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27.04.2024 | Pediatric

Olecranon bone age assessment in puberty using a lateral elbow radiograph and a deep-learning model

verfasst von: Gayoung Choi, Sungwon Ham, Bo-Kyung Je, Young-Jun Rhie, Kyung-Sik Ahn, Euddeum Shim, Mi-Jung Lee

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Abstract

Objectives

To improve pubertal bone age (BA) evaluation by developing a precise and practical elbow BA classification using the olecranon, and a deep-learning AI model.

Materials and methods

Lateral elbow radiographs taken for BA evaluation in children under 18 years were collected from January 2020 to June 2022, retrospectively. A novel classification and the olecranon BA were established based on the morphological changes in the olecranon ossification process during puberty. The olecranon BA was compared with other elbow and hand BA methods, using intraclass correlation coefficients (ICCs), and a deep-learning AI model was developed.

Results

A total of 3508 lateral elbow radiographs (mean age 9.8 ± 1.8 years) were collected. The olecranon BA showed the highest applicability (100%) and interobserver agreement (ICC 0.993) among elbow BA methods. It showed excellent reliability with Sauvegrain (0.967 in girls, 0.969 in boys) and Dimeglio (0.978 in girls, 0.978 in boys) elbow BA methods, as well as Korean standard (KS) hand BA in boys (0.917), and good reliability with KS in girls (0.896) and Greulich–Pyle (GP)/Tanner–Whitehouse (TW)3 (0.835 in girls, 0.895 in boys) hand BA methods. The AI model for olecranon BA showed an accuracy of 0.96 and a specificity of 0.98 with EfficientDet-b4. External validation showed an accuracy of 0.86 and a specificity of 0.91.

Conclusion

The olecranon BA evaluation for puberty, requiring only a lateral elbow radiograph, showed the highest applicability and interobserver agreement, and excellent reliability with other BA evaluation methods, along with a high performance of the AI model.

Clinical relevance statement

This AI model uses a single lateral elbow radiograph to determine bone age for puberty from the olecranon ossification center and can improve pubertal bone age assessment with the highest applicability and excellent reliability compared to previous methods.

Key Points

Elbow bone age is valuable for pubertal bone age assessment, but conventional methods have limitations.
Olecranon bone age and its AI model showed high performances for pubertal bone age assessment.
Olecranon bone age system is practical and accurate while requiring only a single lateral elbow radiograph.

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Creo AL, Schwenk 2nd WF (2017) Bone age: a handy tool for pediatric providers. Pediatrics 140:e20171486CrossRefPubMed

Cavallo F, Mohn A, Chiarelli F, Giannini C (2021) Evaluation of bone age in children: a mini-review. Front Pediatr 9:580314CrossRefPubMedPubMedCentral

Rubin DA (2021) Assessing bone age: a paradigm for the next generation of artificial intelligence in radiology. Radiology 301:700–701CrossRefPubMed

Greulich WW, Pyle SI (1959) Radiographic atlas of skeletal development of the hand and wrist, 2nd edn. Stanford University Press, Stanford, Calif

Mourilyan TJ, Tanner JM (2001) Assessment of skeletal maturity and prediction of adult height (TW3 method), 3rd edn. W.B. Saunders, London

Tanner JM (1983) Assessment of skeletal maturity and prediction of adult height (TW2 method), 2nd edn. Academic Press, London; New York

Lee SY, Im SA (2022) Comparison of bone ages in early puberty: computerized Greulich-Pyle based bone age vs. Sauvegrain method. J Korean Soc Radiol 83:1081–1089CrossRefPubMed

Dimeglio A, Charles YP, Daures JP et al (2005) Accuracy of the Sauvegrain method in determining skeletal age during puberty. J Bone Joint Surg Am 87:1689–1696PubMed

Sauvegrain J, Nahum H, Bronstein H (1962) Study of bone maturation of the elbow. Ann Radiol (Paris) 5:542–550PubMed

10.

Charles YP, Dimeglio A, Canavese F, Daures JP (2007) Skeletal age assessment from the olecranon for idiopathic scoliosis at risser grade 0. J Bone Joint Surg Am 89a:2737–2744CrossRef

11.

Halabi SS, Prevedello LM, Kalpathy-Cramer J et al (2019) The RSNA pediatric bone age machine learning challenge. Radiology 290:498–503CrossRefPubMed

12.

Wang X, Zhou B, Gong P et al (2022) Artificial intelligence-assisted bone age assessment to improve the accuracy and consistency of physicians with different levels of experience. Front Pediatr 10:818061CrossRefPubMedPubMedCentral

13.

Eng DK, Khandwala NB, Long J et al (2021) Artificial intelligence algorithm improves radiologist performance in skeletal age assessment: a prospective multicenter randomized controlled trial. Radiology 301:692–699CrossRefPubMed

14.

