Journal of Forensic Medicine ›› 2024, Vol. 40 ›› Issue (6): 589-596.DOI: 10.12116/j.issn.1004-5619.2023.231203

• Original Article • Previous Articles     Next Articles

MRI Application in Quantification of Epiphyseal Development in the Wrist and Bone Age Estimation of Han Male Adolescents in East China

Zhi-lu ZHOU1(), Dong-fei ZHANG2(), Jie-min CHEN3, Ya-hui WANG3, Hong-xia HAO3, Tai-ang LIU4, Yu-heng HE4, Ding-nian LONG5, Rui-jue LIU3(), Lei WAN3()   

  1. 1.School of Forensic Medicine, Guizhou Medical University, Guiyang 550009, China
    2.Anhui Tianheng Forensic Judicial Identification Institute, Fuyang 236000, Anhui Province, China
    3.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
    4.Shanghai Shuzhiwei Information Technology Co. , Ltd, Shanghai 200444
    5.Guangxi Jingui Judicial Expertise Center, Nanning 530000, China
  • Received:2023-12-22 Online:2025-03-10 Published:2024-12-25
  • Contact: Rui-jue LIU, Lei WAN

Abstract:

Objective To investigate the value of wrist MRI in bone age estimation for male adolescents in Shanghai, Zhejiang and Jiangsu. Methods A total of 124 Han male adolescents aged 6.0 to 18.0 years from Shanghai, Zhejiang and Jiangsu were selected as subjects. Their weight and height were measured, and T1WI and T2WI sequences of the wrist were scanned. The distal ends of the radius and ulna, and the first to five metacarpal epiphyses and corresponding metaphyses were selected as observational indexes after MRI images of the wrist were obtained. The development of each index was classified (0-2 grades) by a deputy senior imaging expert, then the maximum width of each index was measured by another deputy senior expert. Height, weight, classification and maximum width of indexes were used as input variables, and age was used as the target variable. Support vector machine, random forest, current reality tree, and linear regression models were established to estimate the bone age, and the model with the highest accuracy was selected. Results The height, weight, classification of wrist bone epiphysis development, maximum width of each bone metaphysis and epiphysis were all correlated with age (P<0.05). The accuracies of the support vector machine were the highest when the differences between bone age and actual chronological age were within 1.0 and 1.5 years (88.7% and 96.0%, respectively). Conclusion It is feasible to estimate bone age by using MRI images. Quantifying the maximum width of the epiphysis and corresponding metaphysis of bone and combining it with MRI image classification can effectively reduce the estimation error.

Key words: forensic anthropology, age estimation, magnetic resonance imaging (MRI), wrist, epiphysis, stage of skeletal growth, machine learning, adolescents

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