Determination of bone age based on hand radiography: from classical methods to artificial intelligence (a review)

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Abstract

Bone age assessment methods are crucial in diagnosing diseases associated with growth and developmental disorders, especially in pediatric practice. These methods have advantages and limitations, and their accuracy may vary depending on population-specific characteristics.

This article outlines the current state and potential of bone age assessment methods, including solutions based on artificial intelligence technologies.

Scientific data on bone age assessment over the past 10 years were explored using PubMed and eLibrary. Earlier publications that serve as reference points in the development of bone age assessment methodology—such as atlases, guidelines, and relevant studies—were included. Publications addressing the prevalence and practical use of various bone age assessment techniques, including radiography, ultrasound, computed tomography, magnetic resonance imaging, and artificial intelligence, were prioritized. The search was performed using the following keywords: bone age, bone age assessment, radiography, artificial intelligence, deep learning, growth development, AI, костный возраст (bone age), рентгенография (radiography), and искусственный интеллект (artificial intelligence).

This review demonstrates the wide range of existing bone age assessment methods and emphasizes the importance of new technologies such as artificial intelligence in improving diagnostic accuracy. Modern automated techniques show potential for optimizing diagnostic workflows in pediatric care and contribute to the early detection of growth and developmental disorders.

About the authors

Dmitry N. Reznikov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Author for correspondence.
Email: reznik.m.d@mail.ru
ORCID iD: 0009-0004-8730-883X
SPIN-code: 9305-7875

MD

Russian Federation, 24 Petrovka st. bld. 1, Moscow, 127051

Dmitriy V. Kuligovskiy

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: rock_100@mail.ru
ORCID iD: 0009-0000-9824-6073
SPIN-code: 2821-5979
Russian Federation, Moscow

Inna G. Vorontsova

Russian Children's Clinical Hospital — branch of the Russian National Research Medical University named after N.I. Pirogov

Email: vorontsova-inna@mail.ru
ORCID iD: 0000-0001-5657-9371
SPIN-code: 7829-5461
Russian Federation, Moscow

Alexey V. Petraikin

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: alexeypetraikin@gmail.com
ORCID iD: 0000-0003-1694-4682
SPIN-code: 6193-1656

MD, Dr. Sci. (Medicine)

Russian Federation, Moscow

Elena E. Petryaykina

Russian Children's Clinical Hospital — branch of the Russian National Research Medical University named after N.I. Pirogov; Morozov Children's Municipal Clinical Hospital; The Russian National Research Medical University named after N.I. Pirogov

Email: lepet_morozko@mail.ru
ORCID iD: 0000-0002-8520-2378
SPIN-code: 5997-7464

MD, Dr. Sci. (Medicine), Professor

Russian Federation, Moscow; Moscow; Moscow

Alexander E. Gordeev

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies; Sechenov First Moscow State Medical University (Sechenov University)

Email: almanelis.dev@gmail.com
ORCID iD: 0009-0007-8537-8991
Russian Federation, Moscow; Moscow

Maria D. Varyukhina

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: VaryukhinaMD@zdrav.mos.ru
ORCID iD: 0000-0001-8870-7649
SPIN-code: 7463-4645

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Rustam A. Erizhokov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies; Sechenov First Moscow State Medical University (Sechenov University)

Email: npcmr@zdrav.mos.ru
ORCID iD: 0009-0007-3636-2889
SPIN-code: 2274-6428

MD

Russian Federation, Moscow; Moscow

Olga V. Omelyanskaya

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: OmelyanskayaOV@zdrav.mos.ru
ORCID iD: 0000-0002-0245-4431
SPIN-code: 8948-6152
Russian Federation, Moscow

Anton V. Vladzymyrskyy

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: VladzimirskijAV@zdrav.mos.ru
ORCID iD: 0000-0002-2990-7736
SPIN-code: 3602-7120

MD, Dr. Sci. (Medicine)

