Artificial Intelligence in Pediatric Dentistry – A Systematic Review

Abstract

Abstract Background: Artificial intelligence (AI) refers to the development of computer systems capable of performing tasks that normally require human intelligence. AI and its subsets, machine learning and deep learning, have been incorporated into several aspects of dentistry including pediatric dentistry. However, there is a lack of documentation and analysis of the current applications of AI in pediatric dentistry. Aim: The aim of this systematic review was to evaluate the effectiveness of AI as a diagnostic tool in pediatric dentistry. Materials and Methods: The literature for this paper was identified by performing a thorough search in electronic databases such as PubMed, Google Scholar, and Cochrane Library from the years 2011 to 2021. The following keywords and Boolean operators were used: AI AND pediatric dentistry, artificial neural networks AND pediatric dentistry, convolutional neural networks AND pediatric dentistry, and machine learning AND pediatric dentistry. After applying appropriate inclusion and exclusion criteria, 13 articles were selected, fully read, and systematically analyzed as per a specific research question. Results: Among the 13 selected articles, it was found that AI is a useful tool for dental diagnosis/classification, cephalometric landmark identification, identification of early childhood caries patterns, chronological age assessment in children, assessment of facial attractiveness in cleft patients, dental plaque detection, and oral health education. Conclusion: The selected articles indicate that AI is an effective diagnostic tool and has the potential for assisting several aspects of pediatric dentistry. However, further studies are required to assess the clinical effectiveness of these AI models.