Research Progress of Age Estimation in the Living by Knee Joint MRI

doi:10.12116/j.issn.1004-5619.2022.220503

Abstract

Abstract:

Bone development shows certain regularity with age. The regularity can be used to infer age and serve many fields such as justice， medicine， archaeology， etc. As a non-invasive evaluation method of the epiphyseal development stage， MRI is widely used in living age estimation. In recent years， the rapid development of machine learning has significantly improved the effectiveness and reliability of living age estimation， which is one of the main development directions of current research. This paper summarizes the analysis methods of age estimation by knee joint MRI， introduces the current research trends， and future application trend.

Key words: forensic anthropolgy, age estimation, magnetic resonance imaging, knee joint, deep learning, review

CLC Number:

DF795.6

Hong-xia HAO, Ya-hui WANG, Zhi-lu ZHOU, Tai-ang LIU, Jin CHEN, Yu-heng HE, Lei WAN, Wen-tao XIA. Research Progress of Age Estimation in the Living by Knee Joint MRI[J]. Journal of Forensic Medicine, 2023, 39(1): 66-71.

References 52

1	鲁婷，范飞，施蕾，等. MRI在法医学活体年龄推断中的研究进展[J].法医学杂志，2020，36（4）：549-558. doi：10.12116/j.issn.1004-5619.2020.04.022 .
	LU T， FAN F， SHI L， et al. Research progress on the forensic age estimation in living individuals using MRI[J]. Fayixue Zazhi，2020，36（4）：549-558.
2	SCHMELING A， DETTMEYER R， RUDOLF E， et al. Forensic age estimation[J]. Dtsch Arztebl Int，2016，113（4）：44-50. doi：10.3238/arztebl.2016.0044 .
3	DVORAK J， GEORGE J， JUNGE A， et al. Age determination by magnetic resonance imaging of the wrist in adolescent male football players[J]. Br J Sports Med，2007，41（1）：45-52. doi：10.1136/bjsm.2006.031021 .
4	CATTANEO C， RITZ-TIMME S， GABRIEL P， et al. The difficult issue of age assessment on pedo-pornographic material[J]. Forensic Sci Int，2009，183（1/2/3）：e21-e24. doi：10.1016/j.forsciint. 2008.09.005 .
5	RÖSING F W， GRAW M， MARRÉ B， et al. Arbeitsgemeinschaft für forensische altersdiagnostik der deutschen gesellschaft für rechtsmedizin[J]. Rechtsmedizin，2005，15（1）：32-38. doi：10.1007/s00194-004-0296-9 .
6	SWEET M B， THONAR E J， IMMELMAN A R， et al. Biochemical changes in progressive osteoarthrosis[J]. Ann Rheum Dis，1977，36（5）：387-398. doi：10.1136/ard.36.5.387 .
7	MOSHER T J， DARDZINSKI B J. Cartilage MRI T₂relaxation time mapping： Overview and applications[J]. Semin Musculoskelet Radiol，2005，8（4）：355-368. doi：10.1055/s-2004-861764 .
8	SHIMADA Y， SHIMAO D， KOBAYASHI J， et al. Comparison of MR images for age determination； T₁ weighted images （T₁WI） versus T₂* weighted images （T₂*WI）[J]. Asian J Sports Med，2012，3（1）：47-52. doi：10.5812/asjsm.34727 .
9	南细柳. 多序列MRI扫描在诊断腰椎压缩性骨折中的效果观察[J].基层医学论坛，2022，26（11）：66-68. doi：10.19435/j.1672-1721.2022.11.022 .
	NAN X L. Effect of multi-sequence MRI scanning in the diagnosis of lumbar compression fracture[J]. Jiceng Yixue Luntan，2022，26（11）：66-68.
10	李承启，曹代荣，邢振，等. MRI场强及设备类型对同反相位成像定量分析健康志愿者腰椎脂肪含量的影响[J].中国中西医结合影像学杂志，2015，13（2）：122-125. doi：10.3969/j.issn.1672-0512.2015.02.002 .
	LI C Q， CAO D R， XING Z， et al. Influence of different field strength and different MR scanner with same field on quantitative analysis for lumbar bone marrow fat content with in- and opposed-phase in healthy volunteers[J]. Zhongguo Zhongxiyi Jiehe Yingxiangxue Zazhi，2015，13（2）：122-125.