Russian Federation, Moscow

References

  1. Gilsanz V, Ratib O. Hand bone age: a digital atlas of skeletal maturity. Heidelberg: Springer; 2005. ISBN: 978-3-540-27070-6 doi: 10.1007/b138568
  2. Melmed S, Auchus RJ, Goldfine AB, et al. Williams textbook of endocrinology, 15th ed. Elsevier; 2024. ISBN: 978-032-393-347-6 [cited 2024 Jul 9]. Available from: https://shop.elsevier.com/books/williams-textbook-of-endocrinology/melmed/978-0-323-93230-1
  3. Petrov SS, Rogacheva EA, Taranukha NN. Assessment of the state of biological age in adolescent male and female individuals. Eurasian Union of Scientists. 2015;(3-7):50–55. (In Russ.) EDN: XDYHIT
  4. Grossman AB, Ismailov SI, Kulmirzayeva MG, et al. Constitutional delay of growth and puberty in boys: review. International Journal of Endocrinology. 2019;15(5):402–409. doi: 10.22141/2224-0721.15.5.2019.180045
  5. Otto NY, Bezrukova DA, Dzhumagaziev AA, et al. Clinical cases of delayed growth in children and adolescents of the astrakhan region. Journal of Volgograd State Medical University. 2021;18(1):144–149. doi: 10.19163/1994-9480-2021-1(77)-144-149 EDN: CGDPTR
  6. Wagner UA, Diedrich V, Schmitt O. Determination of skeletal maturity by ultrasound: a preliminary report. Skeletal Radiology. 1995;24(6):417–420. doi: 10.1007/bf00941236 EDN: RGDKXG
  7. Melmed S, Polonski K S, Larsen PR, Kronenberg GM. Pediatric endocrinology: Williams textbook of endocrinology. Dedov II, Melnichenko GA, editors. Moscow: GEOTAR-Media; 2020. ISBN: 978-5-9704-4951-6 [cited 2024 Jul 9]. Available from: https://www.labirint.ru/books/738012/
  8. Liss VL, Skopodok YuL, Plotnikova EV, et al. Diagnostics and treatment of endocrine diseases in children and adolescents: a tutorial. Shabalov NP, editor. Moscow: MEDpress-inform; 2018. (In Russ.) ISBN: 978-5-00030-528-7 [cited 2024 Jul 9]. Available from: https://static-sl.insales.ru/files/1/5887/12326655/original/diag_lech_end_zab_det.pdf
  9. Dedov II, Peterkova VA, Bezlepkina OB, et al. Handbook of pediatric endocrinologist. 3rd ed. Moscow: Litterra; 2020. (In Russ.) ISBN: 978-5-4235-0339-0 EDN: ELAUWO
  10. Volevodz N.N. Federal clinical practice guidelines on the diagnostics and treatment of Shereshevsky-Turner syndrome. Problems of Endocrinology. 2014;60(4):65–76. doi: 10.14341/probl201460452-63 EDN: TGRUDJ
  11. Nagaeva EV, Shiryaeva TY, Peterkova VA, et al. Russian national consensus. Diagnostics and treatment of hypopituitarism in children and adolescences. Problems of Endocrinology. 2019;64(6):402–411. doi: 10.14341/probl10091 EDN: NXICGE
  12. Matveev RP, Bragina SV. Radiology in traumatology and orthopedics. Selected sections. Arkhangelsk: Northern State Medical University; 2018. (In Russ.) ISBN: 978-5-91702-295-6 EDN: VMGEUT
  13. Ivanov IaA, Mininkov DS, Gushchina DA, Yeltsin AG. Comparison of bone age assessment methods using a hand radiography in patients with active growth plate and anteromedial knee instability. Genij Ortopedii. 2024;30(1):67–75. doi: 10.18019/1028-4427-2024-30-1-67-75 EDN: VIJHBH
  14. Zinenko YuV, Kotelnikova IV. Some problems of the production of forensic medical examinations to establish the age of living persons. Dnevnik nauki. 2021;(5):71. EDN: BAVWRU
  15. Davydov VYu, Shantarovich VV, Zhuravskii AYu. Morphofunctional criteria for selection and control in rowing and canoeing: methodological recommendations. Pinsk: Polessky State University; 2015. ISBN: 978-985-516-417-4 EDN: YXBJDJ
  16. Baranaev YuA. Assessment methods of biological maturity of children in sport science. Uchenye zapiski universiteta imeni P.F. Lesgafta. 2022;(8):12–20. doi: 10.34835/issn.2308-1961.2022.8.p12-20 EDN: MMATDY
