1 |
郝虹霞,王亚辉,周智露,等. 膝关节MRI活体年龄推断研究进展[J].法医学杂志,2023,39(1):66-71,82. doi:10.12116/j.issn.1004-5619.2022.220503 .
|
|
HAO H X, WANG Y H, ZHOU Z L, et al. Research progress of age estimation in the living by knee joint MRI[J]. Fayixue Zazhi,2023,39(1):66-71,82.
|
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 |
HAZRA A, GOGTAY N. Biostatistics series module 1: Basics of biostatistics[J]. Indian J Dermatol,2016,61(1):10-20. doi:10.4103/0019-5154.173988 .
|
4 |
DEO R C. Machine learning in medicine[J]. Circulation,2015,132(20):1920-1930. doi:10.1161/CIRCU LATIONAHA.115.001593 .
|
5 |
万雷,应充亮,夏文涛,等. 海南、河南及浙江地区汉族男性青少年骨发育差异性分析[J].法医学杂志,2012,28(1):21-23,27. doi:10.3969/j.issn.1004-5619.2012.01.005 .
|
|
WAN L, YING C L, XIA W T, et al. Analysis of variation of Han male adolescent bone development in Hainan, Henan and Zhejiang provinces[J]. Fayixue Zazhi,2012,28(1):21-23,27.
|
6 |
王亚辉,魏华,应充亮,等. 锁骨胸骨端薄层CT扫描并图像重组的骨骺发育分级方法[J].法医学杂志,2013,29(3):168-171,179. doi:10.3969/j.issn.1004-5619.2013.03.003 .
|
|
WANG Y H, WEI H, YING C L, et al. The staging method of sternal end of clavicle epiphyseal growth by thin layer CT scan and imaging reconstruction[J]. Fayixue Zazhi,2013,29(3):168-171,179.
|
7 |
丁世荣,应充亮,万雷,等. 四川省阿坝地区藏族青少年膝关节骨发育趋势[J].法医学杂志,2013,29(4):244-247,251. doi:10.3969/j.issn.1004-5619.2013.04.002 .
|
|
DING S R, YING C L, WAN L, et al. Bone deve-lopment trend in the knee joint of Tibetan teenagers in Aba prefecture of Sichuan province[J]. Fayixue Za-zhi,2013,29(4):244-247,251.
|
8 |
WANG J, BAI X, WANG M, et al. Applicability and accuracy of Demirjian and Willems methods in a population of Eastern Chinese subadults[J]. Forensic Sci Int,2018,292:90-96. doi:10.1016/j.forsciint.2018.09.006 .
|
9 |
EKIZOGLU O, ER A, BOZDAG M, et al. Forensic age estimation based on fast spin-echo proton density (FSE PD)-weighted MRI of the distal radial epiphysis[J]. Int J Legal Med,2021,135(4):1611-1616. doi:10.1007/s00414-021-02505-2 .
|
10 |
MISHRA P, PANDEY C M, SINGH U, et al. Descriptive statistics and normality tests for statistical data[J]. Ann Card Anaesth,2019,22(1):67-72. doi:10.4103/aca.ACA_157_18 .
|
11 |
张华初,楚鹏飞,谢观霞. 统计分布和中心极限定理的随机模拟[J].统计与决策,2021,37(4):69-72. doi:10.13546/j.cnki.tjyjc.2021.04.015 .
|
|
ZHANG H C, CHU P F, XIE G X. Stochastic simulation of statistical distribution and central-limit theorem[J]. Tongji Yu Juece,2021,37(4):69-72.
|
12 |
MISHRA P, PANDEY C M, SINGH U,et al. Selection of appropriate statistical methods for data analysis[J]. Ann Card Anaesth,2019,22(3):297-301. doi:10.4103/aca.ACA_248_18 .
|
13 |
彭丽琴,万雷,汪茂文,等. 运用3种卷积神经网络模型对青少年骨盆骨龄评估的比较[J].法医学杂志,2020,36(5):622-630. doi:10.12116/j.issn.1004-5619.2020.05.004 .
|
|
PENG L Q, WAN L, WANG M W, et al. Comparison of three CNN models applied in bone age assessment of pelvic radiographs of adolescents[J]. Fayixue Zazhi,2020,36(5):622-630.
|
14 |
王妙辰,沈诗慧,白雪冰,等. 颈椎骨龄与牙龄推断上海地区儿童年龄的准确性比较[J].上海口腔医学,2022,31(1):89-95. doi:10.19439/j.sjos.2022.01.019 .
|
|
WANG M C, SHEN S H, BAI X B, et al. Comparison of the accuracy for evaluating cervical vertebral bone age and dental age of children in Shanghai[J]. Shanghai Kouqiang Yixue,2022,31(1):89-95.
|
15 |
SONG J W, HAAS A, CHUNG K C. Applications of statistical tests in hand surgery[J]. J Hand Surg Am,2009,34(10):1872-1881. doi:10.1016/j.jhsa.2009.08.001 .
