Comparison of bone age assessment methods using a hand radiography in patients with active growth plate and anteromedial knee instability

Background Bone age is essential for pediatric patients with active growth zones and anteromedial instability to facilitate optimal treatment strategy and minimize postoperative complications. However, many people are unaware of various tools for determining bone age, including classical methods and modern machine learning techniques.

The objective was to show and compare different methods for calculating bone age and determining surgical strategy for patients with anteromedial instability of the knee joint.

Material and methods All-Inside anterior cruciate ligament reconstruction was performed for 20 patients. Wrist radiographs were performed for bone age assessment using the "point scoring system" of Tanner and Whitehouse and the "atlas matching" method of Greulich and Pyle. Machine learning programs were used in addition to standard bone age assessments.

Results The findings showed an average difference of 21 months (80 %) in a group of 20 individuals with bone age ahead of the passport age and an average difference of 18 months (20 %) in patients with retarded bone age.

Discussion The findings showed the difference between chronological and bone age and could be encountered in scientific articles on endocrinology and pediatrics. No scientific studies on the use of the methods could be found in the specialty “trauma and orthopaedics”.

Conclusion Bone age assessment, prediction of children's target height are essential for surgical treatment of patients with open growth plates.

1. Иванов Я.А., Ельцин АГ., Мининков Д.С. Повреждение передней крестообразной связки у детей и подростков. Современные тенденции и исследования. Вестник травматологии и ортопедии им. Н.Н. Приорова. 2021;28(1):89-107. doi: 10.17816/vto51034

2. Тюрин К.А., Захарченко А.Е. Правила техники безопасности в процессе занятий физическими упражнениями. Профилактика травматизма и оказание доврачебной помощи. Тенденции развития науки и образования. 2020;(67-3):145-150. doi: 10.18411/lj-11-2020-123

3. Колунин Е.Т., Прокопьев Н.Я. Баранхин, О.В. Профилактика детского травматизма на занятиях физической культурой и спортом. Современные проблемы физической культуры и спорта: материалы ХXIV Всероссийской научно-практической конференции. Под ред. Е.А. Ветошкиной. Хабаровск: Дальневосточная государственная академия физической культуры; 2020:140-145.

4. Milewski MD, Beck NA, Lawrence JT, Ganley TJ. Anterior cruciate ligament reconstruction in the young athlete: a treatment algorithm for the skeletally immature. Clin Sports Med. 2011;30(4):801-10. doi: 10.1016/j.csm.2011.08.001

5. Yoo WJ, Kocher MS, Micheli LJ. Growth plate disturbance after transphyseal reconstruction of the anterior cruciate ligament in skeletally immature adolescent patients: an MR imaging study. J Pediatr Orthop. 2011;31(6):691-6. doi: 10.1097/BPO.0b013e3182210952

6. Дедов И.И., Петеркова В.А., Семичева Т.В. и др. Детская эндокринология. Руководство по детской эндокринологии. М.: Универсум Паблишинг; 2006:600.

7. Tanner JM, Whitehouse RH. A note on the bone age at which patients with true isolated growth hormone deficiency enter puberty. J Clin Endocrinol Metab. 1975;41(4):788-790. doi: 10.1210/jcem-41-4-788

8. Iannaccone, G. (1959). W. W. Greulich and S. I. Pyle: Radiographic atlas of skeletal development of the hand and wrist. 2nd edition. I volume-atlante di 256 pagine. Stanford University Press, Stanford, California, 1959. Acta Geneticae Medicae Et Gemellologiae: Twin Research. 8(4):513-513. doi: 10.1017/S1120962300018680

9. Thodberg HH, Kreiborg S, Juul A, Pedersen KD. The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging. 2009;28(1):52-66. doi: 10.1109/TMI.2008.926067

10. Косик И.И., Кабак С.Л., Карапетян Г.М. и др. Определение костного возраста с использованием искусственного интеллекта. БГМУ в авангарде медицинской науки и практики: рецензируемый ежегодный сборник научных трудов. Минск: Белорусский государственный медицинский университет; 2020:154-165.

