Wu Yiyang, Ding Xiaotong, Wang Yiwei, Ouyang Defang
State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Avenida da Universidade, Taipa, Macau SAR, 999078, China.
Jiangsu Provincial Engineering Research Center of TCM External Medication Development and Application, Nanjing University of Chinese Medicine, 138 Xianlin Avenue, Nanjing, Jiangsu, 210023, PR China.
Burns Trauma. 2024 Dec 6;12:tkae053. doi: 10.1093/burnst/tkae053. eCollection 2024.
Tissue engineering is a discipline based on cell biology and materials science with the primary goal of rebuilding and regenerating lost and damaged tissues and organs. Tissue engineering has developed rapidly in recent years, while scaffolds, growth factors, and stem cells have been successfully used for the reconstruction of various tissues and organs. However, time-consuming production, high cost, and unpredictable tissue growth still need to be addressed. Machine learning is an emerging interdisciplinary discipline that combines computer science and powerful data sets, with great potential to accelerate scientific discovery and enhance clinical practice. The convergence of machine learning and tissue engineering, while in its infancy, promises transformative progress. This paper will review the latest progress in the application of machine learning to tissue engineering, summarize the latest applications in biomaterials design, scaffold fabrication, tissue regeneration, and organ transplantation, and discuss the challenges and future prospects of interdisciplinary collaboration, with a view to providing scientific references for researchers to make greater progress in tissue engineering and machine learning.
组织工程是一门基于细胞生物学和材料科学的学科，其主要目标是重建和再生受损及缺失的组织和器官。近年来，组织工程发展迅速，支架、生长因子和干细胞已成功应用于各种组织和器官的重建。然而，生产耗时、成本高昂以及组织生长不可预测等问题仍有待解决。机器学习是一门新兴的跨学科领域，它将计算机科学与强大的数据集相结合，在加速科学发现和提升临床实践方面具有巨大潜力。机器学习与组织工程的融合虽尚处于起步阶段，但有望取得变革性进展。本文将综述机器学习在组织工程应用中的最新进展，总结其在生物材料设计、支架制造、组织再生和器官移植方面的最新应用，并探讨跨学科合作面临的挑战与未来前景，以期为研究人员在组织工程和机器学习领域取得更大进展提供科学参考。
