用机器学习预测静电纺丝纤维直径分布，提升组织工程支架设计精度。
FibreCastML: An Open Web Platform for Predicting Electrospun Nanofibre Diameter Distributions
- 基于6个工艺参数构建分布感知的机器学习模型，预测完整纤维直径谱。
- 非线性模型对多种聚合物的预测决定系数超0.91，优于线性基准。
- 开源平台支持可解释性分析，适合材料与生物医学工程师使用。
静电纺丝是一种可扩展的技术，可用于制备具有可调微纳结构的纤维支架，应用于组织工程、药物递送和伤口护理。尽管机器学习已用于优化静电纺丝工艺，但现有方法大多仅预测平均纤维直径，忽略了决定支架性能的完整直径分布。本文提出FibreCastML，一个开放的、分布感知的机器学习框架，能够从常规报告的静电纺丝参数中预测完整的纤维直径谱，并提供工艺与结构关系的可解释洞察。研究构建了一个包含1778项研究、68538个独立纤维直径测量值的元数据集，涵盖16种生物医用聚合物。使用六个标准工艺参数——溶液浓度、施加电压、流速、针头到收集器距离、针头直径和收集器转速——通过嵌套交叉验证与留一研究外部折叠训练了七个机器学习模型。模型可解释性通过变量重要性分析、SHapley Additive exPlanations、相关性矩阵和三维参数图实现。非线性模型始终优于线性基线，在多种常用聚合物上达到超过0.91的决定系数。溶液浓度被确定为纤维直径分布的主导全局驱动因素。在不同静电纺丝系统上的实验验证显示，预测与实测分布高度一致。FibreCastML实现了更可重复、数据驱动的静电纺丝支架结构优化。
原文摘要 · Abstract (English)
Electrospinning is a scalable technique for producing fibrous scaffolds with tunable micro- and nanoscale architectures for applications in tissue engineering, drug delivery, and wound care. While machine learning (ML) has been used to support electrospinning process optimisation, most existing approaches predict only mean fibre diameters, neglecting the full diameter distribution that governs scaffold performance. This work presents FibreCastML, an open, distribution-aware ML framework that predicts complete fibre diameter spectra from routinely reported electrospinning parameters and provides interpretable insights into process structure relationships. A meta-dataset comprising 68538 individual fibre diameter measurements extracted from 1778 studies across 16 biomedical polymers was curated. Six standard processing parameters, namely solution concentration, applied voltage, flow rate, tip to collector distance, needle diameter, and collector rotation speed, were used to train seven ML models using nested cross validation with leave one study out external folds. Model interpretability was achieved using variable importance analysis, SHapley Additive exPlanations, correlation matrices, and three dimensional parameter maps. Non linear models consistently outperformed linear baselines, achieving coefficients of determination above 0.91 for several widely used polymers. Solution concentration emerged as the dominant global driver of fibre diameter distributions. Experimental validation across different electrospinning systems demonstrated close agreement between predicted and measured distributions. FibreCastML enables more reproducible and data driven optimisation of electrospun scaffold architectures.
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