Modernization of bone age assessment: comparing the accuracy and reliability of an artificial intelligence algorithm and shorthand bone age to Greulich and Pyle.
16 bit
Bone age
Greulich and Pyle
Shorthand bone age
Journal
Skeletal radiology
ISSN: 1432-2161
Titre abrégé: Skeletal Radiol
Pays: Germany
ID NLM: 7701953
Informations de publication
Date de publication:
Sep 2020
Sep 2020
Historique:
received:
18
11
2019
accepted:
23
03
2020
revised:
20
03
2020
pubmed:
25
4
2020
medline:
25
6
2021
entrez:
25
4
2020
Statut:
ppublish
Résumé
Greulich and Pyle (GP) is one of the most common methods to determine bone age from hand radiographs. In recent years, new methods were developed to increase the efficiency in bone age analysis like the shorthand bone age (SBA) and automated artificial intelligence algorithms. The aim of this study is to evaluate the accuracy and reliability of these two methods and examine if the reduction in analysis time compromises their efficacy. Two hundred thirteen males and 213 females had their bone age determined by two separate raters using the SBA and GP methods. Three weeks later, the two raters repeated the analysis of the radiographs. The raters timed themselves using an online stopwatch. De-identified radiographs were securely uploaded to an automated algorithm developed by a group of radiologists in Toronto. The gold standard was determined to be the radiology report attached to each radiograph, written by experienced radiologists using GP. Intraclass correlation between each method and the gold standard fell within the range of 0.8-0.9, highlighting significant agreement. Most of the comparisons showed a statistically significant difference between the new methods and the gold standard; however, it may not be clinically significant as it ranges between 0.25 and 0.5 years. A bone age is considered clinically abnormal if it falls outside 2 standard deviations of the chronological age; standard deviations are calculated and provided in GP atlas. The shorthand bone age method and the automated algorithm produced values that are in agreement with the gold standard while reducing analysis time.
Identifiants
pubmed: 32328674
doi: 10.1007/s00256-020-03429-5
pii: 10.1007/s00256-020-03429-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1449-1457Références
Heyworth BE, Osei DA, Fabricant PD, et al. The shorthand bone age assessment: a simpler alternative to current methods. J Pediatr Orthop. 2013;33(5):569–74. https://doi.org/10.1097/BPO.0b013e318293e5f2 .
doi: 10.1097/BPO.0b013e318293e5f2
pubmed: 23752158
Bass S, Pearce G, Bradney M, et al. Exercise before puberty may confer residual benefits in bone density in adulthood: studies in active prepubertal and retired female gymnasts. J Bone Miner Res. 1998;13:500–7.
doi: 10.1359/jbmr.1998.13.3.500
Martin DD, Wit JM, Hochberg Z, et al. The use of bone age in clinical practice - part 1. Horm Res Paediatr. 2011;76(1):1–9. https://doi.org/10.1159/000329372 .
doi: 10.1159/000329372
pubmed: 21691054
Satoh M. Bone age: assessment methods and clinical applications. 2015. Clin Pediatr Endocrinol. 2015;24(4):143–52. Published online 2015 Oct 24. https://doi.org/10.1297/cpe.24.143 .
doi: 10.1297/cpe.24.143
pubmed: 26568655
pmcid: 4628949
Martin DD, Wit JM, Hochberg Z, et al. The use of bone age in clinical practice - part 2. Horm Res Paediatr. 2011;76(1):10–6. https://doi.org/10.1159/000329374 .
doi: 10.1159/000329374
pubmed: 21691055
Makarov MR, Jackson TJ, Smith CM, Jo CH, Birch JG. Timing of epiphysiodesis to correct leg-length discrepancy: a comparison of prediction methods. J Bone Joint Surg Am. 2018;100(14):1217–22. https://doi.org/10.2106/JBJS.17.01380 .
doi: 10.2106/JBJS.17.01380
pubmed: 30020127
Diméglio A, Charles YP, Daures JP, de Rosa V, Kaboré B. Accuracy of the Sauvegrain method in determining skeletal age during puberty. J Bone Joint Surg Am. 2005;87(8):1689–96.
pubmed: 16085606
Bitan FD, Veliskakis KP, Campbell BC. Differences in the Risser grading systems in the United States and France. Clin Orthop Relat Res. 2005;436:190–5. https://doi.org/10.1097/01.blo.0000160819.10767.88 .
doi: 10.1097/01.blo.0000160819.10767.88
Wittschieber D, Vieth V, Domnick C, Pfeiffer H, Schmeling A. The iliac crest in forensic age diagnostics: evaluation of the apophyseal ossification in conventional radiography. Int J Legal Med. 2013;127(2):473–9. https://doi.org/10.1007/s00414-012-0763-x .
doi: 10.1007/s00414-012-0763-x
pubmed: 23052440
Schmidt S, Schmeling A, Zwiesigk P, Pfeiffer H, Schulz R. Sonographic evaluation of apophyseal ossification of the iliac crest in forensic age diagnostics in living individuals. Int J Legal Med. 2011;125(2):271–6. https://doi.org/10.1007/s00414-011-0554-9 .
