Comparative analysis of deep learning models for predicting biocompatibility in tissue scaffold images.

Journal: Computers in biology and medicine
Published Date: Apr 29, 2025

Abstract

MOTIVATION: Bioprinting enables the creation of complex tissue scaffolds, which are vital for tissue engineering. However, predicting scaffold biocompatibility before fabrication remains a critical challenge, potentially leading to inefficiencies and resource wastage. Artificial Intelligence (AI) models, particularly Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), offer promising predictive capabilities to address this issue. This study aims to compare the performance of ANN and CNN models to identify the most suitable approach for predicting scaffold biocompatibility using PrusaSlicer-generated designs.

Authors

  • Emir Oncu
    Department of Bioengineering, Faculty of Chemical and Metallurgical Engineering, Yıldız Technical University, İstanbul, 34210, Turkey; BioriginAI Research Group, Department of Biomedical Engineering, Fatih Sultan Mehmet Vakıf University, Istanbul, 34015, Turkey. Electronic address: biomedical.emr@gmail.com.
  • Kadriye Yasemin Usta Ayanoglu
    Department of Tropiko Software and Consultancy, Istanbul, Turkey.
  • Fatih Ciftci
    BioriginAI Research Group, Department of Biomedical Engineering, Fatih Sultan Mehmet Vakıf University, Istanbul, 34015, Turkey; Faculty of Engineering, Department of Biomedical Engineering, Fatih Sultan Mehmet Vakıf University, Istanbul, 34015, Turkey; Department of Technology Transfer Office, Fatih Sultan Mehmet Vakıf University, Istanbul, 34015, Turkey. Electronic address: faciftcii@gmail.com.

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