  17. Prokop-Piotrkowska M, Marszałek-Dziuba K, Moszczyńska E, et al. Traditional and new methods of bone age assessment-an overview. Journal of Clinical Research in Pediatric Endocrinology. 2021;13(3):251–262. doi: 10.4274/jcrpe.galenos.2020.2020.0091 EDN: IHXXYW
  18. Watanabe S, Terazawa K, Matoba K. Age estimation from quantitative evaluation of atherosclerosis of abdominal aorta in Japanese. Hokkaido Igaku Zasshi. 2007;82(2):91–98.
  19. Pickhardt PJ, Kattan MW, Lee MH, et al. Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity. Nature Communications. 2025;16(1):1432. doi: 10.1038/s41467-025-56741-w EDN: OFZAXI
  20. Keylock L, Cameron N. Reproducibility of bone age assessment from DXA hand scans: expert versus novice. Annals of Human Biology. 2021;48(4):343–345. doi: 10.1080/03014460.2021.1956586 EDN: JKOVPN
  21. Pereira CP, Santos R, Nushi V, et al. Dental age assessment: scoring systems and models from the past until the present—how is it presented in the court? International Journal of Legal Medicine. 2023;137(5):1497–1504. doi: 10.1007/s00414-023-03011-3 EDN: KGOJFX
  22. Dang-Tran KD, Dedouit F, Joffre F, et al. Thyroid cartilage ossification and multislice computed tomography examination: a useful tool for age assessment? Journal of Forensic Sciences. 2010;55(3):677–683. doi: 10.1111/j.1556-4029.2010.01318.x EDN: NZJDGX
  23. Macedo F, Stefanel ME, Sakurada A, et al. Skull joints assessed via CT for age estimation-a systematic review. Dentomaxillofac Radiol. 2025. doi: 10.1093/dmfr/twaf013
  24. Martín Pérez SE, Martín Pérez IM, Vega González JM, et al. Precision and accuracy of radiological bone age assessment in children among different ethnic groups: a systematic review. Diagnostics (Basel). 2023;13(19):3124. doi: 10.3390/diagnostics13193124
  25. Santoro V, Marini C, Fuzio G, et al. A Comparison of 3 established skeletal age estimation methods in an african group from benin and an italian group from Southern Italy. American Journal of Forensic Medicine & Pathology. 2019;40(2):125–128. doi: 10.1097/PAF.0000000000000472
  26. Martin DD, Wit JM, Hochberg Z, et al. The use of bone age in clinical practice – part 2. Hormone Research in Paediatrics. 2011;76(1):10–16. doi: 10.1159/000329374 EDN: OZUDFH
  27. Huang S, Su Z, Liu S, et al. Combined assisted bone age assessment and adult height prediction methods in chinese girls with early puberty: analysis of three artificial intelligence systems. Pediatric Radiology. 2023;53(6):1108–1116. doi: 10.1007/s00247-022-05569-3
  28. Kim JR, Lee YS, Yu J. Assessment of bone age in prepubertal healthy korean children: comparison among the Korean standard bone age chart, greulich-pyle method, and tanner-whitehouse method. Korean Journal of Radiology. 2015;16(1):201–205. doi: 10.3348/kjr.2015.16.1.201
  29. Willems G. A review of the most commonly used dental age estimation techniques. The Journal of Forensic Odonto-Stomatology. 2001;19(1):9–17 [cited 2024 Jul 9]. Available from: https://ojs.iofos.eu/index.php/Journal/article/view/1725/329
  30. Szemraj A, Wojtaszek-Słomińska A, Racka-Pilszak B. Is the cervical vertebral maturation (CVM) method effective enough to replace the hand-wrist maturation (HWM) method in determining skeletal maturation?— A systematic review. European Journal of Radiology. 2018;102:125–128. doi: 10.1016/j.ejrad.2018.03.012
  31. Castriota-Scanderbeg A, De Micheli V. Ultrasound of femoral head cartilage: a new method of assessing bone age. Skeletal Radiology. 1995;24(3):197–200. doi: 10.1007/bf00228922 EDN: ISPXDV
  32. Alekseyeva LN, Kinzersky AYu. Detection of bone age in children using ultrasound method. Genij Ortopedii. 2012;(2):123–127. EDN: PBXJRJ
