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

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Bibliographic Details
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
Description
ISSN:1437-1596
DOI:10.1007/s00414-026-03802-4