Dental Age Estimation in Northern Chinese Han Children and Adolescents Using Demirjian’s Method Combined with Machine Learning Algorithms

doi:10.12116/j.issn.1004-5619.2023.231208

Abstract

Abstract:

Objective To investigate the application value of combining the Demirjian’s method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents. Methods Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian’s method. Various machine learning algorithms, including support vector regression （SVR）, gradient boosting regression （GBR）, linear regression （LR）, random forest regression （RFR）, and decision tree regression （DTR） were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated. Results SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error （MAE） of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old. Conclusion The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered.

Key words: forensic anthropology, forensic dentistry, age estimation, machine learning, Demirjian’s method, oral panoramic image, children, adolescents

CLC Number:

Yu-xin GUO, Wen-qing BU, Yu TANG, Di WU, Hui YANG, Hao-tian MENG, Yu-cheng GUO. Dental Age Estimation in Northern Chinese Han Children and Adolescents Using Demirjian’s Method Combined with Machine Learning Algorithms[J]. Journal of Forensic Medicine, 2024, 40(2): 135-142.

Figures/Tables 7

Fig. 1 Schematic diagram of dental age estimation model construction

Fig. 2 Heatmap of the developmental scores for 8 permanent teeth in the left mandible

Tab. 1 The fitting effect and optimal parameter setting of different machine learning modelsin total， female and male samples

模型	总样本			女性样本			男性样本
模型	R²	MAE/岁	最佳参数	R²	MAE/岁	最佳参数	R²	MAE/岁	最佳参数
SVR	0.904 2	1.246 3	C=10，kernel=rbf	0.900 8	1.281 8	C=1，kernel=rbf	0.908 6	1.161 8	C=10，kernel=rbf
GBR	0.904 0	1.263 8	max_depth=3，n_estimators=100	0.901 1	1.292 2	max_depth=3，n_estimators=100	0.909 4	1.153 8	max_depth=3，n_estimators=50
RFR	0.900 7	1.297 2	max_depth=7，n_estimators=200	0.890 8	1.331 6	max_depth=7，n_estimators=200	0.905 5	1.192 0	max_depth=7，n_estimators=200
DTR	0.899 5	1.305 1	max_depth=5	0.890 5	1.354 3	max_depth=5	0.900 4	1.225 0	max_depth=5
LR	0.865 9	1.597 7	-	0.853 6	1.663 7	-	0.869 9	1.522 5	-

Fig. 3 The fitting results of the optimal age estimation model for total， female and male samples

Tab. 2 The prediction performance of the optimal age estimation model （SVR） for total samples

年龄区间/岁	例数	真实值/（ $x ¯$ ±s）	预测值/（ $x ¯$ ±s）	残差/（ $x ¯$ ±s）	MAE
5.00~5.99	501	5.49±0.03	5.82±0.07	-0.33±0.06	0.44
6.00~6.99	505	6.51±0.03	6.70±0.07	-0.19±0.07	0.53
7.00~7.99	508	7.54±0.03	7.73±0.08	-0.19±0.07	0.56
8.00~8.99	509	8.51±0.03	9.06±0.27	-0.55±0.27	1.17
9.00~9.99	516	9.47±0.03	9.59±0.07	-0.12±0.07	0.52
10.00~10.99	503	10.52±0.03	10.63±0.10	-0.12±0.09	0.72
11.00~11.99	518	11.51±0.03	12.11±0.21	-0.60±0.21	1.42
12.00~12.99	502	12.47±0.03	12.92±0.13	-0.45±0.13	1.10
13.00~13.99	512	13.49±0.03	13.78±0.13	-0.29±0.13	1.01
14.00~14.99	512	14.49±0.03	14.80±0.15	-0.31±0.14	1.16
15.00~15.99	510	15.53±0.03	15.58±0.15	-0.04±0.15	1.19
16.00~16.99	521	16.49±0.03	16.70±0.19	-0.22±0.19	1.69
17.00~17.99	538	17.57±0.03	18.39±0.21	-0.82±0.21	1.90
18.00~18.99	505	18.58±0.03	18.85±0.26	-0.27±0.26	2.00
19.00~19.99	509	19.54±0.03	20.14±0.23	-0.60±0.23	2.02
20.00~20.99	501	20.52±0.03	20.78±0.19	-0.26±0.19	1.50
21.00~21.99	546	21.45±0.03	21.16±0.15	0.30±0.15	1.27
22.00~22.99	501	22.48±0.03	21.76±0.13	0.73±0.13	0.77
23.00~23.99	533	23.49±0.03	21.77±0.12	1.72±0.12	1.72
24.00~24.99	506	24.46±0.03	22.06±0.07	2.40±0.08	2.40

