Artificial intelligence in the endocrine clinic: automated bone age analysis in arabic children from UAE Artificial intelligence in bone age analysis

Main Article Content

Nandu Kumar Thalange
Elham Ahmed Elgabaly Moustafa Ahmed
Ajay Prasanth D’Souza
Mireille El Bejjani


Objective: Artificial intelligence (AI) is playing an increasing role in patient assessment. AI bone age analysis is such a tool, but its value in Arabic children presenting to an endocrine clinic has not been explored. We compared results from an experienced pediatric radiologist and the AI bone age system, BoneXpert (BX), (Visiana, Denmark) to assess its utility in a cohort of children presenting to the Al Jalila Children’s Specialty Hospital endocrine service.

Materials and Methods: We conducted a retrospective chart review of 47 children with growth disorders, initially assessed by a single experienced radiologist and subsequently by BX, to confirm the usefulness of the BX system in our population. The results of the analyses were analysed using a Bland-Altman plot constructed to compare differences between the radiologist’s interpretation and BX across the available range of bone age.

Results: Forty-four of the patient x-ray images were analysed by BX. Three X-ray images were rejected by BX due to post-processing artifacts, which prevented computer interpretation. For the remaining 44 X-rays, there was a close correlation between radiologist and BX results (r=0.93; p <0.00001).  Two radiographs were identified with a large discrepancy in the reported bone ages. Blinded, independent re-evaluation of the radiographs showed the original manually interpreted bone age to have been erroneous, with the BX results corresponding closely to the amended bone age. A small positive bias was noted in bone age (+0.39 years) in the BX analyses, relative to manual interpretation.

Conclusions: AI bone age analysis was of high utility in Arabic children from UAE presenting to an endocrine clinic, with results highly comparable to an experienced radiologist. In the two cases where a large discrepancy was found, independent re-evaluation showed AI analysis was correct.


Download data is not yet available.

Article Details

How to Cite
Thalange, N. K., Elgabaly Moustafa Ahmed, E., D’Souza, A. P., & El Bejjani, M. (2021). Artificial intelligence in the endocrine clinic: automated bone age analysis in arabic children from UAE: Artificial intelligence in bone age analysis. Medical Science and Discovery, 8(7), 418–422.
Research Article
Received 2021-07-04
Accepted 2021-07-20
Published 2021-07-28


Creo AL, Schwenk WF 2nd. Bone Age: A Handy Tool for Pediatric Providers. Pediatrics. 2017 Dec;140(6):e20171486. PMID:29141916

Martin DD, Wit JM, Hochberg Z, Sävendahl L, van Rijn RR, Fricke O, et al. The use of bone age in clinical practice - part 1. Horm Res Paediatr. 2011;76(1):1–9. PMID:21691054

Labarta JI, Ranke MB, Maghnie M, Martin D, Guazzarotti L, Pfäffle R, et al. Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short Stature. J Clin Res Pediatr Endocrinol. 2021 Jun;13(2):124–35. PMID:33006554

Vargas Trujillo M, Dragnic S, Aldridge P, Klein KO. Importance of individualizing treatment decisions in girls with central precocious puberty when initiating treatment after age 7 years or continuing beyond a chronological age of 10 years or a bone age of 12 years. J Pediatr Endocrinol Metab. 2021 Apr;34(6):733–9. PMID:33856747

Martin DD, Schittenhelm J, Thodberg HH. Validation of adult height prediction based on automated bone age determination in the Paris Longitudinal Study of healthy children. Pediatr Radiol. 2016 Feb;46(2):263–9. PMID:26573823

Martin DD, Wit JM, Hochberg Z, van Rijn RR, Fricke O, Werther G, et al. The use of bone age in clinical practice - part 2. Horm Res Paediatr. 2011;76(1):10–6. PMID:21691055

Greulich W, Pyle I. Radiographic atlas of skeletal development of the hand and wrist. London: Stanford University Press; 1959.

Tanner JM, Healy MJ, Goldstein H, Cameron N. Assessment of skeletal maturity and prediction of adult height TW3 method. London: WB Saunders; 2001.

