Prokop-Piotrkowska M, Marszałek-Dziuba K, Moszczyńska E, Szalecki M, Jurkiewicz E (2021) Traditional and new methods of bone age assessment-an overview. J Clin Res Pediatr Endocrinol 13:251–262. https://doi.org/10.4274/jcrpe.galenos.2020.2020.0091
Article PubMed PubMed Central Google Scholar
Santos C, Ferreira M, Alves FC, Cunha E (2011) Comparative study of Greulich and Pyle Atlas and Maturos 4.0 program for age estimation in a Portuguese sample. Forensic Sci Int 212:276.e1-276.e7. https://doi.org/10.1016/j.forsciint.2011.05.032
Cavallo F, Mohn A, Chiarelli F, Giannini C (2021) Evaluation of bone age in children: a mini-review. Front Pediatr 9. https://doi.org/10.3389/fped.2021.580314
Martin DD, Wit JM, Hochberg Z, Sävendahl L, van Rijn RR, Fricke O, Cameron N, Caliebe J, Hertel T, Kiepe D, Albertsson-Wikland K, Thodberg HH, Binder G, Ranke MB (2011) The use of bone age in clinical practice – part 1. Horm Res Paediatr 76:1–9. https://doi.org/10.1159/000329372
Article CAS PubMed Google Scholar
Greulich WW, Pyle SI (1959) Radiographic atlas of skeletal development of the hand and wrist, 2nd edn. Stanford University Press
Tanner JM, Healy MJR, Cameron N, Goldstein H (2001) Assessment of skeletal maturity and prediction of adult height (TW3 method), 3rd edn. W.B. Saunders
De Sanctis V, Di Maio S, Soliman A, Raiola G, Elalaily R, Millimaggi G (2014) Hand X-ray in pediatric endocrinology: skeletal age assessment and beyond. Indian J Endocrinol Metab 18:63. https://doi.org/10.4103/2230-8210.145076
Gao C, Qian Q, Li Y, Xing X, He X, Lin M, Ding Z (2022) A comparative study of three bone age assessment methods on Chinese preschool-aged children. Front Pediatr 10. https://doi.org/10.3389/fped.2022.976565
Dimitri P, Savage MO (2024) Artificial intelligence in paediatric endocrinology: conflict or cooperation. J Pediatr Endocrinol Metab 37:209–221. https://doi.org/10.1515/jpem-2023-0554
Thodberg HH, Kreiborg S, Juul A, Pedersen KD (2009) The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging 28:52–66. https://doi.org/10.1109/TMI.2008.926067
Maratova K, Zemkova D, Sedlak P, Pavlikova M, Amaratunga SA, Krasnicanova H, Soucek O, Sumnik Z (2023) A comprehensive validation study of the latest version of BoneXpert on a large cohort of Caucasian children and adolescents. Front Endocrinol (Lausanne) 14. https://doi.org/10.3389/fendo.2023.1130580
Martin DD, Calder AD, Ranke MB, Binder G, Thodberg HH (2022) Accuracy and self-validation of automated bone age determination. Sci Rep 12:6388. https://doi.org/10.1038/s41598-022-10292-y
Article CAS PubMed PubMed Central Google Scholar
Thodberg HH, Sävendahl L (2010) Validation and reference values of automated bone age determination for four ethnicities. Acad Radiol 17:1425–1432. https://doi.org/10.1016/j.acra.2010.06.007
Liang Y, Chen X, Zheng R, Cheng X, Su Z, Wang X, Du H, Zhu M, Li G, Zhong Y, Cheng S, Yu B, Yang Y, Chen R, Cui L, Yao H, Gu Q, Gong C, Jun Z, Huang X, Liu D, Yan X, Wei H, Li Y, Zhang H, Liu Y, Wang F, Zhang G, Fan X, Dai H, Luo X (2024) Validation of an AI-powered automated X-ray bone age analyzer in chinese children and adolescents: a comparison with the Tanner-Whitehouse 3 method. Adv Ther 41:3664–3677. https://doi.org/10.1007/s12325-024-02944-4
Liu J, Qi J, Liu Z, Ning Q, Luo X (2008) Automatic bone age assessment based on intelligent algorithms and comparison with TW3 method. Comput Med Imaging Graph 32:678–684. https://doi.org/10.1016/j.compmedimag.2008.08.005
Zhou X-L, Wang E-G, Lin Q, Dong G-P, Wu W, Huang K, Lai C, Yu G, Zhou H-C, Ma X-H, Jia X, Shi L, Zheng Y-S, Liu L-X, Ha D, Ni H, Yang J, Fu J-F (2020) Diagnostic performance of convolutional neural network-based Tanner-Whitehouse 3 bone age assessment system. Quant Imaging Med Surg 10:657–667. https://doi.org/10.21037/qims.2020.02.20
Article PubMed PubMed Central Google Scholar
Liu Y, Ouyang L, Wu W, Zhou X, Huang K, Wang Z, Song C, Chen Q, Su Z, Zheng R, Wei Y, Lu W, Wu W, Liu Y, Yan Z, Wu Z, Fan J, Zhou M, Fu J (2024) Validation of an established TW3 artificial intelligence bone age assessment system: a prospective, multicenter, confirmatory study. Quant Imaging Med Surg 14:144–159. https://doi.org/10.21037/qims-23-715
Wang X, Zhou B, Gong P, Zhang T, Mo Y, Tang J, Shi X, Wang J, Yuan X, Bai F, Wang L, Xu Q, Tian Y, Ha Q, Huang C, Yu Y, Wang L (2022) Artificial intelligence–assisted bone age assessment to improve the accuracy and consistency of physicians with different levels of experience. Front Pediatr 10. https://doi.org/10.3389/fped.2022.818061
Kim JR, Shim WH, Yoon HM, Hong SH, Lee JS, Cho YA, Kim S (2017) Computerized bone age estimation using deep learning based program: evaluation of the accuracy and efficiency. Am J Roentgenol 209:1374–1380. https://doi.org/10.2214/AJR.17.18224
Halabi SS, Prevedello LM, Kalpathy-Cramer J, Mamonov AB, Bilbily A, Cicero M, Pan I, Pereira LA, Sousa RT, Abdala N, Kitamura FC, Thodberg HH, Chen L, Shih G, Andriole K, Kohli MD, Erickson BJ, Flanders AE (2019) The RSNA pediatric bone age machine learning challenge. Radiology 290:498–503
R Core Team (2021) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Hsieh C-W, Jong T-L, Tiu C-M (2007) Bone age estimation based on phalanx information with fuzzy constrain of carpals. Med Biol Eng Comput 45:283–295. https://doi.org/10.1007/s11517-006-0155-9
Ahn K-S, Bae B, Jang WY, Lee JH, Oh S, Kim BH, Lee SW, Jung HW, Lee JW, Sung J, Jung K-H, Kang CH, Lee SH (2021) Assessment of rapidly advancing bone age during puberty on elbow radiographs using a deep neural network model. Eur Radiol 31:8947–8955. https://doi.org/10.1007/s00330-021-08096-1
Zhang A, Sayre JW, Vachon L, Liu BJ, Huang HK (2009) Racial differences in growth patterns of children assessed on the basis of bone age. Radiology 250:228–235. https://doi.org/10.1148/radiol.2493080468
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