Ahn K-S, Bae B, Jang WY et al (2021) Assessment of rapidly advancing bone age during puberty on elbow radiographs using a deep neural network model. Eur Radiol 31:8947–8955CrossRefPubMed

15.

Lee K-C, Lee K-H, Kang CH et al (2021) Clinical validation of a deep learning-based hybrid (Greulich-Pyle and Modified Tanner-Whitehouse) method for bone age assessment. Korean J Radiol 22:2017–2025CrossRefPubMedPubMedCentral

16.

Kan JH, Strouse PJ (2019) Embryology, Anatomy, and Normal Findings. In: Coley BD (ed) Caffey’s pediatric diagnostic imaging. Elsevier, Philadelphia, PA, p 1219–1236

17.

Anthimopoulos M, Christodoulidis S, Ebner L et al (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35:1207–1216CrossRefPubMed

18.

Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313:504–507CrossRefPubMed

19.

Zhu Z, Albadawy E, Saha A et al (2019) Deep learning for identifying radiogenomic associations in breast cancer. Comput Biol Med 109:85–90CrossRefPubMedPubMedCentral

20.

Cai Z, Vasconcelos N (2021) Cascade R-CNN: high quality object detection and instance segmentation. IEEE Trans Pattern Anal Mach Intell 43:1483–1498CrossRefPubMed

21.

Zhang J, Xie Z, Sun J et al (2020) A cascaded R-CNN with multiscale attention and imbalanced samples for traffic sign detection. IEEE Access 8:29742–29754CrossRef

22.

Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:200410934

23.

Tan M, Pang R, Le QV (2020) Efficientdet: scalable and efficient object detection Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 10781–10790

24.

Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15:155–163CrossRefPubMedPubMedCentral

25.

Leeflang M, Deeks JJ, Gatsonis C, Bossuyt P (2008) Cochrane diagnostic test accuracy working G. systematic reviews of diagnostic test accuracy. Ann Intern Med 149:889–897CrossRefPubMedPubMedCentral

26.

Pepe MS (2003) The statistical evaluation of medical tests for classification and prediction. Oxford University Press, USACrossRef

27.

Zhou X-h, McClish DK, Obuchowski NA (2011) Statistical methods in diagnostic medicine, 2nd edn. Wiley, Hoboken, N.J.

28.

Dimeglio A (2001) Growth in pediatric orthopaedics. J Pediatr Orthop 21:549–555CrossRefPubMed

29.

Little DG, Song KM, Katz D, Herring JA (2000) Relationship of peak height velocity to other maturity indicators in idiopathic scoliosis in girls. J Bone Joint Surg Am 82:685–693CrossRefPubMed

30.

Canavese F, Charles YP, Dimeglio A et al (2014) A comparison of the simplified olecranon and digital methods of assessment of skeletal maturity during the pubertal growth spurt. Bone Joint J 96-b:1556–1560CrossRefPubMed

31.

Cheng JC, Leung SS, Chiu BS et al (1998) Can we predict body height from segmental bone length measurements? A study of 3647 children. J Pediatr Orthop 18:387–393CrossRefPubMed

32.

Lovell WW, Weinstein SL, Flynn JM (2014) Lovell and Winter’s pediatric orthopaedics, 7th edn. Wolters Kluwer Health/Lippincott Williams & Wilkins, Philadelphia

33.

Greene JD, Yu K, Li DT et al (2021) The relationship of olecranon apophyseal ossification and sanders hand scores with the timing of peak height velocity in adolescents. J Bone Joint Surg Am 103:1543–1551CrossRefPubMed

34.

Canavese F, Charles YP, Dimeglio A (2008) Skeletal age assessment from elbow radiographs. Review of the literature. Musculoskeletal Surg 92:1

Titel: Olecranon bone age assessment in puberty using a lateral elbow radiograph and a deep-learning model
verfasst von: Gayoung Choi
Sungwon Ham
Bo-Kyung Je
Young-Jun Rhie
Kyung-Sik Ahn
Euddeum Shim
Mi-Jung Lee
Publikationsdatum: 27.04.2024
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-024-10748-x

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Springer Medizin

Olecranon bone age assessment in puberty using a lateral elbow radiograph and a deep-learning model

Abstract

Objectives

Materials and methods

Results

Conclusion

Clinical relevance statement

Key Points

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Update Radiologie

Springer Medizin

Abstract

Objectives

Materials and methods

Results

Conclusion

Clinical relevance statement

Key Points

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Neu im Fachgebiet Radiologie

Darf man die Behandlung eines Neonazis ablehnen?

Ein Drittel der jungen Ärztinnen und Ärzte erwägt abzuwandern

Endlich: Zi zeigt, mit welchen PVS Praxen zufrieden sind

Akuter Schwindel: Wann lohnt sich eine MRT?

Update Radiologie