RenKai Fu , Zhenghong Chen , Hua Tian , Jiajie Hu , Fangxin Bu , Peng Zheng , Liang Chi , Lulu Xue , Qing Jiang , Lan Li , Liya Zhu
{"title":"A review on the applications of machine learning in biomaterials, biomechanics, and biomanufacturing for tissue engineering","authors":"RenKai Fu , Zhenghong Chen , Hua Tian , Jiajie Hu , Fangxin Bu , Peng Zheng , Liang Chi , Lulu Xue , Qing Jiang , Lan Li , Liya Zhu","doi":"10.1016/j.smaim.2025.06.003","DOIUrl":null,"url":null,"abstract":"<div><div>In recent years, machine learning, a powerful data analysis and modeling technique, is continuously revolutionizing the field of tissue engineering. Its ability to learn and extract information from complex datasets opens up new opportunities for the development of tissue engineering. In this paper, we first provide a categorized overview of different types of machine learning algorithms, and then focus on the recent advances in the application of machine learning in tissue engineering. We summarize the technology's latest applications in biomaterials, biomechanics, and biomanufacturing, discuss the challenges faced, and explore future prospects aiming at providing scientific references for researchers to achieve further progress in the fields of tissue engineering and machine learning.</div></div>","PeriodicalId":22019,"journal":{"name":"Smart Materials in Medicine","volume":"6 2","pages":"Pages 171-204"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Materials in Medicine","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590183425000183","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, machine learning, a powerful data analysis and modeling technique, is continuously revolutionizing the field of tissue engineering. Its ability to learn and extract information from complex datasets opens up new opportunities for the development of tissue engineering. In this paper, we first provide a categorized overview of different types of machine learning algorithms, and then focus on the recent advances in the application of machine learning in tissue engineering. We summarize the technology's latest applications in biomaterials, biomechanics, and biomanufacturing, discuss the challenges faced, and explore future prospects aiming at providing scientific references for researchers to achieve further progress in the fields of tissue engineering and machine learning.