Bone development shows certain regularity with age. The regularity can be used to infer age and serve many fields such as justice, medicine, archaeology, etc. As a non-invasive evaluation method of the epiphyseal development stage, MRI is widely used in living age estimation. In recent years, the rapid development of machine learning has significantly improved the effectiveness and reliability of living age estimation, which is one of the main development directions of current research. This paper summarizes the analysis methods of age estimation by knee joint MRI, introduces the current research trends, and future application trend.
骨骺发育随年龄增长呈现一定的规律，通过该规律推断年龄，可服务于司法、医学、考古等多个领域。MRI作为一种无侵入性的骨骺发育阶段的评估方法，被广泛应用于活体年龄推断。近年来，机器学习发展迅速，显著提升了活体年龄推断的有效性和可靠性，是目前研究的主要发展方向之一。本文通过总结膝关节MRI推断年龄的分析方法，介绍目前研究动态并展望后期应用趋势。.
Keywords: age estimation; deep learning; forensic anthropolgy; knee joint; magnetic resonance imaging; review.