Tab. 2 The prediction performance of the optimal age estimation model （SVR） for total samples

年龄区间/岁	例数	真实值/（ $x ¯$ ±s）	预测值/（ $x ¯$ ±s）	残差/（ $x ¯$ ±s）	MAE
5.00~5.99	501	5.49±0.03	5.82±0.07	-0.33±0.06	0.44
6.00~6.99	505	6.51±0.03	6.70±0.07	-0.19±0.07	0.53
7.00~7.99	508	7.54±0.03	7.73±0.08	-0.19±0.07	0.56
8.00~8.99	509	8.51±0.03	9.06±0.27	-0.55±0.27	1.17
9.00~9.99	516	9.47±0.03	9.59±0.07	-0.12±0.07	0.52
10.00~10.99	503	10.52±0.03	10.63±0.10	-0.12±0.09	0.72
11.00~11.99	518	11.51±0.03	12.11±0.21	-0.60±0.21	1.42
12.00~12.99	502	12.47±0.03	12.92±0.13	-0.45±0.13	1.10
13.00~13.99	512	13.49±0.03	13.78±0.13	-0.29±0.13	1.01
14.00~14.99	512	14.49±0.03	14.80±0.15	-0.31±0.14	1.16
15.00~15.99	510	15.53±0.03	15.58±0.15	-0.04±0.15	1.19
16.00~16.99	521	16.49±0.03	16.70±0.19	-0.22±0.19	1.69
17.00~17.99	538	17.57±0.03	18.39±0.21	-0.82±0.21	1.90
18.00~18.99	505	18.58±0.03	18.85±0.26	-0.27±0.26	2.00
19.00~19.99	509	19.54±0.03	20.14±0.23	-0.60±0.23	2.02
20.00~20.99	501	20.52±0.03	20.78±0.19	-0.26±0.19	1.50
21.00~21.99	546	21.45±0.03	21.16±0.15	0.30±0.15	1.27
22.00~22.99	501	22.48±0.03	21.76±0.13	0.73±0.13	0.77
23.00~23.99	533	23.49±0.03	21.77±0.12	1.72±0.12	1.72
24.00~24.99	506	24.46±0.03	22.06±0.07	2.40±0.08	2.40

Tab. 3 The prediction performance of the optimal age estimation model （SVR） for female samples

年龄区间/岁	例数	真实值/（ $x ¯$ ±s）	预测值/（ $x ¯$ ±s）	残差/（ $x ¯$ ±s）	MAE
5.00~5.99	225	5.52±0.04	5.73±0.07	-0.21±0.07	0.38
6.00~6.99	222	6.43±0.05	6.59±0.10	-0.16±0.09	0.44
7.00~7.99	205	7.47±0.05	7.61±0.12	-0.14±0.12	0.68
8.00~8.99	162	8.55±0.05	8.94±0.48	-0.40±0.47	0.78
9.00~9.99	232	9.47±0.04	9.50±0.09	-0.03±0.08	0.47
10.00~10.99	237	10.50±0.04	11.01±0.14	-0.50±0.13	0.89
11.00~11.99	295	11.55±0.04	12.35±0.23	-0.80±0.22	1.29
12.00~12.99	273	12.43±0.03	13.30±0.16	-0.87±0.17	1.31
13.00~13.99	299	13.54±0.04	14.00±0.22	-0.46±0.21	1.20
14.00~14.99	304	14.44±0.04	14.69±0.19	-0.25±0.20	1.12
15.00~15.99	297	15.46±0.04	16.35±0.21	-0.89±0.21	1.49
16.00~16.99	286	16.48±0.04	16.69±0.25	-0.21±0.25	1.56
17.00~17.99	303	17.53±0.04	18.16±0.29	-0.64±0.28	1.87
18.00~18.99	310	18.53±0.04	18.18±0.36	0.34±0.35	2.07
19.00~19.99	329	19.49±0.04	20.44±0.27	-0.95±0.27	1.85
20.00~20.99	331	20.50±0.04	20.64±0.27	-0.14±0.27	1.51
21.00~21.99	353	21.50±0.04	21.33±0.16	0.16±0.16	1.19
22.00~22.99	335	22.47±0.04	22.05±0.11	0.43±0.11	0.56
23.00~23.99	346	23.45±0.04	21.98±0.11	1.47±0.12	1.47
24.00~24.99	334	24.51±0.03	22.10±0.10	2.41±0.10	2.41

