Artificial Neural Networks: A New Frontier in Dental Tissue Regeneration

In the realm of dental tissue regeneration research, various constraints exist such as the potential variance in cell quality, potency arising from differences in donor tissue and tissue microenvironment, the difficulties associated with sustaining long-term and large-scale cell expansion while preserving stemness and therapeutic attributes, as well as the need for extensive investigation into the enduring safety and effectiveness in clinical settings. The adoption of artificial intelligence (AI) technologies has been suggested as a means to tackle these challenges. This is because, tissue regeneration research could be advanced through the use of diagnostic systems that incorporate mining methods such as neural networks (NN), fuzzy, predictive modeling, genetic algorithms, machine learning (ML), cluster analysis, and decision trees. This article seeks to offer foundational insights into a subset of AI referred to as artificial neural networks (ANNs) and assess their potential applications as essential decision-making support tools in the field of dentistry, with a particular focus on tissue engineering research. Although ANNs may initially appear complex and resource intensive, they have proven to be effective in laboratory and therapeutic settings. This expert system can be trained using clinical data alone, enabling their deployment in situations where rule-based decision-making is impractical. As ANNs progress further, it is likely to play a significant role in revolutionizing dental tissue regeneration research, providing promising results in streamlining dental procedures and improving patient outcomes in the clinical setting.