11. Косик И.И, Недзьведь А.М, Карапетян Г.М. Комбинированный алгоритм определения костного возраста на основе анализа рентгенограмм кисти. Журнал Белорусского государственного университета. Математика. Информатика. 2020;(2):105-114. doi: 10.33581/2520-6508-2020-2-105-114

12. Thodberg HH. Automatic determination of skeletal maturity using appearance models Proc. ESPE/LWPES 7th Joint Meeting. Hormone Res. 2005;64(Suppl. 1). doi: 10.1159/000088318

13. Иванов Я.А., Ельцин А.Г., Мининков Д.С. Валидация и культурная адаптация шкалы KOOS-Child. Вестник травматологии и ортопедии им. НН Приорова. 2021;28(1):53-64. doi: 10.17816/vto60489

14. Örtqvist M, Roos EM, Broström EW, et al. Development of the Knee Injury and Osteoarthritis Outcome Score for children (KOOS-Child): comprehensibility and content validity. Acta Orthop. 2012;83(6):666-73. doi: 10.3109/17453674.2012.747921

15. Kocher MS, Smith JT, Iversen MD, et al D. Reliability, validity, and responsiveness of a modified International Knee Documentation Committee Subjective Knee Form (Pedi-IKDC) in children with knee disorders. Am J Sports Med. 2011;39(5):933-9. doi: 10.1177/0363546510383002

16. De Sanctis V, Soliman AT, Di Maio S, Bedair S. Are the new automated methods for bone age estimation advantageous over the manual approaches? Pediatr Endocrinol Rev. 2014;12(2):200-205.

17. Manzoor Mughal A, Hassan N, Ahmed A. Bone age assessment methods: a critical review. Pak J Med Sci. 2014;30(1):211-215. doi: 10.12669/pjms.301.4295

18. Kim JR, Shim WH, Yoon HM, et al. Computerized Bone Age Estimation Using Deep Learning Based Program: Evaluation of the Accuracy and Efficiency. AJR Am J Roentgenol. 2017;209(6):1374-1380. doi: 10.2214/AJR.17.18224

19. Yildiz M, Guvenis A, Guven E, Talat D, Haktan M. Implementation and statistical evaluation of a web-based software for bone age assessment. J Med Syst. 2011;35(6):1485-9. doi: 10.1007/s10916-009-9425-z

20. Halabi SS, Prevedello LM, Kalpathy-Cramer J, et al. The RSNA Pediatric Bone Age Machine Learning Challenge. Radiology. 2019;290(2):498-503. doi: 10.1148/radiol.2018180736

21. Booz C, Yel I, Wichmann JL, Boettger S, et al. Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method. Eur Radiol Exp. 2020;4(1):6. doi: 10.1186/s41747-019-0139-9

22. Lee H, Tajmir S, Lee J, et al. Fully Automated Deep Learning System for Bone Age Assessment. J Digit Imaging. 2017;30(4):427-441. doi: 10.1007/s10278-017-9955-8

23. Nadeem MW, Goh HG, Ali A, et al. Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions. Diagnostics (Basel). 2020;10(10):781. doi: 10.3390/diagnostics10100781

24. Iglovikov VI, Rakhlin A, Kalinin AA, Shvets AA. Paediatric Bone Age Assessment Using Deep Convolutional Neural Networks. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. DLMIA ML-CDS 2018. Lecture Notes in Computer Science. Springer, Cham.; 2018;11045. doi: 10.1007/978-3-030-00889-5_34

25. Nguyen QH, Nguyen BP, Nguyen MT, et al. Bone age assessment and sex determination using transfer learning. Expert Systems with Applications. 2022;200:116926. doi: 10.1016/j.eswa.2022.116926

26. Lea WW, Hong SJ, Nam HK, et al. External validation of deep learning-based bone-age software: a preliminary study with real world data. Sci Rep. 2022;12(1):1232. doi: 10.1038/s41598-022-05282-z

27. 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. Front Pediatr. 2022;10:818061. doi: 10.3389/fped.2022.818061

28. Guo L, Wang J, Teng J, Chen Y. Bone age assessment based on deep convolutional features and fast extreme learning machine algorithm. Front Energy Res. 2022;9: 813650. doi: 10.3389/fenrg.2021.813650

29. Son S.J., 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

30. Li Y, Huang Z, Dong X, et al. Forensic age estimation for pelvic X-ray images using deep learning. Eur Radiol. 2019;29(5):2322-2329. doi: 10.1007/s00330-018-5791-6