelling in biological systems, focusing on its impact on tissue engineering and regenerative medicine (TERM). It aims to analyze how thermal effects influence biological processes and investigate the role of machine learning (ML) and artificial intelligence (AI) in enhancing thermal modelling applications.

Methods

A comprehensive review of thermal modelling techniques was conducted, including finite element analysis (FEA) and multiscale modelling. The study also examined AI and ML applications in processing experimental data related to cryopreservation, hyperthermia treatments, and biomaterial design. The importance of standardized thermal measurement protocols and advanced sensing techniques, such as infrared thermography and fiber optic sensors, was also discussed.

Results

Findings indicate that AI and ML significantly improve the predictive accuracy of thermal models, optimizing thermal parameters for TERM applications. However, challenges persist in experimental validation due to biological variability and the need for large, high-quality datasets for AI training. The study highlights the potential of integrating real-time monitoring systems with computational models to enhance thermal process precision.

Conclusion

Integrating AI with thermal modelling offers promising advancements in TERM by improving precision, adaptability, and therapeutic efficacy. Future research should focus on overcoming experimental validation challenges, refining predictive models, and developing scalable AI-driven thermal optimization techniques. These efforts will enhance the stability and functionality of engineered biological constructs, ensuring reliable TERM applications.

Lay Summary.

This research explores how thermal modelling simulating heat transfer can improve TERM. Temperature plays a crucial role in cell survival, biomaterial stability, and therapies such as cryopreservation (freezing cells for storage) and hyperthermia (using heat to target diseased cells). The study highlights the role of AI and ML in optimizing thermal conditions for TERM applications. AI can analyze complex data, predict temperature distributions, and enhance treatment precision. Advanced measurement tools like infrared thermography and fiber optic sensors further improve accuracy. Challenges remain, such as the need for high-quality experimental data to validate AI models and the complexity of biological systems. Future research should focus on refining predictive models, integrating real-time temperature monitoring, and ensuring sustainable, scalable thermal processes. By combining AI, advanced thermal modelling, and precise temperature control, TERM can become more efficient and effective, improving tissue engineering outcomes and medical treatments.