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.

中文翻译：

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将机器学习的力量用于组织工程：当前进展和未来展望


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丢失和受损的组织器官。近年来，组织工程发展迅速，支架、生长因子和干细胞已成功用于各种组织器官的重建。然而，耗时的生产、高成本和不可预测的组织生长仍然需要解决。机器学习是一门新兴的跨学科学科，它结合了计算机科学和强大的数据集，在加速科学发现和增强临床实践方面具有巨大潜力。机器学习和组织工程的融合虽然处于起步阶段，但有望带来变革性的进步。本文将回顾机器学习在组织工程中应用的最新进展，总结在生物材料设计、支架制备、组织再生和器官移植方面的最新应用，并讨论跨学科合作的挑战和未来前景，以期为研究人员在组织工程和机器学习方面取得更大进展提供科学参考。