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Area of Science:

  • Biomedical Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Microneedles (MNs) offer a less painful alternative to conventional needles for drug delivery and sample collection.
  • 3D printing technologies, like fused deposition modeling (FDM), provide cost-effective and efficient manufacturing of MNs.
  • Integrating artificial intelligence (AI), including machine learning (ML) and deep learning (DL), presents opportunities for optimizing biomedical device fabrication.

Purpose of the Study:

  • To develop and present an AI framework for assessing and predicting the features of 3D-printed microneedles.
  • To utilize DL for quality control and anomaly detection in fabricated MNAs.
  • To train ML models for predicting fabrication outcomes based on design and etching parameters.

Main Methods:

  • Fabrication of biodegradable MNs using FDM 3D printing.
  • Chemical etching to improve the geometrical precision of MNs.
  • Application of DL for quality control and anomaly detection.
  • Development of ML models using a data library of ten MN designs and various etching doses to predict fabrication outcomes.

Main Results:

  • The AI framework successfully assessed and predicted features of 3D-printed MNs.
  • DL enabled effective quality control and anomaly detection in the fabricated MNAs.
  • ML models demonstrated the ability to predict new fabrication outcomes by adjusting parameters.

Conclusions:

  • The integration of AI with 3D-printed MNs facilitates the development of advanced healthcare systems.
  • This approach enhances the biomedical applications of microneedles.
  • AI-driven prediction optimizes the manufacturing process for microneedle-based therapies.