Tab. 3 The prediction performance of the optimal age estimation model （SVR） for female samples

年龄区间/岁	例数	真实值/（ $x ¯$ ±s）	预测值/（ $x ¯$ ±s）	残差/（ $x ¯$ ±s）	MAE
5.00~5.99	225	5.52±0.04	5.73±0.07	-0.21±0.07	0.38
6.00~6.99	222	6.43±0.05	6.59±0.10	-0.16±0.09	0.44
7.00~7.99	205	7.47±0.05	7.61±0.12	-0.14±0.12	0.68
8.00~8.99	162	8.55±0.05	8.94±0.48	-0.40±0.47	0.78
9.00~9.99	232	9.47±0.04	9.50±0.09	-0.03±0.08	0.47
10.00~10.99	237	10.50±0.04	11.01±0.14	-0.50±0.13	0.89
11.00~11.99	295	11.55±0.04	12.35±0.23	-0.80±0.22	1.29
12.00~12.99	273	12.43±0.03	13.30±0.16	-0.87±0.17	1.31
13.00~13.99	299	13.54±0.04	14.00±0.22	-0.46±0.21	1.20
14.00~14.99	304	14.44±0.04	14.69±0.19	-0.25±0.20	1.12
15.00~15.99	297	15.46±0.04	16.35±0.21	-0.89±0.21	1.49
16.00~16.99	286	16.48±0.04	16.69±0.25	-0.21±0.25	1.56
17.00~17.99	303	17.53±0.04	18.16±0.29	-0.64±0.28	1.87
18.00~18.99	310	18.53±0.04	18.18±0.36	0.34±0.35	2.07
19.00~19.99	329	19.49±0.04	20.44±0.27	-0.95±0.27	1.85
20.00~20.99	331	20.50±0.04	20.64±0.27	-0.14±0.27	1.51
21.00~21.99	353	21.50±0.04	21.33±0.16	0.16±0.16	1.19
22.00~22.99	335	22.47±0.04	22.05±0.11	0.43±0.11	0.56
23.00~23.99	346	23.45±0.04	21.98±0.11	1.47±0.12	1.47
24.00~24.99	334	24.51±0.03	22.10±0.10	2.41±0.10	2.41

Tab. 4 The prediction performance of the optimal age estimation model （SVR） for male samples