11	FAN F， ZHANG K， PENG Z， et al. Forensic age estimation of living persons from the knee： Comparison of MRI with radiographs[J]. Forensic Sci Int，2016，268：145-150. doi：10.1016/j.forsciint. 2016.10.002 .
12	HERRMANN J， SÄRING D， DER MAUER M AUF， et al. Forensic age assessment of the knee： Proposal of a new classification system using two-dimensional ultrasound volumes and comparison to MRI[J]. Eur Radiol，2021，31（5）：3237-3247. doi：10.1007/s00330-020-07343-1 .
13	DODIN P， MARTEL-PELLETIER J， PELLETIER J P， et al. A fully automated human knee 3D MRI bone segmentation using the ray casting technique[J]. Med Biol Eng Comput，2011，49（12）：1413-1424. doi：10.1007/s11517-011-0838-8 .
14	O’CONNOR J E， BOGUE C， SPENCE L D， et al. A method to establish the relationship between chronological age and stage of union from radiographic assessment of epiphyseal fusion at the knee： An Irish population study[J]. J Anat，2008，212（2）：198-209. doi：10.1111/j.1469-7580.2007.00847.x .
15	BANJAR M， HORIUCHI S， GEDEON D N， et al. Review of quantitative knee articular cartilage MR imaging[J]. Magn Reson Med Sci，2022，21（1）：29-40. doi：10.2463/mrms.rev.2021-0052 .
16	LU T， QIU L R， REN B， et al. Forensic age estimation based on magnetic resonance imaging of the proximal humeral epiphysis in Chinese living individuals[J]. Int J Legal Med，2021，135（6）：2437-2446. doi：10.1007/s00414-021-02653-5 .
17	SCHMIDT S， VIETH V， TIMME M， et al. Examination of ossification of the distal radial epiphysis using magnetic resonance imaging. New insights for age estimation in young footballers in FIFA tournaments[J]. Sci Justice，2015，55（2）：139-144. doi：10.1016/j.scijus.2014.12.003 .
18	SERIN J， RÉROLLE C， PUCHEUX J， et al. Contribution of magnetic resonance imaging of the wrist and hand to forensic age assessment[J]. Int J Legal Med，2016，130（4）：1121-1128. doi：10.1007/s00414-016-1362-z .
19	HILLEWIG E， DEGROOTE J， VAN DER PAELT T， et al. Magnetic resonance imaging of the sternal extremity of the clavicle in forensic age estimation： Towards more sound age estimates[J]. Int J Legal Med，2013，127（3）：677-689. doi：10.1007/s00414-012-0798-z .
20	HILLEWIG E， DE TOBEL J， CUCHE O， et al. Magnetic resonance imaging of the medial extremity of the clavicle in forensic bone age determination： A new four-minute approach[J]. Eur Radiol，2011，21（4）：757-767. doi：10.1007/s00330-010-1978-1 .
21	DE TOBEL J， PHLYPO I， FIEUWS S， et al. Forensic age estimation based on development of third molars： A staging technique for magnetic resonance imaging[J]. J Forensic Odontostomatol，2017，35（2）：117-140.
22	DE TOBEL J， HILLEWIG E， BOGAERT S， et al. Magnetic resonance imaging of third molars： Developing a protocol suitable for forensic age estimation[J]. Ann Hum Biol，2017，44（2）：130-139. doi：10.1080/03014460.2016.1202321 .
23	EKIZOGLU O， HOCAOGLU E， CAN I O， et al. Magnetic resonance imaging of distal tibia and calcaneus for forensic age estimation in living individuals[J]. Int J Legal Med，2015，129（4）：825-831. doi：10.1007/s00414-015-1187-1 .
24	SAINT-MARTIN P， RÉROLLE C， DEDOUIT F， et al. Age estimation by magnetic resonance imaging of the distal tibial epiphysis and the calcaneum[J]. Int J Legal Med，2013，127（5）：1023-1030. doi：10.1007/s00414-013-0844-5 .