Thodberg HH, Jenni OG, Caflisch J, Ranke MB, Martin DD. Prediction of adult height based on automated determination of bone age. J Clin Endocrinol Metab. 2009 Dec;94(12):4868–74. PMID:19926715

Alshamrani K, Messina F, Offiah AC. Is the Greulich and Pyle atlas applicable to all ethnicities? A systematic review and meta-analysis. Eur Radiol. 2019 Jun;29(6):2910–23. PMID:30617474

Michael DJ, Nelson AC. HANDX: a model-based system for automatic segmentation of bones from digital hand radiographs. IEEE Trans Med Imaging. 1989;8(1):64–9. PMID:18230501

Pietka E, McNitt-Gray MF, Kuo ML, Huang HK. Computer-assisted phalangeal analysis in skeletal age assessment. IEEE Trans Med Imaging. 1991;10(4):616–20. PMID:18222868

Tanner JM, Oshman D, Lindgren G, Grunbaum JA, Elsouki R, Labarthe D. Reliability and validity of computer-assisted estimates of Tanner-Whitehouse skeletal maturity (CASAS): comparison with the manual method. Horm Res. 1994;42(6):288–94. PMID:7698726

Thodberg HH, Kreiborg S, Juul A, Pedersen KD. The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging. 2009 Jan;28(1):52–66. PMID:19116188

Thodberg HH, Sävendahl L. Validation and reference values of automated bone age determination for four ethnicities. Acad Radiol. 2010 Nov;17(11):1425–32. PMID:20691616

Halabi SS, Prevedello LM, Kalpathy-Cramer J, Mamonov AB, Bilbily A, Cicero M, et al. The RSNA Pediatric Bone Age Machine Learning Challenge. Radiology. 2019 Feb;290(2):498–503. PMID:30480490

Schündeln MM, Marschke L, Bauer JJ, Hauffa PK, Schweiger B, Führer-Sakel D, et al. A Piece of the Puzzle: The Bone Health Index of the BoneXpert Software Reflects Cortical Bone Mineral Density in Pediatric and Adolescent Patients. PLoS One. 2016 Mar;11(3):e0151936. PMID:27014874

Leijten AD, Hampsink B, Janssen M, Klein WM, Draaisma JM. Can digital X-ray radiogrammetry be an alternative for dual-energy X-ray absorptiometry in the diagnosis of secondary low bone quality in children? Eur J Pediatr. 2019 Sep;178(9):1433–41. PMID:31352546

Boeyer ME, Sherwood RJ, Deroche CB, Duren DL. Early Maturity as the New Normal: A Century-long Study of Bone Age. Clin Orthop Relat Res. 2018 Nov;476(11):2112–22. PMID:30179948

De Sanctis V, Di Maio S, Soliman AT, Raiola G, Elalaily R, Millimaggi G. Hand X-ray in pediatric endocrinology: skeletal age assessment and beyond. Indian J Endocrinol Metab. 2014 Nov;18(7 Suppl 1):S63–71. PMID:25538880

Zhang SY, Liu G, Ma CG, Han YS, Shen XZ, Xu RL, et al. Automated determination of bone age in a modern chinese population. ISRN Radiol. 2013 Feb;2013:874570. PMID:24967289

Koc U, Taydaş O, Bolu S, Elhan AH, Karakas SP. The Greulich-Pyle and Gilsanz-Ratib atlas method versus automated estimation tool for bone age: a multi-observer agreement study. Jpn J Radiol. 2021 Mar;39(3):267–72. PMID:33067733

Alshamrani K, Hewitt A, Offiah AC. Applicability of two bone age assessment methods to children from Saudi Arabia. Clin Radiol. 2020 Feb;75(2):156.e1–9. PMID:31706569

Berst MJ, Dolan L, Bogdanowicz MM, Stevens MA, Chow S, Brandser EA. Effect of knowledge of chronologic age on the variability of pediatric bone age determined using the Greulich and Pyle standards. AJR Am J Roentgenol. 2001 Feb;176(2):507–10. PMID:11159105

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 Aug;81(2):172–3. PMID:10490531

van Rijn RR, Thodberg HH. Bone age assessment: automated techniques coming of age? Acta Radiol. 2013 Nov;54(9):1024–9. PMID:24179234

Lee BD, Lee MS. Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment. Korean J Radiol. 2021 May;22(5):792–800. PMID:33569930