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Comprehensive assessment of minors' biological profiles using explainable machine learning: the assessment of age, stature, body mass, and sex.

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Title: Comprehensive assessment of minors' biological profiles using explainable machine learning: the assessment of age, stature, body mass, and sex.
Authors: Fan F; West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, PR China., Sun Y; West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, PR China., Zhang X; West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, PR China., Yang H; West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, PR China., Ning G; Department of Radiology, West China Second University Hospital, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Chengdu, 610041, PR China., Deng Z; West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, PR China. dengzhenhua@scu.edu.cn., Zhan M; West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, PR China. zhanmengjun@scu.edu.cn.
Source: International journal of legal medicine [Int J Legal Med] 2026 Apr 22. Date of Electronic Publication: 2026 Apr 22.
Publication Type: Journal Article
Journal Info: Publisher: Springer International Country of Publication: Germany NLM ID: 9101456 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1437-1596 (Electronic) Linking ISSN: 09379827 NLM ISO Abbreviation: Int J Legal Med Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Comprehensive assessment of minors' biological profiles using explainable machine learning: the assessment of age, stature, body mass, and sex.
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  Data: <searchLink fieldCode="AU" term="%22Fan+F%22">Fan F</searchLink>; West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, PR China.<br /><searchLink fieldCode="AU" term="%22Sun+Y%22">Sun Y</searchLink>; West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, PR China.<br /><searchLink fieldCode="AU" term="%22Zhang+X%22">Zhang X</searchLink>; West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, PR China.<br /><searchLink fieldCode="AU" term="%22Yang+H%22">Yang H</searchLink>; West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, PR China.<br /><searchLink fieldCode="AU" term="%22Ning+G%22">Ning G</searchLink>; Department of Radiology, West China Second University Hospital, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Chengdu, 610041, PR China.<br /><searchLink fieldCode="AU" term="%22Deng+Z%22">Deng Z</searchLink>; West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, PR China. dengzhenhua@scu.edu.cn.<br /><searchLink fieldCode="AU" term="%22Zhan+M%22">Zhan M</searchLink>; West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, PR China. zhanmengjun@scu.edu.cn.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Springer+International%22">Springer International </searchLink><i>Country of Publication: </i>Germany <i>NLM ID: </i>9101456 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1437-1596 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2209379827%22">09379827 </searchLink><i>NLM ISO Abbreviation: </i>Int J Legal Med <i>Subsets: </i>MEDLINE
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        Value: 10.1007/s00414-026-03802-4
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      – TitleFull: Comprehensive assessment of minors' biological profiles using explainable machine learning: the assessment of age, stature, body mass, and sex.
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              Text: 2026 Apr 22
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