年龄区间/岁	例数	真实值/（ $x ¯$ ±s）	预测值/（ $x ¯$ ±s）	残差/（ $x ¯$ ±s）	MAE
5.00~5.99	276	5.54±0.04	5.85±0.11	-0.32±0.11	0.45
6.00~6.99	283	6.50±0.04	6.79±0.09	-0.29±0.09	0.53
7.00~7.99	303	7.47±0.04	7.76±0.08	-0.28±0.07	0.53
8.00~8.99	347	8.48±0.03	9.24±0.34	-0.76±0.34	1.19
9.00~9.99	284	9.47±0.04	9.79±0.13	-0.32±0.13	0.76
10.00~10.99	266	10.46±0.04	10.91±0.12	-0.45±0.11	0.76
11.00~11.99	223	11.50±0.05	11.71±0.26	-0.21±0.24	1.14
12.00~12.99	229	12.40±0.04	12.54±0.16	-0.14±0.16	0.78
13.00~13.99	213	13.45±0.04	14.02±0.19	-0.57±0.18	1.09
14.00~14.99	208	14.50±0.04	15.18±0.21	-0.69±0.21	1.16
15.00~15.99	213	15.53±0.05	15.65±0.17	-0.12±0.17	0.90
16.00~16.99	235	16.53±0.04	17.09±0.28	-0.56±0.27	1.39
17.00~17.99	235	17.46±0.04	17.84±0.32	-0.38±0.32	1.76
18.00~18.99	195	18.49±0.05	18.25±0.37	0.24±0.36	1.80
19.00~19.99	180	19.50±0.05	20.09±0.33	-0.59±0.34	1.85
20.00~20.99	170	20.53±0.06	21.16±0.22	-0.64±0.24	1.42
21.00~21.99	193	21.52±0.05	21.30±0.17	0.23±0.17	0.65
22.00~22.99	166	22.40±0.05	21.23±0.24	1.17±0.25	1.17
23.00~23.99	187	23.54±0.05	21.19±0.18	2.35±0.18	2.35
24.00~24.99	172	24.48±0.05	21.52±0.16	2.96±0.18	2.96

Tab. 4 The prediction performance of the optimal age estimation model （SVR） for male samples

年龄区间/岁	例数	真实值/（ $x ¯$ ±s）	预测值/（ $x ¯$ ±s）	残差/（ $x ¯$ ±s）	MAE
5.00~5.99	276	5.54±0.04	5.85±0.11	-0.32±0.11	0.45
6.00~6.99	283	6.50±0.04	6.79±0.09	-0.29±0.09	0.53
7.00~7.99	303	7.47±0.04	7.76±0.08	-0.28±0.07	0.53
8.00~8.99	347	8.48±0.03	9.24±0.34	-0.76±0.34	1.19
9.00~9.99	284	9.47±0.04	9.79±0.13	-0.32±0.13	0.76
10.00~10.99	266	10.46±0.04	10.91±0.12	-0.45±0.11	0.76
11.00~11.99	223	11.50±0.05	11.71±0.26	-0.21±0.24	1.14
12.00~12.99	229	12.40±0.04	12.54±0.16	-0.14±0.16	0.78
13.00~13.99	213	13.45±0.04	14.02±0.19	-0.57±0.18	1.09
14.00~14.99	208	14.50±0.04	15.18±0.21	-0.69±0.21	1.16
15.00~15.99	213	15.53±0.05	15.65±0.17	-0.12±0.17	0.90
16.00~16.99	235	16.53±0.04	17.09±0.28	-0.56±0.27	1.39
17.00~17.99	235	17.46±0.04	17.84±0.32	-0.38±0.32	1.76
18.00~18.99	195	18.49±0.05	18.25±0.37	0.24±0.36	1.80
19.00~19.99	180	19.50±0.05	20.09±0.33	-0.59±0.34	1.85
20.00~20.99	170	20.53±0.06	21.16±0.22	-0.64±0.24	1.42
21.00~21.99	193	21.52±0.05	21.30±0.17	0.23±0.17	0.65
22.00~22.99	166	22.40±0.05	21.23±0.24	1.17±0.25	1.17
23.00~23.99	187	23.54±0.05	21.19±0.18	2.35±0.18	2.35
24.00~24.99	172	24.48±0.05	21.52±0.16	2.96±0.18	2.96