|
16 |
GUO Y C, HAN M Q, CHI Y T, et al. Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images[J]. Int J Legal Med,2021,135(4):1589-1597. doi:10.1007/s00414-021-02542-x .
|
17 |
BEDEK I, DUMANČIĆ J, LAUC T, et al. Applicability of the Demirjian, Willems and Haavikko methods in Croatian children[J]. J Forensic Odontostomatol,2022,40(2):21-30.
|
18 |
SHAN W, SUN Y, HU L, et al. Boosting algorithm improves the accuracy of juvenile forensic dental age estimation in southern China population[J]. Sci Rep,2022,12(1):15649. doi:10.1038/s41598-022-20034-9 .
|
19 |
WIDEK T, DE TOBEL J, EHAMMER T, et al. Forensic age estimation in males by MRI based on the medial epiphysis of the clavicle[J]. Int J Legal Med,2023,137(3):679-689. doi:10.1007/s00414-022-02924-9 .
|
20 |
WANG M, FAN L, SHEN S, et al. Applicability of the third molar maturity index for assessment of age of majority in eastern China[J]. Leg Med (Tokyo),2019,41:101639. doi:10.1016/j.legalmed.2019.101639 .
|
21 |
ZHOU J, QU D, FAN L, et al. Applicability of the London Atlas method in the East China population[J]. Pediatr Radiol,2023,53(2):256-264. doi:10.1007/s00247-022-05491-8 .
|
22 |
HOJREH A, GAMPER J, SCHMOOK M T, et al. Hand MRI and the Greulich-Pyle atlas in skeletal age estimation in adolescents[J]. Skeletal Radiol,2018,47(7):963-971. doi:10.1007/s00256-017-2867-3 .
|
23 |
CHOI R Y, COYNER A S, KALPATHY-CRAMER J, et al. Introduction to machine learning, neural networks, and deep learning[J]. Transl Vis Sci Technol,2020,9(2):14. doi:10.1167/tvst.9.2.14 .
|
24 |
CHANDRASEKAR R, CHANDRASEKHAR S, SHANTHA SUNDARI K K, et al. Development and validation of a formula for objective assessment of cervical vertebral bone age[J]. Prog Orthod,2020,21(1):38. doi:10.1186/s40510-020-00338-0 .
|
25 |
LU T, SHI L, ZHAN M J, et al. Age estimation based on magnetic resonance imaging of the ankle joint in a modern Chinese Han population[J]. Int J Legal Med,2020,134(5):1843-1852. doi:10.1007/s00414-020-02364-3 .
|
26 |
王亚辉,朱广友,王鹏,等. 中国汉族女性青少年法医学活体骨龄推断数学模型的建立[J].法医学杂志,2008,24(2):110-113.
|
|
WANG Y H, ZHU G Y, WANG P, et al. Mathe-matical models of forensic bone age assessment of living subjects in Chinese Han female teenagers[J]. Fayixue Zazhi,2008,24(2):110-113.
|
27 |
王鹏,朱广友,王亚辉,等. 中国男性青少年骨龄鉴定方法[J].法医学杂志,2008,24(4):252-255,258.
|
|
WANG P, ZHU G Y, WANG Y H, et al. Assessment of skeletal age in Chinese male adolescents[J]. Fayixue Zazhi,2008,24(4):252-255,258.
|
28 |
WANG M, WANG J, PAN Y, et al. Applicability of newly derived second and third molar maturity indices for indicating the legal age of 16 years in the southern Chinese population[J]. Leg Med (Tokyo),2020,46:101725. doi:10.1016/j.legalmed.2020.101725 .
|
29 |
SHEN S, GUO Y, WANG M, et al. A quick method of determining the age of 8 years old: Based on the first premolars on eastern Chinese population[J]. Leg Med (Tokyo),2021,53:101950. doi:10.1016/j.legalmed.2021.101950 .
|
30 |
BI Q, GOODMAN K E, KAMINSKY J, et al. What is machine learning? A primer for the epidemiologist[J]. Am J Epidemiol,2019,188(12):2222-2239. doi:10.1093/aje/kwz189 .
|
31 |
RAUSCHERT S, RAUBENHEIMER K, MELTON P E, et al. Machine learning and clinical epigenetics: A review of challenges for diagnosis and classification[J]. Clin Epigenetics,2020,12(1):51. doi:10.1186/s13148-020-00842-4 .
|
32 |
SHEN S, LIU Z, WANG J, et al. Machine learning assisted Cameriere method for dental age estimation[J]. BMC Oral Health,2021,21(1):641. doi:10.1186/s12903-021-01996-0 .
|
33 |
DOGAN A, BIRANT D. Machine learning and data mining in manufacturing[J]. Expert Syst Appl,2021,166:114060. doi:10.1016/j.eswa.2020.114060 .
|
34 |
FAWCETT T. An introduction to ROC analysis[J]. Pattern Recogn Lett,2006,27(8):861-874. doi:10 .
|
|
1016/j.patrec.2005.10.010.
