Using Deep Learning For Estimation of Time-since-injury in Pediatric Accidental Fractures.

Details

• Alternative Title:
  Pediatric Radiology, 2025, v. 55, no. 6: Using Deep Learning For Estimation of Time-since-injury in Pediatric Accidental Fractures.
• Journal Article:
  Pediatric Radiology
• Personal Author:
  Brink, Farah W. ; Adler, Brent ; Bambach, Sven ; Lo, Charmaine B. ; Rust, Steven ; Bartlett, Christopher W. ; Bradshaw, Logan ; Henry, M. Katherine ; Messer, Diana
• Description:
  Estimating time-since-injury of healing fractures is imprecise, encompassing excessively wide timeframes. Most injured children are evaluated at non-children's hospitals, yet pediatric radiologists can disagree with up to one in six skeletal imaging interpretations from referring community hospitals. There is a need to improve image interpretation by considering additional methods for fracture dating.
  
  To train and validate deep learning models to correctly estimate the age of pediatric accidental long bone fractures.
  
  This secondary data analysis used radiographic images of accidental long bone fractures in children <6 years at the time of injury seen at a large Midwestern children's hospital between 2000-2016. We built deep learning models both to classify fracture images into different age groups and to directly estimate fracture age (time-since-injury). We used cross-validation to evaluate model performance across various metrics, including confusion matrices, sensitivity/specificity, and activation maps for age classification, and mean absolute error (MAE) and root mean squared error (RMSE) for age estimation.
  
  Our study cohort contained 2,328 radiographs from 399 patients. Overall, our models performed above baselines for fracture age classification and estimation, both when trained/validated across all bones and on specific bone types. The best model was able to estimate fracture age for any long bone with a MAE of 6.2 days and with 68% of estimates falling within 7 days of the correct fracture age.
  
  Our study successfully demonstrated that, for radiographic dating of accidental fractures of long bones, deep learning models can estimate time-since-injury with above-baseline accuracy.
• Content Notes:
  Author manuscript
• Subjects:
  • Child
  • Child, Preschool
  • Humans
  • Infant
  • Clavicle
  • Deep Learning
  • Feasibility Studies
  • Fractures, Bone
  • Image Processing, Computer-Assisted
  • Radiography
  More +
• Keywords:
  • Artificial intelligence
  • Bone
  • Children
  • Dating
  • Deep learning
  • Fractures
  • Radiographs
• Source:
  Pediatric Radiology, 2025, v. 55, no. 6
• DOI:
  https://doi.org/10.1007/s00247-025-06223-4
• ISSN:
  0301-0449 ; 1432-1998
• Pubmed ID:
  40258953
• Pubmed Central ID:
  PMC12306432
• Document Type:
  Text
• Funding:
  K08 HS028847/HS/AHRQ HHSUnited States/ ; R49 CE003074/CE/NCIPC CDC HHSUnited States/ ; 1R49CE003074-01/CC/CDC HHSUnited States/ ; K08HS028847/HS/AHRQ HHSUnited States/ ; Nationwide Children's Hospital/
• Genre:
  Journal Article
• Pages in Document:
  1257-1269 (21 pdf pages)
• Volume:
  55
• Issue:
  6
• Download URL:
  https://stacks.cdc.gov/view/cdc/261468/cdc_261468_DS1.pdf
• File Type:
  [PDF - 3.18 MB]
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha-512:5386c448ea100d3a1dc7bb5df07b32fc6f7c84e7557ec831bf7a43b92819f36f2887d84681652d53ef6781b1871517dc8eb4eacbcf977380c0f412a88d8b69cf