참고문헌
- Sarmah SS. Concept of artificial intelligence, its impact and emerging trends. Int Res J Eng Technol 2019;6:2164-2168
- Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun Acm 2017;60:84-90 https://doi.org/10.1145/3065386
- Yuksel K, Skarbek W. Convolutional and recurrent neural networks for face image analysis. Found Comput Decis Sci 2019;44:331-347 https://doi.org/10.2478/fcds-2019-0017
- Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42:60-88 https://doi.org/10.1016/j.media.2017.07.005
- Kushibar K, Valverde S, Gonzalez-Villa S, Bernal J, Cabezas M, Oliver A, et al. Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features. Med Image Anal 2018;48:177-186 https://doi.org/10.1016/j.media.2018.06.006
- Alvarez JD, Matias-Guiu JA, Cabrera-Martin MN, Risco-Martin JL, Ayala JL. An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders. BMC Bioinformatics 2019;20:491
- Hwang EJ, Park CM. Clinical implementation of deep learning in thoracic radiology: potential applications and challenges. Korean J Radiol 2020;21:511-525 https://doi.org/10.3348/kjr.2019.0821
- Wang K, Mamidipalli A, Retson T, Bahrami N, Hasenstab K, Blansit K, et al. Automated CT and MRI liver segmentation and biometry using a generalized convolutional neural network. Radiol Artif Intell 2019;1:180022
- Kiani A, Uyumazturk B, Rajpurkar P, Wang A, Gao R, Jones E, et al. Impact of a deep learning assistant on the histopathologic classification of liver cancer. NPJ Digit Med 2020;3:23
- McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature 2020;577:89-94 https://doi.org/10.1038/s41586-019-1799-6
- Son SJ, Song Y, Kim N, Do Y, Kwak N, Lee MS, et al. TW3-based fully automated bone age assessment system using deep neural networks. IEEE Access 2019;7:33346-33358 https://doi.org/10.1109/ACCESS.2019.2903131
- Spampinato C, Palazzo S, Giordano D, Aldinucci M, Leonardi R. Deep learning for automated skeletal bone age assessment in X-ray images. Med Image Anal 2017;36:41-51 https://doi.org/10.1016/j.media.2016.10.010
- Lee H, Tajmir S, Lee J, Zissen M, Yeshiwas BA, Alkasab TK, et al. Fully automated deep learning system for bone age assessment. J Digit Imaging 2017;30:427-441 https://doi.org/10.1007/s10278-017-9955-8
- Tajmir SH, Lee H, Shailam R, Gale HI, Nguyen JC, Westra SJ, et al. Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability. Skeletal Radiol 2019;48:275-283 https://doi.org/10.1007/s00256-018-3033-2
- 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;209:1374-1380 https://doi.org/10.2214/AJR.17.18224
- Mansourvar M, Ismail MA, Herawan T, Raj RG, Kareem SA, Nasaruddin FH. Automated bone age assessment: motivation, taxonomies, and challenges. Comput Math Methods Med 2013;2013:391626
- Greulich WW, Pyle SI. Radiographic atlas of skeletal development of the hand and wrist, 1st ed. Stanford: Stanford University Press, 1959
- Gilsanz V, Ratib O. A digital atlas of skeletal maturity. Berlin: Springer, 2011
- Tanner JM, Healy MJ, Goldstein H, Cameron N. Assessment of skeletal maturity and prediction of adult height: TW3 method. London: W.B Saunders Company, 2001
- Khan KM, Miller BS, Hoggard E, Somani A, Sarafoglou K. Application of ultrasound for bone age estimation in clinical practice. J Pediatr 2009;154:243-247 https://doi.org/10.1016/j.jpeds.2008.08.018
- Hojreh A, Gamper J, Schmook MT, Weber M, Prayer D, Herold CJ, et al. Hand MRI and the Greulich-Pyle atlas in skeletal age estimation in adolescents. Skeletal Radiol 2018;47:963-971 https://doi.org/10.1007/s00256-017-2867-3
- Mettler FA Jr, Huda W, Yoshizumi TT, Mahesh M. Effective doses in radiology and diagnostic nuclear medicine: a catalog. Radiology 2008;248:254-263 https://doi.org/10.1148/radiol.2481071451
- Martin DD, Wit JM, Hochberg Z, Savendahl L, van Rijn RR, Fricke O, et al. The use of bone age in clinical practice-part 1. Horm Res Paediatr 2011;76:1-9 https://doi.org/10.1159/000329372
- van Rijn RR, Thodberg HH. Bone age assessment: automated techniques coming of age? Acta Radiol 2013;54:1024-1029 https://doi.org/10.1258/ar.2012.120443
- Roche AF, Rohmann CG, French NY, Davila GH. Effect of training on replicability of assessments of skeletal maturity (Greulich-Pyle). Am J Roentgenol Radium Ther Nucl Med 1970;108:511-515 https://doi.org/10.2214/ajr.108.3.511
- Michael DJ, Nelson AC. HANDX: a model-based system for automatic segmentation of bones from digital hand radiographs. IEEE Trans Med Imaging 1989;8:64-69 https://doi.org/10.1109/42.20363
