AI Insight
Researchers developed a deep learning framework that predicts the blood compatibility of electrospun nanomaterials directly from scanning electron microscopy images, achieving 99% accuracy using an ensemble of three convolutional neural networks. The method uses explainable AI techniques to confirm that predictions are based on relevant nanofiber microstructural features rather than spurious patterns. This approach offers a sustainable alternative to traditional blood-based compatibility testing by reducing the need for biological samples, specialized reagents, and laboratory waste.
Why it matters
This framework enables faster, more cost-effective screening of nanomaterials for blood-contacting medical devices while significantly reducing environmental impact and animal/human sample requirements. The method aligns with ISO 10993 regulatory guidelines and could accelerate biomaterial development cycles by providing reliable pre-screening before conducting mandatory biological validation tests.
Understand the Science
by Narasimhan Kumaravelu, Adalarasu Kanagasabai, Preetham Raj Aravindan, Surya Murugan Moorthy Sathyanarayana, Saravana Kumar Jaganathan
Background
Electrospun nanocomposite biomaterials are promising candidates for blood-contacting medical devices owing to their high surface area, tunable nanoscale morphology, and biomimetic replication of the native extracellular matrix. However, conventional hemocompatibility assessments rely heavily on biological assays. These methods require blood samples, specialized reagents, and consumables, thereby inherently increasing costs, resource demands, and laboratory waste. To mitigate these limitations, sustainable evaluation strategies that leverage physicochemical features are highly desirable.
Methodology
In this study, we present a deep learning framework for predicting the hemocompatibility of electrospun nanofibres directly from scanning electron microscopy (SEM) images. Pretrained convolutional neural network models, namely VGG19, ResNet50, and InceptionV3, were fine-tuned to classify scanning electron microscopy (SEM) images of nanofibres as haemocompatible or non-haemocompatible. To ensure data robustness, an Albumentations-based augmentation strategy was implemented, and predictive reliability was further optimized using a majority-voting ensemble learning approach.
Results
Among the individual architectures evaluated, InceptionV3 demonstrated superior performance, achieving a precision of 0.98, a recall of 1.00, and an F1-score of 0.99. The ensemble model exhibited comparable robustness, reaching an overall accuracy of 99%. Furthermore, explainable AI (XAI) techniques, specifically LIME and Integrated Gradients, confirmed that the network’s predictions were driven by physiologically relevant microstructural features of the nanofibres.
Conclusion
This artificial intelligence-assisted, SEM image-based physicochemical approach offers an eco-friendly pre-screening strategy that significantly reduces reliance on blood-based assays and associated laboratory consumables. Ultimately, this framework aligns with ISO 10993 guidelines by using SEM-derived physicochemical information as a pre-screening tool to support subsequent biological evaluation, thereby enabling efficient, regulatory-compliant biomaterial development.