Application of artificial intelligence for dental age estimation in children and adolescents: A review - Kurniawan, Arofi; Chusida, An'nisaa; Rahmaputri, Annisa; Nurmalia, Salsabila; Prasetyo, Aulia Imani Sri; Akbar, Aeeshah Aswi; Maritza, Yasmina Putri; Rizky, Beta Novia; Marini, Maria Istiqomah; Alias, Aspalilah; Marya, Anand (Faculty of Dental Medicine Universitas Airlangga, 2025)
Background: Accurate dental age estimation is essential across multiple disciplines, including forensic identification, pediatric dentistry, and legal medicine. Conventional approaches, while extensively utilized, are constrained by observer subjectivity, population-dependent variation, and limited reproducibility. The emergence of artificial intelligence (AI) particularly through machine learning (ML) and deep learning (DL) technologies has introduced a transformative shift in age estimation, offering automated, data-driven alternatives that enhance precision, consistency, and efficiency. Purpose: This review aims to critically examine the current applications of AI in dental age estimation for children and adolescents. Review: An online literature search was conducted in the PubMed database using a structured set of keywords, complemented by manual searches through Google Scholar to ensure comprehensive coverage. Nine relevant studies were identified, encompassing a range of artificial intelligence (AI) approaches, including artificial neural networks (ANN), convolutional neural networks (CNN), support vector machines (SVM), and other machine learning (ML) algorithms. These models were applied to established dental age estimation methods such as those proposed by Demirjian, Willems, Cameriere, and Al-Qahtani. Overall, AI-based models demonstrated superior performance compared to traditional techniques, showing lower mean absolute error values and higher classification accuracy across various age categories. Notably, several models achieved accuracy levels exceeding 90%, highlighting the potential of AI to enhance precision and reliability in dental age estimation. Conclusion: Artificial intelligence demonstrates significant potential in improving the accuracy, efficiency, and reproducibility of dental age estimation in children and adolescents. While current findings are promising, further validation across diverse populations and standardized protocols is necessary before widespread forensic and clinical adoption.