DefinePK

Abstract

Natal teeth, present at birth, are rare developmental anomalies that pose both clinical and diagnostic challenges. These teeth are often associated with complications such as feeding difficulties, oral trauma, and in some cases, systemic syndromes. Traditional understanding of natal teeth is based primarily on case reports and small-scale clinical studies, which offer limited insight into broader patterns and predictive factors. With the advent of machine learning (ML) in healthcare, there is growing interest in using computational approaches to identify predictive indicators and enhance early diagnosis. This review synthesizes recent clinical literature on natal teeth and explores the potential application of ML techniques for predictive modeling. The integration of clinical findings with AI-based tools could pave the way for improved diagnostics and proactive neonatal care