Comparative Analysis of Deep Learning Models for Predicting Biocompatibility in Tissue Scaffold Images

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. Description: Fifteen key design parameters influencing scaffold biocompatibility were modelled using ANN, while scaffold images were analyzed using CNN. PrusaSlicer was employed in designing scaffolds, with parameters influencing biocompatibility predictions. ANN models analyzed these parameters, while CNN models processed scaffold images. Data was standardized, and models were trained on an 80/20 split dataset. Performance evaluation metrics included accuracy, precision, recall, F1-Scores, and confusion matrices. Experimental validation involved biocompatibility tests on five scaffolds. Results: ANN model with 20 neurons and 100 epochs earned perfect (1.0) scores in F1-Score, Precision, and Recall, indicating the best possible model performance. A batch size of 56 for the Convolutional Neural Network model demonstrated balance in F1-Score (0.87), Precision (0.88), and Recall (0.9). Five scaffold tissues were tested for biocompatibility using these two models. ANN model predicted 5 scaffold tissues’ biocompatibilities correctly. While the ANN model accurately predicted biocompatibilities for all five scaffold samples, the CNN model misclassified one sample. Conclusion: This study demonstrates that ANN models are superior to CNN models in predicting scaffold biocompatibility from numerical design parameters. The findings underscore the value of ANNs for structured data in bioprinting, enhancing prediction accuracy and efficiency. These insights can accelerate advancements in tissue engineering and personalized medicine by reducing costs and improving success rates in bioprinting applications. Future work will focus on addressing overfitting challenges and optimizing the models to further enhance their robustness and predictive capabilities.