- Pietka E, McNitt-Gray MF, Kuo ML, Huang HK. Computer-assisted phalangeal analysis in skeletal age assessment. IEEE Trans Med Imaging 1991;10:616-620 https://doi.org/10.1109/42.108597
- 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;42:288-294 https://doi.org/10.1159/000184211
- Yoo JW, Lee JM, Kim WY. A bone age assessment method based on normalized shape model. J Korea Multimed Soc 2009;12:383-396
- Oakden-Rayner L. The rebirth of CAD: how is modern AI different from the CAD we know? Radiology: Artificial Intelligence 2019;1:e180089
- Fujita H. AI-based computer-aided diagnosis (AI-CAD): the latest review to read first. Radiol Phys Technol 2020;13:6-19 https://doi.org/10.1007/s12194-019-00552-4
- Hu TH, Wan L, Liu TA, Wang MW, Chen T, Wang YH. Advantages and application prospects of deep learning in image recognition and bone age assessment. Fa Yi Xue Za Zhi 2017;33:629-634
- Dallora AL, Anderberg P, Kvist O, Mendes E, Diaz Ruiz S, Sanmartin Berglund J. Bone age assessment with various machine learning techniques: a systematic literature review and meta-analysis. PLoS One 2019;14:e0220242
- Cunha P, Moura DC, Guevara Lopez MA, Guerra C, Pinto D, Ramos I. Impact of ensemble learning in the assessment of skeletal maturity. J Med Syst 2014;38:87
- O'Connor JE, Coyle J, Bogue C, Spence LD, Last J. Age prediction formulae from radiographic assessment of skeletal maturation at the knee in an Irish population. Forensic Sci Int 2014;234:188.e1-8 https://doi.org/10.1016/j.forsciint.2013.08.024
- Tang FH, Chan JLC, Chan BKL. Accurate age determination for adolescents using magnetic resonance imaging of the hand and wrist with an artificial neural network-based approach. J Digit Imaging 2019;32:283-289 https://doi.org/10.1007/s10278-018-0135-2
- Kashif M, Deserno TM, Haak D, Jonas S. Feature description with SIFT, SURF, BRIEF, BRISK, or FREAK? A general question answered for bone age assessment. Comput Biol Med 2016;68:67-75 https://doi.org/10.1016/j.compbiomed.2015.11.006
- Hillewig E, Degroote J, Van der Paelt T, Visscher A, Vandemaele P, Lutin B, et al. Magnetic resonance imaging of the sternal extremity of the clavicle in forensic age estimation: towards more sound age estimates. Int J Legal Med 2013;127:677-689 https://doi.org/10.1007/s00414-012-0798-z
- Urschler M, Grassegger S, Stern D. What automated age estimation of hand and wrist MRI data tells us about skeletal maturation in male adolescents. Ann Hum Biol 2015;42:358-367 https://doi.org/10.3109/03014460.2015.1043945
- Harmsen M, Fischer B, Schramm H, Seidl T, Deserno TM. Support vector machine classification based on correlation prototypes applied to bone age assessment. IEEE J Biomed Health Inform 2013;17:190-197 https://doi.org/10.1109/TITB.2012.2228211
- Mansourvar M, Shamshirband S, Raj RG, Gunalan R, Mazinani I. An automated system for skeletal maturity assessment by extreme learning machines. PLoS One 2015;10:e0138493
- Thodberg HH, Kreiborg S, Juul A, Pedersen KD. The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging 2009;28:52-66 https://doi.org/10.1109/TMI.2008.926067
- Booz C, Yel I, Wichmann JL, Boettger S, Al Kamali A, Albrecht MH, et al. Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method. Eur Radiol Exp 2020;4:6
- Bui TD, Lee JJ, Shin J. Incorporated region detection and classification using deep convolutional networks for bone age assessment. Artif Intell Med 2019;97:1-8 https://doi.org/10.1016/j.artmed.2019.04.005
- Ontell FK, Ivanovic M, Ablin DS, Barlow TW. Bone age in children of diverse ethnicity. AJR Am J Roentgenol 1996;167:1395-1398 https://doi.org/10.2214/ajr.167.6.8956565
- Thodberg HH, Savendahl L. Validation and reference values of automated bone age determination for four ethnicities. Acad Radiol 2010;17:1425-1432
- Margalit A, Cottrill E, Nhan D, Yu L, Tang X, Fritz J, et al. The spatial order of physeal maturation in the normal human knee using magnetic resonance imaging. J Pediatr Orthop 2019;39:e318-e322 https://doi.org/10.1097/BPO.0000000000001298
- Fan F, Zhang K, Peng Z, Cui JH, Hu N, Deng ZH. Forensic age estimation of living persons from the knee: comparison of MRI with radiographs. Forensic Sci Int 2016;268:145-150 https://doi.org/10.1016/j.forsciint.2016.10.002
- Stern D, Payer C, Urschler M. Automated age estimation from MRI volumes of the hand. Med Image Anal 2019;58:101538
- Dallora AL, Berglund JS, Brogren M, Kvist O, Diaz Ruiz S, Dubbel A, et al. Age assessment of youth and young adults using magnetic resonance imaging of the knee: a deep learning approach. JMIR Med Inform 2019;7:e16291