Abstract
Objective
To determine which bones and which grades had the highest inter-rater variability when employing the Tanner–Whitehouse (T-W) method.
Materials and methods
Twenty-four radiologists were recruited and trained in the T-W classification of skeletal development. The consistency and skill of the radiologists in determining bone development status were assessed using 20 pediatric hand radiographs of children aged 1 to 18 years old. Four radiologists had a poor concordance rate and were excluded. The remaining 20 radiologists undertook a repeat reading of the radiographs, and their results were analyzed by comparing them with the mean assessment of two senior experts as the reference standard. Concordance rate, scoring, and Kendall’s W were calculated to evaluate accuracy and consistency.
Results
Both the radius, ulna, and short finger (RUS) system (Kendall’s W = 0.833) and the carpal (C) system (Kendall’s W = 0.944) had excellent consistency, with the RUS system outperforming the C system in terms of scores. The repeatability analysis showed that the second rating test, performed after 2 months of further bone age assessment (BAA) practice, was more consistent and accurate than the first. The capitate had the lowest average concordance rate and scoring, as well as the lowest overall concordance rate for its D classification. Moreover, the G classifications of the seven carpal bones all had a concordance rate less than 0.6. The bones with lower Kendall’s W were likewise those with lower scores and concordance rates.
Conclusion
The D grade of the capitate showed the highest variation, and the use of the Tanner–Whitehouse 3rd edition (T-W3) to determine bone age (BA) was frequently inconsistent. A more comprehensive description with a focus on inaccuracy bones or ratings and a modification to the T-W3 approach would significantly advance BAA.
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Data availability
Data are available upon request.
Abbreviations
- BA:
-
Bone age
- BAA:
-
Bone age assessment
- GP:
-
Greulich–Pyle
- T-W:
-
Tanner–Whitehouse
- CAD:
-
Computer-assisted diagnosis
- AI:
-
Artificial intelligence
- JPEG:
-
Joint Photographic Experts Group
- RUS:
-
Radius, ulna, and short finger bones
- C:
-
Carpal bone
- CA:
-
Chronological age
References
Schwarze CP, Arens D, Haber HP, Wollmann HA, Binder G, Mayer EI, et al. Bone age in 116 untreated patients with Turner’s syndrome rated by a computer-assisted method (CASAS). Acta Paediatr. 1998;87(11):1146–50.
Jones G, Ma D. Skeletal age deviation assessed by the Tanner-Whitehouse 2 method is associated with bone mass and fracture risk in children. Bone. 2005;36(2):352–7.
Tanner JM, Landt KW, Cameron N, Carter BS, Patel J. Prediction of adult height from height and bone age in childhood. A new system of equations (TW Mark II) based on a sample including very tall and very short children. Arch Dis Child. 1983;58(10):767–76.
Malina RM, Coelho ESMJ, Figueiredo AJ, Philippaerts RM, Hirose N, Pena Reyes ME, et al. Tanner-Whitehouse skeletal ages in male youth soccer players: TW2 or TW3? Sports Med. 2018;48(4):991–1008.
Acheson RM. A method of assessing skeletal maturity from radiographs; a report from the Oxford child health survey. J Anat. 1954;88(4):498–508.
So LL. Skeletal maturation of the hand and wrist and its correlation with dental development. Aust Orthod J. 1997;15(1):1–9.
Subramanian S, Viswanathan VK. Bone age. StatPearls. Treasure Island (FL); 2022.
Taylor CJ, Monahan M, Roalfe AK, Barton P, Iles R, Hobbs FDR. The REFER (REFer for EchocaRdiogram) study: a prospective validation and health economic analysis of a clinical decision rule, NT-proBNP or their combination in the diagnosis of heart failure in primary care. Southampton (UK); 2017.
Zachmann M, Frasier SD, McLaughlin J, Hurley L, Nessi P. Importance and accuracy of bone age ratings in a computerized growth evaluation system. Horm Res. 1983;18(4):160–7.
Bull RK, Edwards PD, Kemp PM, Fry S, Hughes IA. Bone age assessment: a large scale comparison of the Greulich and Pyle, and Tanner and Whitehouse (TW2) methods. Arch Dis Child. 1999;81(2):172–3.
Gertych A, Zhang A, Sayre J, Pospiech-Kurkowska S, Huang HK. Bone age assessment of children using a digital hand atlas. Comput Med Imaging Graph. 2007;31(4–5):322–31.
Zhou XL, Wang EG, Lin Q, Dong GP, Wu W, Huang K, et al. Diagnostic performance of convolutional neural network-based Tanner-Whitehouse 3 bone age assessment system. Quant Imaging Med Surg. 2020;10(3):657–67.
Wang X, Zhou B, Gong P, Zhang T, Mo Y, Tang J, 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.
Computer-assisted diagnosis. Lancet (London, England). 1989;2(8676):1371.
Lee BD, Lee MS. Automated bone age assessment using artificial intelligence: the future of bone age assessment. Korean J Radiol. 2021;22(5):792–800.
Groell R, Lindbichler F, Riepl T, Gherra L, Roposch A, Fotter R. The reliability of bone age determination in central European children using the Greulich and Pyle method. Br J Radiol. 1999;72(857):461–4.
Gao C, Qian Q, Li Y, Xing X, He X, Lin M, et al. A comparative study of three bone age assessment methods on Chinese preschool-aged children. Front Pediatr. 2022;10:976565.
Yuh YS, Chou TY, Tung TH. Bone age assessment: large-scale comparison of Greulich-Pyle method and Tanner-Whitehouse 3 method for Taiwanese children. J Chin Med Assoc. 2023;86(2):246–53.
Ashizawa K, Kumakura C, Zhou X, Jin F, Cao J. RUS skeletal maturity of children in Beijing. Ann Hum Biol. 2005;32(3):316–25.
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.
Büken B, Erzengin OU, Büken E, Safak AA, Yazici B, Erkol Z. Comparison of the three age estimation methods: which is more reliable for Turkish children? Forensic Sci Int. 2009;183(1–3):103.e101-107.
Zhang A, Sayre JW, Vachon L, Liu BJ, Huang HK. Racial differences in growth patterns of children assessed on the basis of bone age. Radiology. 2009;250(1):228–35.
Han Y, Wang G. Skeletal bone age prediction based on a deep residual network with spatial transformer. Comput Methods Programs Biomed. 2020;197:105754.
Lee JH, Kim YJ, Kim KG. Bone age estimation using deep learning and hand X-ray images. Biomed Eng Lett. 2020;10(3):323–31.
Nadeem MW, Goh HG, Ali A, Hussain M, Khan MA, Ponnusamy VA. Bone age assessment empowered with deep learning: a survey, open research challenges and future directions. Diagnostics (Basel). 2020;10(10).
Cavallo F, Mohn A, Chiarelli F, Giannini C. Evaluation of bone age in children: a mini-review. Front Pediatr. 2021;9:580314.
Funding
This work was supported by the Beijing Jishuitan Hospital Elite Young Scholar Programme (XKGG202122), the Beijing Hospitals Authority Youth Programme (code: 20200402), and the Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support (code: ZYLX202107).
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Jian Geng, Wenshuang Zhang, and Yufeng Ge contribute equally and are co-first authors.
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Geng, J., Zhang, W., Ge, Y. et al. Inter-rater variability and repeatability in the assessment of the Tanner–Whitehouse classification of hand radiographs for the estimation of bone age. Skeletal Radiol (2024). https://doi.org/10.1007/s00256-024-04664-w
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DOI: https://doi.org/10.1007/s00256-024-04664-w