Sustainable hemocompatibility prediction of electrospun nanomaterials using SEM-driven CNN ensemble learning aligned with ISO 10993
4 affiliations
- 1School of Electrical and Electronics Engineering, SASTRA Deemed to be University, Thanjavur, Tamil Nadu, India
- 2Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- 3School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam
- 4Biomedical Engineering Research Group, School of Engineering, University of Leicester, Leicester, United Kingdom
Abstract
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.
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BibTeX
@article{kumaravelu2026sustainable,
title = {{Sustainable hemocompatibility prediction of electrospun nanomaterials using SEM-driven CNN ensemble learning aligned with ISO 10993}},
author = {Narasimhan Kumaravelu and Adalarasu Kanagasabai and Preetham Raj Aravindan and Surya Murugan Moorthy Sathyanarayana and Saravana Kumar Jaganathan},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0359594},
url = {https://doi.org/10.1371/journal.pone.0359594},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Sustainable hemocompatibility prediction of electrospun nanomaterials using SEM-driven CNN ensemble learning aligned with ISO 10993
AU - Narasimhan Kumaravelu
AU - Adalarasu Kanagasabai
AU - Preetham Raj Aravindan
AU - Surya Murugan Moorthy Sathyanarayana
AU - Saravana Kumar Jaganathan
PY - 2026
DA - 2026/10/05
JO - PLOS One
DO - 10.1371/journal.pone.0359594
UR - https://doi.org/10.1371/journal.pone.0359594
AB - 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.
SN - 1932-6203
ER -