  33. Lo Re G, Zerbo S, Terranova MC, et al. Role of imaging in the assessment of age estimation. Seminars in Ultrasound, CT and MRI. 2019;40(1):51–55. doi: 10.1053/j.sult.2018.10.010
  34. Lopatin O, Barszcz M, Woźniak KJ. Skeletal and dental age estimation via postmortem computed tomography in Polish subadults group. International Journal of Legal Medicine. 2023;137(4):1147–1159. doi: 10.1007/s00414-023-03005-1 EDN: GMMXJM
  35. Terada Y, Kono S, Tamada D, et al. Skeletal age assessment in children using an open compact MRI system. Magnetic Resonance in Medicine. 2012;69(6):1697–1702. doi: 10.1002/mrm.24439
  36. Tomei E, Sartori A, Nissman D, et al. Value of MRI of the hand and the wrist in evaluation of bone age: Preliminary results. Journal of Magnetic Resonance Imaging. 2013;39(5):1198–1205. doi: 10.1002/jmri.24286
  37. Hojreh A, Gamper J, Schmook MT, et al. Hand MRI and the Greulich-Pyle atlas in skeletal age estimation in adolescents. Skeletal Radiology. 2018;47(7):963–971. doi: 10.1007/s00256-017-2867-3 EDN: JKRNLX
  38. Greulich WW, Pyle SI. Radiographic atlas of skeletal development of the hand and wrist. Stanford: Stanford University Press; 1959. ISBN: 978-080-470-398-7 doi: 10.1097/00000441-195909000-00030
  39. Zhukovskii MA. Pediatric endocrinology: a guide for physicians. 3rd ed. Moscow: Meditsina; 1995. (In Russ.) ISBN: 5-225-01167-5 [cited 2024 Jul 9]. Available from: https://www.libex.ru/detail/book251915.html
  40. De Sanctis V, Di Maio S, Soliman AT, et al. Hand X-ray in pediatric endocrinology: Skeletal age assessment and beyond. Indian Journal of Endocrinology and Metabolism. 2014;18(7):S63–S71. doi: 10.4103/2230-8210.145076 EDN: YEVLAU
  41. Tanner JM, Whitehouse RH, Cameron N, et al. Assessment of skeletal maturity and prediction of adult height (TW2 method). 2nd ed. London: Academic Press; 1975 ISBN: 978-012-683-350-8 [cited 2024 Jul 9]. Available from: https://a.co/d/a5XA81q
  42. Korolyuk I.P. X-ray anatomy atlas of the skeleton (norm, variants, interpretation errors). Moscow: Vidar; 1996. (In Russ.) ISBN: 5-88429-013-6 [cited 2024 Jul 9]. Available from: https://studfile.net/preview/16674356/
  43. Dedova II, Peterkovoi VA, editors. Federal clinical guidelines (protocols) for the management of children with endocrine diseases. Moscow: Praktika; 2014. (In Russ.) [cited 2024 Jul 9]. Available from: https://library.mededtech.ru/rest/documents/deti_20151/
  44. Moller TB, Reif E. Pocket atlas of radiographic positioning. Moscow: Med. lit.; 2005 ISBN: 5-89-677-044-8 [cited 2024 Jul 9]. Available from: https://mos-medsestra.ru/biblioteka/prof_literatura/Atlas_rentgenologicheskikh_ukladok_Torsten_B_Meller_i_dr.pdf
  45. Vitebskaya AV. Current trends in the diagnosis and treatment of idiopathic dwarfism. Problems of Endocrinology. 2007;53(1):46–53. doi: 10.14341/probl200753146-53 EDN: XGXISN
  46. Ontell FK, Ivanovic M, Ablin DS, Barlow TW. Bone age in children of diverse ethnicity. American Journal of Roentgenology. 1996;167(6):1395–1398. doi: 10.2214/ajr.167.6.8956565
  47. Mansourvar M, Ismail MA, Raj RG, et al. The applicability of Greulich and Pyle atlas to assess skeletal age for four ethnic groups. Journal of Forensic and Legal Medicine. 2014;22:26–29. doi: 10.1016/j.jflm.2013.11.011
  48. Tanner JM, Healy MJR, Goldstein H, et al. Assessment of skeletal maturity and prediction of adult height (TW3 method). 3nd ed. London: W.B. Saunders; 2001 [cited 2024 Jul 9]. Available from: https://search.worldcat.org/en/title/46393147
  49. Tanner JM, Whitehouse RJ. A New System for Estimating Skeletal Maturity from the Hand and Wrist, with Standards Derived from a Study of 2,600 Healthy British Children. Paris: International Children’s Centre; 1962 [cited 2024 Jul 9]. Available from: https://search.worldcat.org/en/title/22456469
