aceSustainable hemocompatibility prediction of electrospun nanomaterials using SEM-driven CNN ensemble learning aligned with ISO 10993
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, sustain…
Fine-tuned CNN models classify nanofibre SEM images as haemocompatible or not, offering a pre-screening step that could cut blood-based assays and lab consumables.
The authors describe the method as a pre-screening tool that supports, rather than replaces, subsequent biological evaluation.
Evidence
- Peer-reviewedPLOS One2026-10-05
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Truvace Impact Record TRV-2026-1866, v1: “Sustainable hemocompatibility prediction of electrospun nanomaterials using SEM-driven CNN ensemble learning aligned with ISO 10993.” Truvace, 2026-10-18. /record/TRV-2026-1866 (accessed at citation time). sha256 f597e695a80d1d5b…
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