References 20

1	SCHMELING A， GRUNDMANN C， FUHRMANN A， et al. Criteria for age estimation in living individuals[J]. Int J Legal Med，2008，122（6）：457-460. doi：10.1007/s00414-008-0254-2 .
2	BAGHERIAN A， SADEGHI M. Assessment of dental maturity of children aged 3.5 to 13.5 years using the Demirjian method in an Iranian population[J]. J Oral Sci，2011，53（1）：37-42. doi：10.2334/josnusd.53.37 .
3	KUMARESAN R， CUGATI N， CHANDRASEKA-RAN B， et al. Reliability and validity of five radiographic dental-age estimation methods in a population of Malaysian children[J]. J Investig Clin Dent，2016，7（1）：102-109. doi：10.1111/jicd.12116 .
4	KUREMOTO K， OKAWA R， MATAYOSHI S， et al. Estimation of dental age based on the developmental stages of permanent teeth in Japanese children and adolescents[J]. Sci Rep，2022，12（1）：3345. doi：10.1038/s41598-022-07304-2 .
5	沈诗慧，陶疆. 基于牙齿不同发育阶段的年龄推断的研究进展[J].上海交通大学学报（医学版），2020，40（11）：1540-1543. doi：10.3969/j.issn.1674-8115.2020.11.019 .
	SHEN S H， TAO J. Research progress of age estimation based on different developmental stages of teeth[J]. Shanghai Jiaotong Daxue Xuebao （Medical science），2020，40（11）：1540-1543.
6	DEMIRJIAN A， GOLDSTEIN H， TANNER J M. A new system of dental age assessment[J]. Hum Biol，1973，45（2）：211-227.
7	VILA-BLANCO N， VARAS-QUINTANA P， TOMÁS I， et al. A systematic overview of dental methods for age assessment in living individuals： From traditional to artificial intelligence-based approaches[J]. Int J Legal Med，2023，137（4）：1117-1146. doi：10.1007/s00414-023-02960-z .
8	毛晓燕，霍德民，周子皓，等. 青少年第三磨牙发育的分期模式与年龄相关性研究[J].中国法医学杂志，2021，36（5）：493-497. doi：10.13618/j.issn.1001-5728.2021.05.012 .
	MAO X Y， HUO D M， ZHOU Z H， et al. The model of classification of third molars development and its correlation with chronological age[J]. Zhongguo Fayixue Zazhi，2021，36（5）：493-497.
9	SUBEDI N， PARAJULI U， PAUDEL I S， et al. Demirjian’s eight teeth method for dental age estimation in Nepalese population[J]. J Nepal Health Res Counc，2021，18（4）：686-691. doi：10.33314/jnhrc.v18i4.3119 .
10	中华人民共和国公安部. 法庭科学汉族青少年骨龄鉴定技术规程： [S].北京：中国标准出版社，2019.
	Ministry of Public Security of the People’s Republic of China. Forensic sciences — Technical specifications for skeletal age evaluation of Han population teenagers： [S]. Beijing： Standards Press of China，2019.
11	AL-EMRAN S. Dental age assessment of 8.5 to 17 year-old Saudi children using Demirjian’s method[J]. J Contemp Dent Pract，2008，9（3）：64-71.
12	CAVRIĆ J， VODANOVIĆ M， MARUŠIĆ A， et al. Time of mineralization of permanent teeth in children and adolescents in Gaborone， Botswana[J]. Ann Anat，2016，203：24-32. doi：10.1016/j.aanat.2015.08.001 .
13	MABER M， LIVERSIDGE H M， HECTOR M P. Accuracy of age estimation of radiographic methods using developing teeth[J]. Forensic Sci Int，2006，159（S1）：68-73. doi：10.1016/j.forsciint.2006.02.019 .
14	MELO M， ATA-ALI J. Accuracy of the estimation of dental age in comparison with chronological age in a Spanish sample of 2 641 living subjects using the Demirjian and Nolla methods[J]. Forensic Sci Int，2017，270：276.e1-276.e7. doi：10.1016/j.forsciint.2016.10.001 .
15	OZVEREN N， SERINDERE G. Comparison of the applicability of Demirjian and Willems methods for dental age estimation in children from the Thrace region， Turkey[J]. Forensic Sci Int，2018，285：38-43. doi：10.1016/j.forsciint.2018.01.017 .
16	NEMSI H， DAYA M BEN， SALEM N H， et al. Applicability of Willems methods and Demirjian’s four teeth method for dental age estimation： Cross sectional study on Tunisian sub-adults[J]. Forensic Sci Int，2018，291：281.e1-281.e9. doi：10.1016/j.forsciint.2018.08.007 .
17	兰玲梅，杨泽登，孙淑乐，等. Demirjian和Cameriere法在8～16岁湖南汉族青少年牙龄推断中的应用[J].法医学杂志，2019，35（4）：406-410. doi：10.12116/j.issn.1004-5619.2019.04.005 .
	LAN L M， YANG Z D， SUN S L， et al. Application of Demirjian’s and Cameriere’s method in dental age estimation of 8-16 year old adolescents from Hunan Han nationality[J]. Fayixue Zazhi，2019，35（4）：406-410.
18	韩梦琪，储光，陈腾，等. 基于Demirjian法推断年龄的研究进展[J].法医学杂志，2019，35（6）：737-743. doi：10.12116/j.issn.1004-5619.2019.06.017 .
	HAN M Q， CHU G， CHEN T， et al. Research progress of age estimation based on the Demirjian’s method[J]. Fayixue Zazhi，2019，35（6）：737-743.
19	ZHAI Y， PARK H， HAN J， et al. Dental age assessment in a northern Chinese population[J]. J Forensic Leg Med，2016，38：43-49. doi：10.1016/j.jflm.2015.11.011 .
20	CHAILLET N， DEMIRJIAN A. Dental maturity in South France： A comparison between Demirjian’s method and polynomial functions[J]. J Forensic Sci，2004，49（5）：1059-1066.