  50. Dahlberg PS, Mosdøl A, Ding Y, et al. A systematic review of the agreement between chronological age and skeletal age based on the Greulich and Pyle atlas. European Radiology. 2018;29(6):2936–2948. doi: 10.1007/s00330-018-5718-2 EDN: ULWTMJ
  51. Torné BE. Comparative study between bone ages: Carpal, Metacarpophalangic, Carpometacarpophalangic Ebrí, Greulich and Pyle and Tanner Whitehouse2. Medical Research Archives. 2021;9(12):1–8. doi: 10.18103/mra.v9i12.2625
  52. Alshamrani K, Offiah AC. Applicability of two commonly used bone age assessment methods to twenty-first century UK children. European Radiology. 2019;30(1):504–513. doi: 10.1007/s00330-019-06300-x EDN: UZVGON
  53. Albaker AB, Aldhilan AS, Alrabai HM, et al. Determination of bone age and its correlation to the chronological age based on the Greulich and Pyle method in Saudi Arabia. Journal of Pharmaceutical Research International. 2021;33:1186–1195. doi: https://doi.org/10.9734/jpri/2021/v33i60b34731 EDN: THCVCG
  54. Creo AL, Schwenk WF 2nd. Bone age: a handy tool for pediatric providers. Pediatrics. 2017;140(6):e20171486. doi: 10.1542/peds.2017-1486
  55. Koc A, Karaoglanoglu M, Erdogan M, et al. Assessment of bone ages: is the Greulich-Pyle method sufficient for Turkish boys? Pediatrics International. 2001;43(6):662–665. doi: 10.1046/j.1442-200X.2001.01470.x
  56. Alshamrani K, Messina F, Offiah AC. Is the Greulich and Pyle atlas applicable to all ethnicities? A systematic review and meta-analysis. European Radiology. 2019;29(6):2910–2923. doi: 10.1007/s00330-018-5792-5 EDN: ILWWDK
  57. Nang KM, Ismail AJ, Tangaperumal A, et al. Forensic age estimation in living children: how accurate is the Greulich-Pyle method in Sabah, East Malaysia? Frontiers in Pediatrics. 2023;11:1137960. doi: 10.3389/fped.2023.1137960 EDN: LOPAFL
  58. Elamin F, Abdelazeem N, Elamin A, et al. Skeletal maturity of the hand in an East African group from Sudan. American Journal of Physical Anthropology. 2017;163(4):816–823. doi: 10.1002/ajpa.23247
  59. Alshamrani K, Hewitt A, Offiah AC. Applicability of two bone age assessment methods to children from Saudi Arabia. Clinical Radiology. 2020;75(2):156.e1–156.e9. doi: https://doi.org/10.1016/j.crad.2019.08.029 EDN: ZMWLVO
  60. Patil ST, Parchand MP, Meshram MM, Kamdi NY. Applicability of Greulich and Pyle skeletal age standards to Indian children. Forensic Science International. 2012;216(1-3):200.e1–200.e4. doi: 10.1016/j.forsciint.2011.09.022
  61. Chiang K-H, Chou AS-B, Yen P-S, et al. The reliability of using greulich-pyle method to determine children's bone age in Taiwan. Tzu Chi Medical Journal. 2005;17(6):417–420 [cited 2024 Jul 9]. Available from: https://www.researchgate.net/publication/286044932_The_reliability_of_using_Greulich-Pyle_method_to_determine_children's_bone_age_in_Taiwan
  62. Baginskiy VA, Denisov SD, Dechko VM, Anisova NS. Experience of bone age assessment using the Greulich - Pyle and Tanner - Whitehouse methods. In: Proceedings of international scientific and practical conference dedicated to the 100th anniversary of the Belarusian State Medical University “Modern Technologies in medical education”; 2019 Oct 3–4; Minsk. Minsk: Belarusian State Medical University; 2021. P. 639–641. EDN: CCQMAM
  63. Baginskiy VA, Denisov SD. Bone age assessment using the Greulich-Pyle method. In: Proceedings of works of the scientific and practical conference with international participation “Modern morphology: problems and prospects of development”; 2021 Nov 1–5; Minsk. Minsk: Information and Computing Center of the Ministry of Finance of the Republic of Belarus; 2019. P. 23–25. EDN: DSTZMC
