Validation of automated bone age analysis from hand radiographs in a North American pediatric population.
Artificial intelligence
Automated
Bone age
Children
Greulich and Pyle
Hand
Radiography
Skeletal maturation
Journal
Pediatric radiology
ISSN: 1432-1998
Titre abrégé: Pediatr Radiol
Pays: Germany
ID NLM: 0365332
Informations de publication
Date de publication:
06 2022
06 2022
Historique:
received:
19
02
2021
accepted:
03
02
2022
revised:
21
12
2021
pubmed:
25
3
2022
medline:
16
6
2022
entrez:
24
3
2022
Statut:
ppublish
Résumé
Radiographic bone age assessment by automated software is precise and instantaneous. The aim of this study was to evaluate the accuracy of an automated tool for bone age assessment. We compared a total of 586 bone age radiographs from 451 patients, which had been assessed by three radiologists from 2013 to 2018, with bone age analysis by BoneXpert, using the Greulich and Pyle method. We made bone age comparisons in different patient groups based on gender, diagnosis and race, and in a subset with repeated bone age studies. We calculated Spearman correlation (r) and accuracy (root mean square error, or R Bone age analyses by automated and manual assessments showed a strong correlation (r=0.98; R Automated bone age assessment was found to be reliable and accurate in a large cohort of pediatric patients in a clinical practice setting in North America.
Sections du résumé
BACKGROUND
Radiographic bone age assessment by automated software is precise and instantaneous.
OBJECTIVE
The aim of this study was to evaluate the accuracy of an automated tool for bone age assessment.
MATERIALS AND METHODS
We compared a total of 586 bone age radiographs from 451 patients, which had been assessed by three radiologists from 2013 to 2018, with bone age analysis by BoneXpert, using the Greulich and Pyle method. We made bone age comparisons in different patient groups based on gender, diagnosis and race, and in a subset with repeated bone age studies. We calculated Spearman correlation (r) and accuracy (root mean square error, or R
RESULTS
Bone age analyses by automated and manual assessments showed a strong correlation (r=0.98; R
CONCLUSION
Automated bone age assessment was found to be reliable and accurate in a large cohort of pediatric patients in a clinical practice setting in North America.
Identifiants
pubmed: 35325266
doi: 10.1007/s00247-022-05310-0
pii: 10.1007/s00247-022-05310-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1347-1355Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Manzoor Mughal A, Hassan N, Ahmed A (2014) Bone age assessment methods: a critical review. Pak J Med Sci 30:211–215
pubmed: 24639863
pmcid: 3955574
Gilli G (1996) The assessment of skeletal maturation. Horm Res 45:49–52
doi: 10.1159/000184847
Martin DD, Wit JM, Hochberg Z et al (2011) The use of bone age in clinical practice — part 1. Horm Res Paediatr 76:1–9
doi: 10.1159/000329372
Schlégl ÁT, O'Sullivan I, Varga P et al (2017) Determination and correlation of lower limb anatomical parameters and bone age during skeletal growth (based on 1,005 cases). J Orthop Res 35:1431–1441
doi: 10.1002/jor.23390
Jada A, Mackel CE, Hwang SW et al (2017) Evaluation and management of adolescent idiopathic scoliosis: a review. Neurosurg Focus 43:E2
doi: 10.3171/2017.7.FOCUS17297
Alshamrani K, Messina F, Offiah AC (2019) Is the Greulich and Pyle atlas applicable to all ethnicities? A systematic review and metaanalysis. Eur Radiol 29:2910–2923
doi: 10.1007/s00330-018-5792-5
Greulich W, Pyle S (1959) Radiographic atlas of skeletal development of the hand and wrist, 2nd edn. Stanford University Press, Stanford
De Sanctis V, Di Maio S, Soliman AT et al (2014) Hand X-ray in pediatric endocrinology: skeletal age assessment and beyond. Indian J Endocrinol Metab 18:S63–S71
doi: 10.4103/2230-8210.145076
Berst MJ, Dolan L, Bogdanowicz MM et al (2001) Effect of knowledge of chronologic age on the variability of pediatric bone age determined using the Greulich and Pyle standards. AJR Am J Roentgenol 176:507–510
doi: 10.2214/ajr.176.2.1760507
Dallora AL, Anderberg P, Kvist O et al (2019) Bone age assessment with various machine learning techniques: a systematic literature review and meta-analysis. PLoS One 14:e0220242
doi: 10.1371/journal.pone.0220242
Kim JR, Shim WH, Yoon HM et al (2017) Computerized bone age estimation using deep learning based program: evaluation of the accuracy and efficiency. AJR Am J Roentgenol 209:1374–1380
doi: 10.2214/AJR.17.18224
Spampinato C, Palazzo S, Giordano D et al (2017) Deep learning for automated skeletal bone age assessment in X-ray images. Med Image Anal 36:41–51
doi: 10.1016/j.media.2016.10.010
Zulkifley MA, Mohamed NA, Abdani SR et al (2021) Intelligent bone age assessment: an automated system to detect a bone growth problem using convolutional neural networks with attention mechanism. Diagnostics 11:765
doi: 10.3390/diagnostics11050765
van Rijn RR, Lequin MH, Thodberg HH (2009) Automatic determination of Greulich and Pyle bone age in healthy Dutch children. Pediatr Radiol 39:591–597
doi: 10.1007/s00247-008-1090-8
Thodberg HH, Kreiborg S, Juul A, Pedersen KD (2009) The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging 28:52–66
doi: 10.1109/TMI.2008.926067
Lin LI (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45:255–268
doi: 10.2307/2532051
Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1:307–310
doi: 10.1016/S0140-6736(86)90837-8
Martin DD, Sato K, Sato M et al (2010) Validation of a new method for automated determination of bone age in Japanese children. Horm Res Paediatr 73:398–404
doi: 10.1159/000308174
Zhang SY, Liu G, Ma CG et al (2013) Automated determination of bone age in a modern Chinese population. ISRN Radiol 2013:874570
doi: 10.5402/2013/874570
Thodberg HH, Savendahl L (2010) Validation and reference values of automated bone age determination for four ethnicities. Acad Radiol 17:1425–1432
doi: 10.1016/j.acra.2010.06.007
Artioli TO, Alvares MA, Carvalho Macedo VS et al (2019) Bone age determination in eutrophic, overweight and obese Brazilian children and adolescents: a comparison between computerized BoneXpert and Greulich-Pyle methods. Pediatr Radiol 49:1185–1191
doi: 10.1007/s00247-019-04435-z
Alshamrani K, Messina F, Offiah AC (2019) Is the Greulich and Pyle atlas applicable to all ethnicities? A systematic review and meta-analysis. Eur Radiol 29:2910–2923
doi: 10.1007/s00330-018-5792-5
van Rijn RR, Thodberg HH (2013) Bone age assessment: automated techniques coming of age? Acta Radiol 54:1024–1029
doi: 10.1258/ar.2012.120443
Martin DD, Deusch D, Schweizer R et al (2009) Clinical application of automated Greulich-Pyle bone age determination in children with short stature. Pediatr Radiol 39:598–607
doi: 10.1007/s00247-008-1114-4