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Machine Learning in Predicting Printable Biomaterial Formulations for Direct Ink Writing

Research 2023年第4期2023卷 105-115页

作者： Hongyi Chen Yuanchang Liu Stavroula Balabani ryuji hirayama Jie Huang Department of Mechanical Engineering University College LondonLondonUK Department of Computer Science University College LondonLondonUK Wellcome-EPSRC Centre for Interventional Surgical Sciences(WEISS) University College LondonLondonUK.

Three-dimensional(3D)printing is emerging as a transformative technology for biomedical *** 3D printed product can be patient-specific by allowing customizability and direct control of the *** trial-and-error approach currently used for developing the composition of printable inks is time-and resource-consuming due to the increasing number of variables requiring expert *** intelligence has the potential to reshape the ink development process by forming a predictive model for printability from experimental *** this paper,we constructed machine learning(ML)algorithms including decision tree,random forest(RF),and deep learning(DL)to predict the printability of biomaterials.A total of 210 formulations including 16 different bioactive and smart materials and 4 solvents were 3D printed,and their printability was *** ML methods were able to learn and predict the printability of a variety of inks based on their biomaterial *** particular,the RF algorithm has achieved the highest accuracy(88.1%),precision(90.6%),and F1 score(87.0%),indicating the best overall performance out of the 3 algorithms,while DL has the highest recall(87.3%).Furthermore,the ML algorithms have predicted the printability window of biomaterials to guide the ink *** printability map generated with DL has finer granularity than other *** has proven to be an effective and novel strategy for developing biomaterial formulations with desired 3D printability for biomedical engineering applications.

关键词： printing formulation biomaterials

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