Peng L Q, Wan L, Wang M W, Li Z, Zhao H, Wang Y H
Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun-Yat Sen University, Guangzhou 510080, China.
Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
Fa Yi Xue Za Zhi. 2020 Feb;36(1):91-98. doi: 10.12116/j.issn.1004-5619.2020.01.018.
Bone age assessment has always been one of the key issues and difficulties in forensic science. With the gradual development of machine learning in many industries, it has been widely introduced to imageology, genomics, oncology, pathology, surgery and other medical research fields in recent years. The reason why the above research fields can be closely combined with machine learning, is because the research subjects of the above branches of medicine belong to the computer vision category. Machine learning provides unique advantages for computer vision research and has made breakthroughs in medical image recognition. Based on the advantages of machine learning in image recognition, it was combined with bone age assessment research, in order to construct a recognition model suitable for forensic skeletal images. This paper reviews the research progress in bone age assessment made by scholars at home and abroad using machine learning technology in recent years.
骨龄评估一直是法医学中的关键问题和难点之一。随着机器学习在众多行业的逐步发展，近年来它已被广泛引入到影像学、基因组学、肿瘤学、病理学、外科手术等医学研究领域。上述研究领域能够与机器学习紧密结合的原因在于，上述医学分支的研究对象属于计算机视觉范畴。机器学习为计算机视觉研究提供了独特优势，并在医学图像识别方面取得了突破。基于机器学习在图像识别方面的优势，将其与骨龄评估研究相结合，以构建适用于法医骨骼图像的识别模型。本文综述了近年来国内外学者利用机器学习技术在骨龄评估方面取得的研究进展。