  64. Hackman L, Black S. The reliability of the Greulich and Pyle atlas when applied to a modern scottish population. Journal of Forensic Sciences. 2012;58(1):114–119. doi: 10.1111/j.1556-4029.2012.02294.x
  65. Baginskiy VA, Denisov SD. Comparative analysis of methods for bone age determination. BGMU v avangarde meditsinskoi nauki i praktiki. 2022;2(12):129–136. EDN: TOLBRP
  66. Boeyer ME, Sherwood RJ, Deroche CB, Duren DL. Early maturity as the new normal: a century-long study of bone age. Clinical Orthopaedics & Related Research. 2018;476(11):2112–2122. doi: 10.1097/CORR.0000000000000446
  67. Dorokhov RN. Fundamentals of somatodiagnostics of children and adolescents. Introduction to the subject “Integrative sports morphology”. Smolensk: Smolensk State Academy of Physical Culture, Sports and Tourism; 2015. (In Russ.) ISBN: 978-5-94578-095-8 [cited 2024 Jul 9]. Available from: https://rusneb.ru/catalog/000199_000009_007903248/?utm_source=chatgpt.com
  68. Tanner J.M., Gibbons R.D. A computerized image analysis system for estimating Tanner-Whitehouse 2 bone age. Hormone research. 1994;42(6):282–287. doi: 10.1159/000184210
  69. Safonenkova EV. Secular trend and development prospects (literature review). Journal of New Medical Technologies. 2022;16(3):83–90. doi: 10.24412/2075-4094-2022-3-3-4 EDN: URHUXG
  70. Sharmazanova EP. Radiological data about osseous age in children. Radiology – Practice. 2011;(4):109–111. (In Russ.) EDN: NYBQUZ
  71. Morozov SP, Abuladze LR, Andreichenko AE, et al. Basic recommendations for the operation of artificial intelligence services for radiation diagnostics: methodological recommendations [Internet]. Moscow: Moscow Center for Diagnostics and Telemedicine; 2022. (In Russ.) [cited 2024 Jul 9]. Available from: https://telemedai.ru/biblioteka-dokumentov/bazovye-rekomendacii-k-rabote-servisov-iskusstvennogo-intellekta-dlya-luchevoj-diagnostiki
  72. Vasilev YuA, Bobrovskaya TM, Arzamasov KM, et al Medical datasets for machine learning: fundamental principles of standartization and systematization. Manager Zdravookhranenia. 2023;(4):28–41. doi: 10.21045/1811-0185-2023-4-28-41 EDN: EPGAMD
  73. Vasiliev YuA, Vlazimirsky AV, Omelyanskaya OV, et al. Methodology for testing and monitoring artificial intelligence-based software for medical diagnostics. Digital Diagnostics. 2023;4(3):252–267. doi: 10.17816/DD321971 EDN: UEDORU
  74. Vladzymyrsky AV, Vasilev YuA, Arzamasov KM, et al. Computer vision in radiation diagnostics: the first stage of the Moscow experiment. 2nd ed. Moscow: Izdatel'skie resheniya; 2023 (In Russ.) ISBN: 978-5-0059-3043-9 EDN: FOYLXK
  75. Hsieh CW, Liu TC, Jong TL, Tiu CM. A fuzzy-based growth model with principle component analysis selection for carpal bone-age assessment. Medical & Biological Engineering & Computing. 2010;48(6):579–588. doi: 10.1007/s11517-010-0609-y
  76. Thodberg HH, Kreiborg S, Juul A, Pedersen KD. The BoneXpert method for automated determination of skeletal maturity. IEEE Transactions on Medical Imaging. 2009;28(1):52–66. doi: 10.1109/TMI.2008.926067
  77. Kosik I, Kabak S, Karapetsian R, et al. Determination of bone age using artificial intelligence. BGMU v avangarde meditsinskoi nauki i praktiki. 2020;(10):154–164. EDN: FGOQDF
  78. Lee BD, Lee MS. Automated bone age assessment using artificial intelligence: the future of bone age assessment. Korean Journal of Radiology. 2021;22(5):792. doi: 10.3348/kjr.2020.0941 EDN: WMLLEF
  79. Son SJ, Song Y, Kim N, et al. TW3-based fully automated bone age assessment system using deep neural networks. IEEE Access. 2019;7:33346–33358. doi: 10.1109/ACCESS.2019.2903131
