基于深度卷积神经网络的中国汉族男性青少年手部和腕关节 MRI 图像的法医骨龄评估

IF 2.2 3区 医学 Q1 MEDICINE, LEGAL
International Journal of Legal Medicine Pub Date : 2024-11-01 Epub Date: 2024-07-26 DOI:10.1007/s00414-024-03282-4
Hui-Ming Zhou, Zhi-Lu Zhou, Yu-Heng He, Tai-Ang Liu, Lei Wan, Ya-Hui Wang
{"title":"基于深度卷积神经网络的中国汉族男性青少年手部和腕关节 MRI 图像的法医骨龄评估","authors":"Hui-Ming Zhou, Zhi-Lu Zhou, Yu-Heng He, Tai-Ang Liu, Lei Wan, Ya-Hui Wang","doi":"10.1007/s00414-024-03282-4","DOIUrl":null,"url":null,"abstract":"<p><p>In Chinese criminal law, the ages of 12, 14, 16, and 18 years old play a significant role in the determination of criminal responsibility. In this study, we developed an epiphyseal grading system based on magnetic resonance image (MRI) of the hand and wrist for the Chinese Han population and explored the feasibility of employing deep learning techniques for bone age assessment based on MRI of the hand and wrist. This study selected 282 Chinese Han Chinese males aged 6.0-21.0 years old. In the course of our study, we proposed a novel deep learning model for extracting and enhancing MRI hand and wrist bone features to enhance the prediction of target MRI hand and wrist bone age and achieve precise classification of the target MRI and regression of bone age. The evaluation metric for the classification model including precision, specificity, sensitivity, and accuracy, while the evaluation metrics chosen for the regression model are MAE. The epiphyseal grading was used as a supervised method, which effectively solved the problem of unbalanced sample distribution, and the two experts showed strong consistency in the epiphyseal plate grading process. In the classification results, the accuracy in distinguishing between adults and minors was 91.1%, and the lowest accuracy in the three minor classifications (12, 14, and 16 years of age) was 94.6%, 91.1% and 96.4%, respectively. The MAE of the regression results was 1.24 years. In conclusion, the deep learning model proposed enabled the age assessment of hand and wrist bones based on MRI.</p>","PeriodicalId":14071,"journal":{"name":"International Journal of Legal Medicine","volume":" ","pages":"2427-2440"},"PeriodicalIF":2.2000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Forensic bone age assessment of hand and wrist joint MRI images in Chinese han male adolescents based on deep convolutional neural networks.\",\"authors\":\"Hui-Ming Zhou, Zhi-Lu Zhou, Yu-Heng He, Tai-Ang Liu, Lei Wan, Ya-Hui Wang\",\"doi\":\"10.1007/s00414-024-03282-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In Chinese criminal law, the ages of 12, 14, 16, and 18 years old play a significant role in the determination of criminal responsibility. In this study, we developed an epiphyseal grading system based on magnetic resonance image (MRI) of the hand and wrist for the Chinese Han population and explored the feasibility of employing deep learning techniques for bone age assessment based on MRI of the hand and wrist. This study selected 282 Chinese Han Chinese males aged 6.0-21.0 years old. In the course of our study, we proposed a novel deep learning model for extracting and enhancing MRI hand and wrist bone features to enhance the prediction of target MRI hand and wrist bone age and achieve precise classification of the target MRI and regression of bone age. The evaluation metric for the classification model including precision, specificity, sensitivity, and accuracy, while the evaluation metrics chosen for the regression model are MAE. The epiphyseal grading was used as a supervised method, which effectively solved the problem of unbalanced sample distribution, and the two experts showed strong consistency in the epiphyseal plate grading process. In the classification results, the accuracy in distinguishing between adults and minors was 91.1%, and the lowest accuracy in the three minor classifications (12, 14, and 16 years of age) was 94.6%, 91.1% and 96.4%, respectively. The MAE of the regression results was 1.24 years. In conclusion, the deep learning model proposed enabled the age assessment of hand and wrist bones based on MRI.</p>\",\"PeriodicalId\":14071,\"journal\":{\"name\":\"International Journal of Legal Medicine\",\"volume\":\" \",\"pages\":\"2427-2440\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Legal Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00414-024-03282-4\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/7/26 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, LEGAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Legal Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00414-024-03282-4","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/26 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MEDICINE, LEGAL","Score":null,"Total":0}
引用次数: 0

摘要

在中国刑法中,12 岁、14 岁、16 岁和 18 岁对刑事责任的认定起着重要作用。在本研究中,我们为中国汉族人群开发了基于手部和腕部磁共振成像(MRI)的骺板分级系统,并探索了基于手部和腕部磁共振成像的骨龄评估采用深度学习技术的可行性。本研究选取了 282 名年龄在 6.0-21.0 岁之间的中国汉族男性作为研究对象。在研究过程中,我们提出了一种新颖的深度学习模型,用于提取和增强核磁共振手部和腕部骨骼特征,以增强对目标核磁共振手部和腕部骨龄的预测,实现目标核磁共振的精确分类和骨龄回归。分类模型的评价指标包括精确度、特异度、灵敏度和准确度,回归模型的评价指标则选择 MAE。骺板分级采用监督方法,有效解决了样本分布不均衡的问题,两位专家在骺板分级过程中表现出较强的一致性。在分类结果中,区分成年人和未成年人的准确率为 91.1%,在三个未成年人分类(12 岁、14 岁和 16 岁)中准确率最低,分别为 94.6%、91.1% 和 96.4%。回归结果的 MAE 为 1.24 岁。总之,所提出的深度学习模型能够根据核磁共振成像对手部和腕部骨骼进行年龄评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Forensic bone age assessment of hand and wrist joint MRI images in Chinese han male adolescents based on deep convolutional neural networks.

Forensic bone age assessment of hand and wrist joint MRI images in Chinese han male adolescents based on deep convolutional neural networks.

In Chinese criminal law, the ages of 12, 14, 16, and 18 years old play a significant role in the determination of criminal responsibility. In this study, we developed an epiphyseal grading system based on magnetic resonance image (MRI) of the hand and wrist for the Chinese Han population and explored the feasibility of employing deep learning techniques for bone age assessment based on MRI of the hand and wrist. This study selected 282 Chinese Han Chinese males aged 6.0-21.0 years old. In the course of our study, we proposed a novel deep learning model for extracting and enhancing MRI hand and wrist bone features to enhance the prediction of target MRI hand and wrist bone age and achieve precise classification of the target MRI and regression of bone age. The evaluation metric for the classification model including precision, specificity, sensitivity, and accuracy, while the evaluation metrics chosen for the regression model are MAE. The epiphyseal grading was used as a supervised method, which effectively solved the problem of unbalanced sample distribution, and the two experts showed strong consistency in the epiphyseal plate grading process. In the classification results, the accuracy in distinguishing between adults and minors was 91.1%, and the lowest accuracy in the three minor classifications (12, 14, and 16 years of age) was 94.6%, 91.1% and 96.4%, respectively. The MAE of the regression results was 1.24 years. In conclusion, the deep learning model proposed enabled the age assessment of hand and wrist bones based on MRI.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.80
自引率
9.50%
发文量
165
审稿时长
1 months
期刊介绍: The International Journal of Legal Medicine aims to improve the scientific resources used in the elucidation of crime and related forensic applications at a high level of evidential proof. The journal offers review articles tracing development in specific areas, with up-to-date analysis; original articles discussing significant recent research results; case reports describing interesting and exceptional examples; population data; letters to the editors; and technical notes, which appear in a section originally created for rapid publication of data in the dynamic field of DNA analysis.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信