25	PRÖVE P L， JOPP-VAN WELL E， STANCZUS B， et al. Automated segmentation of the knee for age assessment in 3D MR images using convolutional neural networks[J]. Int J Legal Med，2019，133（4）：1191-1205. doi：10.1007/s00414-018-1953-y .
26	WITTSCHIEBER D， CHITAVISHVILI N， PAPA-GEORGIOU I， et al. Magnetic resonance imaging of the proximal tibial epiphysis is suitable for statements as to the question of majority： A validation study in forensic age diagnostics[J]. Int J Legal Med，2022，136（3）：777-784. doi：10.1007/s00414-021-02766-x .
27	DENG X D， LU T， LIU G F， et al. Forensic age prediction and age classification for critical age thresholds via 3.0T magnetic resonance imaging of the knee in the Chinese Han population[J]. Int J Legal Med，2022，136（3）：1-12. doi：10.1007/S00414-022-02797-Y .
28	SCHMELING A， SCHULZ R， REISINGER W， et al. Studies on the time frame for ossification of the medial clavicular epiphyseal cartilage in conventional radiography[J]. Int J Legal Med，2004，118（1）：5-8. doi：10.1007/s00414-003-0404-5 .
29	SCHMIDT S， MÜHLER M， SCHMELING A， et al. Magnetic resonance imaging of the clavicular ossification[J]. Int J Legal Med，2007，121（4）：321-324. doi：10.1007/s00414-007-0160-z .
30	ZEMBER J S， ROSENBERG Z S， KWONG S， et al. Normal skeletal maturation and imaging pitfalls in the pediatric shoulder[J]. Radiographics，2015，35（4）：1108-1122. doi：10.1148/rg.2015140254 .
31	ALTINSOY H B， GURSES M S， ALATAS O. Evaluation of proximal humeral epiphysis ossification in 3.0 T MR images according to the Dedouit staging method： Is it be used for age of majority?[J]. J Forensic Leg Med，2021，77：102095. doi：10.1016/j.jflm.2020.102095 .
32	TERADA Y， KONO S， UCHIUMI T， et al. Improved reliability in skeletal age assessment using a pediatric hand MR scanner with a 0.3T permanent magnet[J]. Magn Reson Med Sci，2014，13（3）：215-219. doi：10.2463/mrms.2013-0098 .
33	NEUMAYER B， LESCH A， THALER F， et al. The four-minute approach revisited： Accelerating MRI-based multi-factorial age estimation[J]. Int J Legal Med，2020，134（4）：1475-1485. doi：10.1007/s00414-019-02231-w .
34	JOPP E， SCHRÖDER I， MAAS R， et al. Proximale tibiaepiphyse im magnetresonanztomogramm[J]. Rechtsmedizin，2010，20（6）：464-468. doi：10.1007/s00194-010-0705-1 .
35	KRÄMER J A， SCHMIDT S， JÜRGENS K U， et al. Forensic age estimation in living individuals using 3.0 T MRI of the distal femur[J]. Int J Legal Med，2014，128（3）：509-514. doi：10.1007/s00414-014-0967-3 .
36	KELLINGHAUS M， SCHULZ R， VIETH V， et al. Enhanced possibilities to make statements on the ossification status of the medial clavicular epiphysis using an amplified staging scheme in evaluating thin-slice CT scans[J]. Int J Legal Med，2010，124（4）：321-325. doi：10.1007/s00414-010-04 48-2 .
37	DEDOUIT F， AURIOL J， ROUSSEAU H， et al. Age assessment by magnetic resonance imaging of the knee： A preliminary study[J]. Forensic Sci Int，2012，217（1/2/3）：232.e1-232.e7. doi：10.1016/j.forsciint.2011.11.013 .
38	VIETH V， SCHULZ R， HEINDEL W， et al. Forensic age assessment by 3.0T MRI of the knee： Proposal of a new MRI classification of ossification stages[J]. Eur Radiol，2018，28（8）：3255-3262. doi：10.1007/s00330-017-5281-2 .
