Using deep learning for estimation of time-since-injury in pediatric accidental fractures

Messer DL, Adler BH, Brink FW et al (2020) Radiographic timelines for pediatric healing fractures: a systematic review. Pediatr Radiol 50(8):1041–1048

Article  PubMed  Google Scholar 

Kleinman PK (Ed.) (2015) Diagnostic imaging of child abuse (3rd ed.). Cambridge University Press. Chapter 6: Dating fractures

Yeo LI, Reed MH (1994) Staging of healing of femoral fractures in children. Canadian Assoc Radiol J J Assoc Canadienne Des Radiol 45(1):16–19

Islam O, Soboleski D, Symons S et al (2000) Development and duration of radiographic signs of bone healing in children. AJR Am J Roentgenol 175(1):75–78

Article  CAS  PubMed  Google Scholar 

Malone CA, Sauer NJ, Fenton TW (2011) A radiographic assessment of pediatric fracture healing and time since injury. J Forensic Sci 56(5):1123–1130

Article  PubMed  Google Scholar 

Halliday KE, Broderick NJ, Somers JM, Hawkes R (2011) Dating fractures in infants. Clin Radiol 66(11):1049–1054

Article  CAS  PubMed  Google Scholar 

Prosser I, Lawson Z, Evans A et al (2012) A timetable for the radiologic features of fracture healing in young children. AJR Am J Roentgenol 198(5):1014–1020

Article  PubMed  Google Scholar 

Warner C, Maguire S, Trefan L et al (2017) A study of radiological features of healing in long bone fractures among infants less than a year. Skeletal Radiol 46(3):333–341

Article  PubMed  Google Scholar 

Crompton S, Messina F, Klafkowski G et al (2021) Validating scoring systems for fracture healing in infants and young children: pilot study. Pediatr Radiol 51:1682–1689

Article  PubMed  PubMed Central  Google Scholar 

Karmazyn B, Wanner MR, Marine MB et al (2019) The added value of a second read by pediatric radiologists for outside skeletal surveys. Pediatr Radiol 49(2):203–209

Article  PubMed  Google Scholar 

Densmore JC, Lim HJ, Oldham KT, Guice KS (2006) Outcomes and delivery of care in pediatric injury. J Pediatr Surge 41(1):92–98

Article  Google Scholar 

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

Article  CAS  PubMed  Google Scholar 

Zhou SK, Greenspan H, Davatzikos C et al (2021) A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises. Proc IEEE Inst Electr Electr Eng 109(5):820–838

Article  CAS  Google Scholar 

Tsai A, Grant PE, Warfield SK et al (2022) Deep learning of birth-related infant clavicle fractures: a potential virtual consultant for fracture dating. Pediatr Radiol 52(11):2206–2214

Article  PubMed  Google Scholar 

Tejani AS, Klontzas ME, Gatti AA et al (2024) Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 update. Radiol Artif Intell 6(4):e240300

Article  PubMed  PubMed Central  Google Scholar 

Messer DL (2019) Variables influencing time since injury of pediatric healing fractures; radiographic assessment and implications for child physical abuse [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. https://etd.ohiolink.edu/acprod/odb_etd/etd/r/1501/10?clear=10&p10_accession_num=osu1574408203228578

He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp 770-778

Kornblith S, Shlens J, Le QV (2019) Do better imagenet models transfer better?. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition pp 2661-2671

Paddock M, Choudhary AK, Jeanes A et al (2023) Controversial aspects of imaging in child abuse: a second roundtable discussion from the ESPR child abuse taskforce. Pediatr Radiol 53(4):739–751

Article  PubMed  PubMed Central  Google Scholar 

Paszke A, Gross S, Massa F et al (2019) PyTorch: an imperative style, high-performance deep learning library. ArXiv, abs/1912.01703

Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. ArXiv preprint. arXiv:1412.6980

Selvaraju RR, Cogswell M, Das A et al (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision pp 618-626

Niu Z, Zhou M, Wang L et al (2016) Ordinal regression with multiple output CNN for age estimation. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE pp 4920–4928

Cao W, Mirjalili V, Raschka S (2020) Rank consistent ordinal regression for neural networks with application to age estimation. Pattern Recog Lett 140:325–331

Article  Google Scholar 

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