INTRODUCTION: Automated bone age assessment has recently become increasingly popular. The aim of this study was to assess the agreement between automated and manual evaluation of bone age using the method according to Tanner-Whitehouse (TW3) and Greulich-Pyle (GP). METHODS: We evaluated 1285 bone age scans from 1202 children (657 scans from 612 boys) by using both manual and automated (TW3 as well as GP) bone age assessment. BoneXpert software versions 2.4.5.1. (BX2) and 3.2.1. (BX3) (Visiana, Holte, Denmark) were compared with manual evaluation using root mean squared error (RMSE) analysis. RESULTS: RMSE for BX2 was 0.57 and 0.55 years in boys and 0.72 and 0.59 years in girls, respectively for TW3 and GP. For BX3, RMSE was 0.51 and 0.68 years in boys and 0.49 and 0.52 years in girls, respectively for TW3 and GP. Sex- and age-specific analysis for BX2 identified the largest differences between manual and automated TW3 evaluation in girls between 6-7, 12-13, 13-14 and 14-15 years, with RMSE 0.88, 0.81, 0.92 and 0.84 years, respectively. The BX3 version showed better agreement with manual TW3 evaluation (RMSE 0.64, 0.45, 0.46 and 0.57). CONCLUSION: The latest version of the BoneXpert software provides improved and clinically sufficient agreement with manual bone age evaluation in children of both sexes compared to the previous version and may be used for routine bone age evaluation in non-selected cases in pediatric endocrinology care.

Department of Anthropology and Human Genetics Faculty of Science Charles University Prague Czechia

Department of Pediatrics 2nd Faculty of Medicine Charles University and Motol University Hospital Prague Czechia

Department of Probability and Mathematical Statistics Faculty of Mathematics and Physic Charles University Prague Czechia

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Cohen P, Rogol AD, Deal CL, Saenger P, Reiter EO, Ross JL, et al. . Consensus statement on the diagnosis and treatment of children with idiopathic short stature: A summary of the growth hormone research society, the Lawson Wilkins pediatric endocrine society, and the European society for paediatric endocrinology workshop. J Clin Endocrinol Metab (2008) 93(11):4210–7. doi: 10.1210/jc.2008-0509 PubMed DOI

Bangalore Krishna K, Fuqua JS, Rogol AD, Klein KO, Popovic J, Houk CP, et al. . Use of gonadotropin-releasing hormone analogs in children: Update by an international consortium. Horm Res Paediatr (2019) 91(6):357–72. doi: 10.1159/000501336 PubMed DOI

Greulich WW, Pyle IS. Radiographic atlas of skeletal development of the hand and wrist. 2nd ed. Stanford: Stanford University Press; (1959) 256.

Tanner JM, Healy MJR, Cameron N, Goldstein H. Assessment of skeletal maturity and prediction of adult height (TW3 method). 3rd ed. Russell D, editor. London: Harcourt Publishers Limited; (2001). 110. Available at: https://books.google.cz/books?id=KKdxQgAACAAJ.

Roche AF, Rohmann CG, French NY, Dávila GH. Effect of training on replicability of assessments of skeletal maturity (Greulich-pyle). Am J Roentgenol Radium Ther Nucl Med (1970) 108(3):511–5. doi: 10.2214/ajr.108.3.511 PubMed DOI

Van Rijn RR, Thodberg HH. Bone age assessment: Automated techniques coming of age? Acta Radiol (2013) 54:1024–9. doi: 10.1258/ar.2012.120443 PubMed DOI

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 PubMed DOI

Martin DD, Calder AD, Ranke MB, Binder G, Thodberg HH. Accuracy and self-validation of automated bone age determination. Sci Rep [Internet] (2022) 12(1):1–12. doi: 10.1038/s41598-022-10292-y PubMed DOI PMC

Rijn van RR, Lequin MH, Thodberg HH. Automatic determination of greulich and pyle bone age in healthy Dutch children. Pediatr Radiol (2009) 39:591–7. doi: 10.1007/s00247-008-1090-8 PubMed DOI

Thodberg HH, Jenni OG, Ranke MB, Martin DD. Standardization of the tanner-whitehouse bone age method in the context of automated image analysis. Ann Hum Biol (2012) 39(1):68–75. doi: 10.3109/03014460.2011.642405 PubMed DOI

Czech Statistical Office . Census 2021 - ethicity. Census 2021; (2021). Available at: www.czso.cz/csu/scitani2021/ethnicity.

Avdeef A. Do you know your r2? ADMET DMPK (2021) 9(1):69–74. doi: 10.5599/admet.888 PubMed DOI PMC

Diebold FX, Mariano RS. Comparing predictive accuracy. J Bus Econ Stat (1995) 13:253–63.

R Core Team . R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; (2021). Available at: https://www.r-project.org/.

Thodberg HH, Sa L. Validation and reference values of automated bone age determination for four ethnicities. Academic Radiology (2010) 6):1425–32. doi: 10.1016/j.acra.2010.06.007 PubMed DOI

Oza C, Khadilkar AV, Mondkar S, Gondhalekar K, Ladkat A, Shah N, et al. . A comparison of bone age assessments using automated and manual methods in children of Indian ethnicity. Pediatr Radiol (2022) 52(11):2188–96. doi: 10.1007/s00247-022-05516-2 PubMed DOI

Wang YM, Tsai TH, Hsu JS, Chao MF, Wang YT, Jaw TS. Automatic assessment of bone age in Taiwanese children: A comparison of the greulich and pyle method and the tanner and whitehouse 3 method. Kaohsiung J Med Sci (2020) 36(11):937–43. doi: 10.1002/kjm2.12268 PubMed DOI PMC

Bowden JJ, Bowden SA, Ruess L, Adler BH, Hu H, Krishnamurthy R, et al. . Validation of automated bone age analysis from hand radiographs in a north American pediatric population. Pediatr Radiol (2022) 52(7):1347–55. doi: 10.1007/s00247-022-05310-0 PubMed DOI

Krasnicanova H, Kuchynkova I. New method of assessment of bone age TW3 and first results of its application in the Czech republic. Česko-slovenská Pediatr (2002) 57(2):62–5.

Martin DD, Meister K, Schweizer R, Ranke MB, Thodberg HH, Binder G. Validation of automatic bone age rating in children with precocious and early puberty. J Pediatr Endocrinol Metab (2011) 24:1009–14. doi: 10.1515/JPEM.2011.420 PubMed DOI

Martin DD, Heil K, Heckmann C, Zierl A, Schaefer J, Ranke MB, et al. . Validation of automatic bone age determination in children with congenital adrenal hyperplasia. Pediatr Radiol (2013) 43:1615–21. doi: 10.1007/s00247-013-2744-8 PubMed DOI

Martin DD, Deusch D, Schweizer R, Binder G, Thodberg HH, Ranke MB. Clinical application of automated greulich-pyle bone age determination in children with short stature. Pediatr Radiol (2009) 39:598–607. doi: 10.1007/s00247-008-1114-4 PubMed DOI

Thodberg HH, Thodberg B, Ahlkvist J, Offiah AC. Autonomous artificial intelligence in pediatric radiology: The use and perception of BoneXpert for bone age assessment. Pediatr Radiol (2022) 52(7):1338–46. doi: 10.1007/s00247-022-05295-w PubMed DOI PMC

Cytogenetic anomalies are the predominant genetic alteration in children with nonfamilial tall stature: a comparative study with familial cases