通过机器学习加强生物打印的质量控制
Amedeo Franco Bonatti1, Giovanni Vozzi1, Carmelo De Maria1
1Department of Information Engineering and Research Center 'E. Piaggio', University of Pisa, Pisa, Italy.
Biofabrication
|January 23, 2024
概括
机器学习 (ML) 为生物打印提供自动化质量控制 (QC) 解决方案,解决技术和监管障碍. 这加速了生物打印组织的临床转化,用于再生医学.
科学领域:
- 生物打印是一种生物打印技术.
- 组织工程是组织工程.
- 再生医学是一种再生医学.
背景情况:
- 生物打印在组织工程方面得到了广泛的研究,但临床转化有限.
- 技术挑战包括复制复杂性,打印时间和生物材料选择.
- 监管障碍和缺乏明确的产品分类阻碍了临床使用.
研究的目的:
- 审查生物打印质量控制 (QC) 中的机器学习 (ML) 应用.
- 从技术角度分析生物打印中QC的ML模型.
- 提供关于生物打印QC中的ML挑战和未来方向的见解.
主要方法:
- 在生物打印中基于ML的QC解决方案的全面文献分析.
- 基于使用的数据,算法和性能指标评估ML模型.
- 评估ML在加工前,加工中和加工后的质量控制中的作用.
主要成果:
- 机器学习算法显示出自动化生物打印QC的前景.
- ML可以减少批次间的变化,提高产品的一致性.
- 确定了各种ML模型及其在生物打印QC中的技术性能.
结论:
- 机器学习是推动生物打印QC的关键技术.
- 通过ML自动化QC可以加速临床翻译和商业化.
- 需要进一步的研究来克服ML驱动生物打印QC的当前挑战.
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