doi: 10.1007/s00414-011-0554-9
pubmed: 21360260
Mughal AM, Hassan N, Ahmed A. Bone age assessment methods: a critical review. Pak J Med Sci. 2014;30(1):211–5. https://doi.org/10.12669/pjms.301.4295 .
doi: 10.12669/pjms.301.4295
Su P, Zhang L, Peng Y, Liang A, Du K, Huang D. A histological and ultrastructural study of femoral head cartilage in a new type II collagenopathy. Int Orthop. 2010;34(8):1333–9. https://doi.org/10.1007/s00264-010-0985-9 .
doi: 10.1007/s00264-010-0985-9
pubmed: 20204389
pmcid: 2989094
Kaur G, Khandelwal N, Jasuja OP. Computed tomographic studies on ossification status of medial epiphysis of clavicle: effect of slice thickness and dose distribution. J Indian Acad Forensic Med. 32(4).
Schmidt S, Mühler M, Schmeling A, Reisinger W, Schulz R. Magnetic resonance imaging of the clavicular ossification. Int J Legal Med. 2007;121(4):321–4.
doi: 10.1007/s00414-007-0160-z
Hoerr NL. Radiographic atlas of skeletal development of the knee. Springfield: Charles C. Thomas; 1955.
Zafar AM, Nadeem N, Husen Y, Ahmad MN. An appraisal of Greulich-Pyle atlas for skeletal age assessment in Pakistan. J Pak Med Assoc. 2010;60(7):552–5.
pubmed: 20578605
Gaskin CM, Kahn SL, Bertozzi JC, Bunch PM. Skeletal development of the hand and wrist: a radiographic atlas and digital bone age companion: a radiographic atlas and digital bone age companion. Oxford: Oxford University Press; 2011.
Greulich WW, Pyle SI. Radiograph atlas of skeletal development of the hand and wrist. 2nd ed. Palo Alto: Stanford University Press; 1959.
Halabi SS, Prevedello LM, Kalpathy-Cramer J, et al. The RSNA pediatric bone age machine learning challenge. Radiology. 2019;290(2):498–503. https://doi.org/10.1148/radiol.2018180736 .
doi: 10.1148/radiol.2018180736
pubmed: 30480490
Mukaka MM. A guide to appropriate use of correlation coefficient in medical research. Malawi Med J. 2012;24(3):69–71.
pubmed: 23638278
pmcid: 3576830
Nwosu BU, Lee MM. Evaluation of short and tall stature in children. Am Fam Physician. 2008;78(5):597–604.
pubmed: 18788236
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):1374–80.
doi: 10.2214/AJR.17.18224
Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology. 2018;287(1):313–22.
doi: 10.1148/radiol.2017170236
Lee H, Tajmir S, Lee J, et al. Fully automated deep learning system for bone age assessment. J Digit Imaging. 2017;30(4):427–41.
doi: 10.1007/s10278-017-9955-8
Mutasa S, Chang PD, Ruzal-Shapiro C, Ayyala R. MABAL: a novel deep-learning architecture for machine-assisted bone age labeling. J Digit Imaging. 2018;31(4):513–9.
doi: 10.1007/s10278-018-0053-3
Kaplowitz PB, Slora EJ, Wasserman RC, Pedlow SE, Herman-Giddens ME. Earlier onset of puberty in girls: relation to increased body mass index and race. Pediatrics. 2001;108(2):347–53.
doi: 10.1542/peds.108.2.347
Herman-Giddens ME, Steffes J, Harris D, et al. Secondary sexual characteristics in boys: data from the pediatric research in office settings network. Pediatrics. 2012;130(5):e1058–68. https://doi.org/10.1542/peds.2011-3291 .
doi: 10.1542/peds.2011-3291
pubmed: 23085608
Ontell FK, Ivanovic M, Ablin DS, Barlow TW. Bone age in children of diverse ethnicity. Am J Roentgenol. 1996;167:1395.
doi: 10.2214/ajr.167.6.8956565
Loder RT, Estle DT, Morrison K, et al. Applicability of the Greulich and Pyle skeletal age standards to black and white children of today. Am J Dis Child. 1993;147:1329–33.
pubmed: 8249956
Zhang A, Sayre JW, Vachon L, et al. Racial differences in growth patterns of children assessed on the basis of bone age. Radiology. 2009;250:228–35.
doi: 10.1148/radiol.2493080468
Martin DD, Neuhof J, Jenni OG, et al. Automatic determination of left- and right-hand bone age in the first Zurich longitudinal study. Horm Res Paediatr. 2010;74:50–5.
doi: 10.1159/000313369
Thodberg HH. Clinical review: an automated method for determination of bone age. J Clin Endocrinol Metab. 2009;94:2239–44.
doi: 10.1210/jc.2008-2474
Thodberg HH, Jenni OG, Caflisch J, et al. Prediction of adult height based on automated determination of bone age. J Clin Endocrinol Metab. 2009;94:4868–74.
doi: 10.1210/jc.2009-1429
Thodberg HH, Kreiborg S, Juul A, et al. The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging. 2009;28:52–66.
doi: 10.1109/TMI.2008.926067
Tanner JM. Assessment of skeletal maturity and prediction of adult height (TW3 method). 3rd ed. London: W.B. Saunders; 2001.