2. Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. Radiographics 2017;37:2113–31.
4. Greulich WW, Pyle SI. Radiographic atlas of skeletal development of the hand and wrist. 2nd ed. Stanford (CA): Stanford University Press. 1959.
5. Tanner JM, Healy MJR, Goldstein H, Cameron N. Assessment of skeletal maturity and prediction of adult height (TW3 method). 3rd ed. London: W.B. Saunders. 2001.
6. Hao PY, Chokuwa S, Xie XH, Wu FL, Wu J, Bai C. Skeletal bone age assessments for young children based on regression convolutional neural networks. Math Biosci Eng 2019;16:6454–66.
7. Chai T, Draxler RR. Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature. Geosci Model Dev 2014;7:1247–50.
9. Masood A, Sheng B, Li P, Hou X, Wei X, Qin J, et al. Computer-assisted decision support system in pulmonary cancer detection and stage classification on CT images. J Biomed Inform 2018;79:117–28.
15. 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;287:313–22.
17. Kim JR, Shim WH, Yoon HM, Hong SH, Lee JS, Cho YA, et al. Computerized bone age estimation using deep learning based program: evaluation of the accuracy and efficiency. AJR Am J Roentgenol 2017;209:1374–80.
19. Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 2017;284:574–82.
20. Boeyer ME, Leary EV, Sherwood RJ, Duren DL. Evidence of the non-linear nature of skeletal maturation. Arch Dis Child 2020;105:631–8.
21. Eitel KB, Eugster EA. Differences in bone age readings between pediatric endocrinologists and radiologists. Endocr Pract 2020;26:328–31.