Leveraging transfer learning for efficient bioprinting.

IF 8.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL
F Bracco, G Zanderigo, K Paynabar, B M Colosimo
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引用次数: 0

Abstract

Bioprinting is a promising family of processes combining 3D printing with life sciences, offering the potential to significantly advance various applications. Despite numerous research efforts aimed at enhancing process modeling, optimizing capabilities, and exploring new conditions, there remains a critical need to enhance process efficiency. Experimental data are paramount for improving models. Nevertheless, it is practically unfeasible to explore a multitude of conditions (e.g. different material formulations, process parameters, machines, setups), especially given the experimental constraints of budget and time. Leveraged by in-situ bioprinting monitoring, this paper explores a set of transfer learning (TL) methods designed for resource-efficient bioprinting modeling, aiming to merge established knowledge with new experimental conditions. TL encompasses machine learning strategies focused on transferring knowledge across distinct, yet similar, domains. TL is applied to an extrusion-based bioprinting case study for printability response modeling. The knowledge acquired from a model trained on one material (the source) is transferred to a new material (the target), under conditions of limited experimental data availability. Eventually, the accuracy of the transferred model is assessed and compared against a reference no-transfer scenario, which is developed from scratch following conventional practices. Furthermore, giving high importance to the experimental effort reduction, a sensitivity analysis altering the number of experimental training points is performed to assess performances and limitations of the method. This method demonstrates the feasibility of knowledge transfer in bioprinting as a catalyst for more sophisticated applications across diverse printing conditions, materials, and technologies to advancing this technology towards achieving its full potential.

利用迁移学习实现高效生物打印。
生物打印是将3D打印与生命科学相结合的一个有前途的工艺系列,提供了显著推进各种应用的潜力。尽管大量的研究工作旨在增强过程建模、优化能力和探索新的条件,但仍然迫切需要提高过程效率。实验数据对改进模型至关重要。然而,探索多种条件(例如不同的材料配方,工艺参数,机器,设置)实际上是不可行的,特别是考虑到预算和时间的实验限制。本文利用生物打印现场监测,探索了一套针对资源高效生物打印建模的迁移学习(TL)方法,旨在将已有知识与新的实验条件相融合。TL包含了专注于跨不同但相似的领域转移知识的机器学习策略。TL应用于基于挤出的生物打印案例研究,用于打印响应建模。在实验数据有限的情况下,从一个材料(源)上训练的模型获得的知识被转移到一个新的材料(目标)上。最后,对迁移模型的准确性进行评估,并与参考的无迁移场景进行比较,该场景是根据传统实践从零开始开发的。此外,考虑到减少实验工作量的重要性,进行了改变实验训练点数量的敏感性分析,以评估该方法的性能和局限性。这种方法证明了生物打印中知识转移的可行性,作为催化剂,可以在不同的打印条件、材料和技术上实现更复杂的应用,从而推动这项技术实现其全部潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biofabrication
Biofabrication ENGINEERING, BIOMEDICAL-MATERIALS SCIENCE, BIOMATERIALS
CiteScore
17.40
自引率
3.30%
发文量
118
审稿时长
2 months
期刊介绍: Biofabrication is dedicated to advancing cutting-edge research on the utilization of cells, proteins, biological materials, and biomaterials as fundamental components for the construction of biological systems and/or therapeutic products. Additionally, it proudly serves as the official journal of the International Society for Biofabrication (ISBF).
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