|
35 |
SOKOLOVA M, LAPALME G. A systematic analysis of performance measures for classification tasks[J]. Inform Process Manag,2009,45(4):427-437. doi:10 .
|
|
1016/j.ipm.2009.03.002.
|
36 |
HUANG S, CAI N, PACHECO P P, et al. Applications of support vector machine (SVM) learning in cancer genomics[J]. Cancer Genomics Proteomics,2018,15(1):41-51. doi:10.21873/cgp.20063 .
|
37 |
王亚辉,王子慎,魏华,等. 基于支持向量机实现骨骺发育分级的自动化评估[J].法医学杂志,2014,30(6):422-426. doi:10.3969/j.issn.1004-5619.2014.06.005 .
|
|
WANG Y H, WANG Z S, WEI H, et al. Automated assessment of developmental levels of epi-physis by support vector machine[J]. Fayixue Zazhi,2014,30(6):422-426.
|
38 |
雷义洋,申玉姝,王亚辉,等. 基于主成分分析和支持向量机实现膝关节骨龄评估回归算法[J].法医学杂志,2019,35(2):194-199. doi:10.12116/j.issn.1004-5619.2019.02.012 .
|
|
LEI Y Y, SHEN Y S, WANG Y H, et al. Regression algorithm of bone age estimation of knee-joint based on principal component analysis and support vector machine[J]. Fayixue Zazhi,2019,35(2):194-199.
|
39 |
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 .
|
40 |
孟光磊,丛泽林,宋彬,等. 贝叶斯网络结构学习综述[J].北京航空航天大学学报. doi:10.13700/j.bh.1001-5965.2023.0445 .
|
|
MENG G L, CONG Z L, SONG B, et al. Review of Bayesian network structure learning[J]. Beijing Hangkong Hangtian Daxue Xuebao.
|
41 |
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 .
|
42 |
SHEN S, YUAN X, WANG J, et al. Evaluation of a machine learning algorithms for predicting the dental age of adolescent based on different preprocessing methods[J]. Front Public Health,2022,10:1068253. doi:10.3389/fpubh.2022.1068253 .
|
43 |
胡婷鸿,万雷,刘太昂,等. 深度学习在图像识别及骨龄评估中的优势及应用前景[J].法医学杂志,2017,33(6):629-634,639. doi:10.3969/j.issn.1004-5619.2017.06.013 .
|
|
HU T H, WAN L, LIU T A, et al. Advantages and application prospects of deep learning in image recognition and bone age assessment[J]. Fayixue Zazhi,2017,33(6):629-634,639.
|
44 |
HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Comput,2006,18(7):1527-1554. doi:10.1162/neco.2006.18.7.1527 .
|
45 |
LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature,2015,521(7553):436-444. doi:10.1038/nature14539 .
|
46 |
严春满,王铖. 卷积神经网络模型发展及应用[J].计算机科学与探索,2021,15(1):27-46. doi:10.3778/j.issn.1673-9418.2008016 .
|
|
YAN C M, WANG C. Development and application of convolutional neural network model[J]. Jisuanji Kexue Yu Tansuo,2021,15(1):27-46.
|
47 |
HALABI S S, PREVEDELLO L M, KALPATHY-CRAMER J,et al. The RSNA pediatric bone age machine learning challenge[J]. Radiology,2019,290(2):498-503. doi:10.1148/radiol.2018180736 .
|
48 |
SPAMPINATO C, PALAZZO S, GIORDANO D, et al. Deep learning for automated skeletal bone age assessment in X-ray images[J]. Med Image Anal,2017,36:41-51. doi:10.1016/j.media.2016.10.010 .
|
49 |
刘鸣谦,兰钧,陈旭,等. 基于多维度特征融合的深度学习骨龄评估模型[J].第二军医大学学报,2018,39(8):909-916. doi:10.16781/j.0258-879x.2018.08.0909 .
|
|
LIU M Q, LAN J, CHEN X, et al. Bone age assessment model based on multi-dimensional feature fusion using deep learning[J]. Di-er Junyi Daxue Xuebao,2018,39(8):909-916.
|
50 |
YUAN L, ZHIZHONG H, XIAOAI D, et al. Forensic age estimation for pelvic X-ray images using deep learning[J]. European radiology,2019,29(5):2322-2329.
|
51 |
KIM S, LEE Y H, NOH Y K, et al. Age-group determination of living individuals using first molar images based on artificial intelligence[J]. Sci Rep,2021,11(1):1073. doi:10.1038/s41598-020-80182-8 .
|
52 |
VILA-BLANCO N, VARAS-QUINTANA P, ANEIROS-ARDAO Á, et al. XAS: Automatic yet eXplainable Age and Sex determination by combining imprecise per-tooth predictions[J]. Comput Biol Med,2022,149:106072. doi:10.1016/j.compbiomed.2022.106072 .
|
53 |
WANG J, DOU J, HAN J, et al. A population-based study to assess two convolutional neural networks for dental age estimation[J]. BMC Oral Health,2023,23(1):109. doi:10.1186/s12903-023-02817-2 .
|