[1]	Lei SHI, Ye XUE, Li-rong QIU, Ting LU, Fei FAN, Yu-chi ZHOU, Zhen-hua DENG. Research Progress on Dental Age Estimation Based on MRI Technology [J]. Journal of Forensic Medicine, 2024, 40(2): 112-117.
[2]	Xuan WEI, Yu-shan CHEN, Jie DING, Chang-xing SONG, Jun-jing WANG, Zhao PENG, Zhen-hua DENG, Xu YI, Fei FAN. Age Estimation by Machine Learning and CT-Multiplanar Reformation of Cranial Sutures in Northern Chinese Han Adults [J]. Journal of Forensic Medicine, 2024, 40(2): 128-134.
[3]	Dan-yang LI, Yu PAN, Hui-ming ZHOU, Lei WAN, Cheng-tao LI, Mao-wen WANG, Ya-hui WANG. Application of Medical Statistical and Machine Learning Methods in the Age Estimation of Living Individuals [J]. Journal of Forensic Medicine, 2024, 40(2): 118-127.
[4]	Yi-hang HUANG, Wei-bo LIANG, Hui JIAN, Sheng-qiu QU. Modeling Methods and Influencing Factors for Age Estimation Based on DNA Methylation [J]. Journal of Forensic Medicine, 2023, 39(6): 601-607.
[5]	Xing-yu MA, Hao CHENG, Zhong-duo ZHANG, Ye-ming LI, Dong ZHAO. Research Progress of Metabolomics Techniques Combined with Machine Learning Algorithm in Wound Age Estimation [J]. Journal of Forensic Medicine, 2023, 39(6): 596-600.
[6]	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.
[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]	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.
[10]	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.
[11]	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.
[12]	Xiao-tong YANG, Cheng-hui SUN, Yong-gang MA, Yong-jie CAO, Jian XIONG, Ji ZHANG, Ping HUANG. Sex Estimation of Han Adults in Western China Based on Three-Dimensional Cranial CT Reconstruction [J]. Journal of Forensic Medicine, 2023, 39(1): 27-33.
[13]	Jing-jing MA, Min SHANG, Da-wei QIN, Wen-zhao REN, Jia-yin SHI, Xiao-yun ZHAO, Ze-wei YAO, Fan LI. Relationship between the Height of Alveolar Bone Resorption and Sex and Age in Adolescents [J]. Journal of Forensic Medicine, 2022, 38(5): 589-594.
[14]	Yan-ru XI, Yuan-xin LIU, Na FENG, Zhen GU, Jun-hong SUN, Jie CAO, Qian-qian JIN, Qiu-xiang DU. Application of Linear Regression Model of Gpnmb Gene in Rat Injury Time Estimation [J]. Journal of Forensic Medicine, 2022, 38(4): 468-472.
[15]	Dong-chuan ZHANG, Geng FEI, Ting-ting CHEN, Lu-yi XU, De-lun YU, Tian-ye ZHANG. Comparison of Three Methods for Skeleton Age Estimation [J]. Journal of Forensic Medicine, 2022, 38(3): 319-323.