39	THAPA M M， IYER R S， KHANNA P C， et al. MRI of pediatric patients： Part 1， normal and abnormal cartilage[J]. AJR Am J Roentgenol，2012，198（5）： W450-W455. doi：10.2214/ajr.10.7280 .
40	EKIZOGLU O， HOCAOGLU E， INCI E， et al. Forensic age estimation via 3-T magnetic resonance imaging of ossification of the proximal tibial and distal femoral epiphyses： Use of a T2-weighted fast spin-echo technique[J]. Forensic Sci Int，2016，260：102.e1-102.e7. doi：10.1016/j.forsciint.2015.12.006 .
41	SIEGALL E， FAUST J R， HERZOG M M， et al. Age predicts disruption of the articular surface of the femoral condyles in knee OCD： Can we reduce usage of arthroscopy and MRI?[J]. J Pediatr Orthop，2018，38（3）：176-180. doi：10.1097/bpo.000 0000000000796 .
42	OTTOW C， SCHULZ R， PFEIFFER H， et al. Forensic age estimation by magnetic resonance imaging of the knee： The definite relevance in bony fusion of the distal femoral- and the proximal tibial epiphyses using closest-to-bone T₁ TSE sequence[J]. Eur Radiol，2017，27（12）：5041-5048. doi：10.1007/s00330-017-4 880-2 .
43	KRÄMER J A， SCHMIDT S， JÜRGENS K U， et al. The use of magnetic resonance imaging to examine ossification of the proximal tibial epiphysis for forensic age estimation in living individuals[J]. Forensic Sci Med Pathol，2014，10（3）：306-313. doi：10.1007/s12024-014-9559-2 .
44	KIM H K， SHIRAJ S， ANTON C G， et al. Age and sex dependency of cartilage T₂ relaxation time mapping in MRI of children and adolescents[J]. AJR Am J Roentgenol，2014，202（3）：626-632. doi：10.2214/ajr.13.11327 .
45	DING C， CICUTTINI F， SCOTT F， et al. Association between age and knee structural change： A cross sectional MRI based study[J]. Ann Rheum Dis，2005，64（4）：549-555. doi：10.1136/ard.2004.023069 .
46	DER MAUER M AUF， SÄRING D， STANCZUS B， et al. A 2-year follow-up MRI study for the evaluation of an age estimation method based on knee bone development[J]. Int J Legal Med，2019，133（1）：205-215. doi：10.1007/s00414-018-1826-4 .
47	MUTASA S， CHANG P D， RUZAL-SHAPIRO C， et al. MABAL： a novel deep-learning architecture for machine-assisted bone age labeling[J]. J Digit Imaging，2018，31（4）：513-519. doi：10.1007/s10278-018-0053-3 .
48	LIU Y， ZHANG C， CHENG J， et al. A multi-scale data fusion framework for bone age assessment with convolutional neural networks[J]. Comput Biol Med，2019，108：161-173. doi：10.1016/j.compbiomed.2019.03.015 .
49	DALLORA A L， ANDERBERG P， KVIST O， et al. Bone age assessment with various machine learning techniques： A systematic literature review and meta-analysis[J]. PLoS One，2019，14（7）：e0220242. doi：10.1371/journal.pone.0220242 .
50	MAUER M A， WELL E J， HERRMANN J， et al. Automated age estimation of young individuals based on 3D knee MRI using deep learning[J]. Int J Legal Med，2021，135（2）：649-663. doi：10.1007/s00414-020-02465-z .
51	DALLORA A L， BERGLUND J S， BROGREN M， et al. Age assessment of youth and young adults using magnetic resonance imaging of the knee： A deep learning approach[J]. JMIR Med Inform，2019，7（4）：e16291. doi：10.2196/16291 .
52	DALLORA A L， KVIST O， BERGLUND J S， et al. Chronological age assessment in young individuals using bone age assessment staging and nonradiological aspects： Machine learning multifactorial approach[J]. JMIR Med Inform，2020，8（9）：e18846. doi：10.2196/18846 .

