基于机器学习的牙龄评测研究进展

Research progress of dental age evaluation based on machine learning methods

牙龄是生物学年龄的一项重要指标,在口腔正畸学、牙科法医学、刑侦学等方面有重要的应用价值。牙龄评测的传统方法包括图谱法、计分法、牙髓腔增龄性评测法等,这些人工评测方法较为烦琐而影响其推广应用。随着人工智能和机器学习相关技术的发展,近年来出现了牙龄的智能评测相关研究,并在算法准确性等方面取得了一定的进展。本文对牙龄评测的传统方法和机器学习方法进行总结,归纳了在探索及实践过程中传统方法与现代方法的优势和不足,旨在为牙龄评测的后续研究和应用提供参考。

Dental age is an important indicator of biological age and has essential application value in orthodontics, forensic dentistry, and criminal investigation. Traditional methods of dental age evaluation include atlas, scoring, and pulp cavity evaluation. These manual evaluation methods are relative cumbersome that their applications have been hampered.With the development of artificial intelligence technology and machine learning algorithms, intelligent evaluation of dental age have been gained more attention in recent years. This paper aims to provide a reference for the future research and application of dental age evaluation, summarize traditional and machine learning methods, and demonstrate the benefits and limitations of the existing approaches that are used in this topic.