  80. Bui TD, Lee JJ, Shin J. Incorporated region detection and classification using deep convolutional networks for bone age assessment. Artificial Intelligence in Medicine. 2019;97:1–8. doi: 10.1016/j.artmed.2019.04.005
  81. Spampinato C, Palazzo S, Giordano D, et al. Deep learning for automated skeletal bone age assessment in X-ray images. Medical Image Analysis. 2017;36:41–51. doi: 10.1016/j.media.2016.10.010
  82. Tajmir SH, Lee H, Shailam R, et al. Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability. Skeletal Radiology. 2018;48(2):275–283. doi: 10.1007/s00256-018-3033-2 EDN: RTPUEM
  83. Larson DB, Chen MC, Lungren MP, et al. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology. 2018;287(1):313–322. doi: 10.1148/radiol.2017170236
  84. Koitka S, Kim MS, Qu M, et al. Mimicking the radiologists’ workflow: estimating pediatric hand bone age with stacked deep neural networks. Medical Image Analysis. 2020;64:101743. doi: 10.1016/j.media.2020.101743 EDN: QCVQXX
  85. Pan I, Baird GL, Mutasa S, et al. Rethinking Greulich and Pyle: a deep learning approach to pediatric bone age assessment using pediatric trauma hand radiographs. Radiology: Artificial Intelligence. 2020;2(4):e190198. doi: 10.1148/ryai.2020190198 EDN: GLDRHD
  86. Pan X, Zhao Y, Chen H, et al. Fully automated bone age assessment on large-scale hand X-Ray dataset. International Journal of Biomedical Imaging. 2020;2020:1–12. doi: 10.1155/2020/8460493 EDN: VOBVOF
  87. Lee JH, Kim YJ, Kim KG. Bone age estimation using deep learning and hand X-ray images. Biomedical Engineering Letters. 2020;10(3):323–331. doi: 10.1007/s13534-020-00151-y EDN: CGPHJN
  88. Booz C, Yel I, Wichmann JL, et al. Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method. European Radiology Experimental. 2020;4(1):1–8. doi: 10.1186/s41747-019-0139-9EDN: SPZNYP
  89. Kosik II, Nadzved AM, Karapetsian RM. Combined algorithm for bone age determination based on hand X-rays analysis. Journal of the Belarusian State University. Mathematics and Informatics. 2020;(2):105–114. doi: 10.33581/2520-6508-2020-2-105-114 EDN: AEGXIV
  90. Zulkifley MA, Mohamed NA, Abdani SR, et al. Intelligent bone age assessment: an automated system to detect a bone growth problem using convolutional neural networks with attention mechanism. Diagnostics. 2021;11(5):765. doi: 10.3390/diagnostics11050765 EDN: NSUUHA
  91. Martin DD, Calder AD, Ranke MB, et al. Accuracy and self-validation of automated bone age determination. Scientific Reports. 2022;12(1):1–12. doi: 10.1038/s41598-022-10292-y EDN: JJCJGP
  92. Wang X, Zhou B, Gong P, et al. Artificial intelligence–assisted bone age assessment to improve the accuracy and consistency of physicians with different levels of experience. Frontiers in Pediatrics. 2022;10:818061. doi: 10.3389/fped.2022.818061 EDN: VTUQXQ
  93. Zhao K, Ma S, Sun Z, et al. Effect of AI-assisted software on inter- and intra-observer variability for the X-ray bone age assessment of preschool children. BMC Pediatrics. 2022;22(1):1–6. doi: 10.1186/s12887-022-03727-y EDN: LSHLFM
  94. Li Z, Chen W, Ju Y, et al. Bone age assessment based on deep neural networks with annotation-free cascaded critical bone region extraction. Front Artif Intell. 2023;6:1142895. doi: 10.3389/frai.2023.1142895 EDN: RWIWYP
  95. Rassmann S, Keller A, Skaf K, et al. Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias. Pediatric Radiology. 2023;54(1):82–95. doi: 10.1007/s00247-023-05789-1 EDN: QQNRWW
  96. Diachkova GV, Klimov OV, Novikov KI, Novikova OS. Age-related roentgenological peculiarities of the hand bones in patients with achondroplasia. Genij Ortopedii. 2006;(3):36–38. EDN: JJSIXH

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