[1]	Wen LI, Hao-zhe LI, Chen CHEN, Wei-xiong CAI. Research Progress and Application Prospect of Facial Micro-Expression Analysis in Forensic Psychiatry [J]. Journal of Forensic Medicine, 2023, 39(5): 493-500.
[2]	Zhong-hua WANG, Shu-jin LI. Research Progress on Molecular Biology of Human Height Estimation [J]. Journal of Forensic Medicine, 2023, 39(5): 487-492.
[3]	Lu CHEN, Zhe ZHOU, Sheng-qi WANG. Process of Forensic Medicine in DNA Identification of Aged Human Remains [J]. Journal of Forensic Medicine, 2023, 39(5): 478-486.
[4]	Yong ZENG, Dong-hua ZOU, Ying FAN, Qing XU, Lu-yang TAO, Yi-jiu CHEN, Zheng-dong LI. Research Progress and Forensic Application of Human Vascular Finite Element Modeling and Biomechanics [J]. Journal of Forensic Medicine, 2023, 39(5): 471-477.
[5]	Zi-wei WANG, Cheng-tao LI, Xi-ling LIU. Cross-Platform Application of DNA Methylation Age Estimation Model in Eastern Chinese Han Population [J]. Journal of Forensic Medicine, 2023, 39(5): 441-446.
[6]	Fei FAN, Juan WU, Zhen-hua DENG. Application Progress of Objective Audiological Detection Techniques in Forensic Clinical Medicine [J]. Journal of Forensic Medicine, 2023, 39(4): 360-366.
[7]	Qing-qing XIANG, Li-fang CHEN, Qin SU, Yu-kun DU, Pei-yan LIANG, Xiao-dong KANG, He SHI, Qu-yi XU, Jian ZHAO, Chao LIU, Xiao-hui CHEN. Research Progress on Microbial Community Succession in the Postmortem Interval Estimation [J]. Journal of Forensic Medicine, 2023, 39(4): 399-405.
[8]	Yu-qi CAO, Yan SHI, Ping XIANG, Yin-long GUO. Research Progress on Machine Learning Assisted Non-Targeted Screening Strategy for Identification of Fentanyl Analogs [J]. Journal of Forensic Medicine, 2023, 39(4): 406-416.
[9]	Ran LI, Hong-yu SUN. Methods and Research Hotspots of Forensic Kinship Testing [J]. Journal of Forensic Medicine, 2023, 39(3): 231-239.
[10]	Xiao-yan MA, Hong-yu SUN, Qing LI. Research Progresses of Tri-Allelic Patterns in Autosomal STR in Forensic DNA Analysis [J]. Journal of Forensic Medicine, 2023, 39(3): 240-246.
[11]	Hang CHEN, Jing HU, Zheng QIAO, Hong-xiao DENG, Min LÜ, Wei LIU. Research Progress on Biological Matrix Reference Materials in Forensic Toxicology [J]. Journal of Forensic Medicine, 2023, 39(2): 176-185.
[12]	Hong-yan GAO, Guang-fu LIU, Jian WU, Peng-yu CHEN. Animal DNA Typing and Its Research Progress in Forensic Medicine [J]. Journal of Forensic Medicine, 2023, 39(2): 161-167.
[13]	Yong-gang MA, Yong-jie CAO, Yi-hua ZHAO, Xin-jun ZHOU, Bin HUANG, Gao-chao ZHANG, Ping HUANG, Ya-hui WANG, Kai-jun MA, Feng CHEN, Dong-chuan ZHANG, Ji ZHANG. Sex Estimation of Medial Aspect of the Ischiopubic Ramus in Adults Based on Deep Learning [J]. Journal of Forensic Medicine, 2023, 39(2): 129-136.
[14]	Zhong-ping CHENG, Yan-fei LIU, Xing-min XU, Yao-nan MO. Progress in the Application of Magnetic Nanoparticles in Forensic Trace Analysis [J]. Journal of Forensic Medicine, 2023, 39(2): 168-175.
[15]	Zi-wei WANG, Qian-nan XU, Cheng-tao LI, Xi-ling LIU. Age Estimation Based on DNA Methylation and Its Application Prospects in Forensic Medicine [J]. Journal of Forensic Medicine, 2023, 39(1): 72-82.