title: Pediatric skeletal age: determination with neural networks.
abstract: OBJECTIVE
  To develop a neural network to calculate skeletal age based on measurements taken from digitized hand radiographs.
  
  
  METHODS
  From a database of 521 hand radiographs obtained in healthy patients, four parameters were calculated from seven linear measurements and were used to train a neural network, with use of the jackknife method, to calculate skeletal age. The results were compared with those of an experienced pediatric radiologist using a standard pediatric skeletal atlas.
  
  
  RESULTS
  The mean difference from biologic age for the neural network was -0.261 years +/- 1.82 (standard deviation) and for the radiologist, -0.232 years +/- 1.54; this difference was not significantly different (P = .67, Wilcoxon signed rank test). Skeletal age determined by the neural network was closer to the biologic age than that assigned by the radiologist in 243 of 521 cases (47%).
  
  
  CONCLUSIONS
  A simple neural network may assist radiologists in the assessment of skeletal age.
authors.affiliation: Department of Radiology, Jefferson Medical College, Thomas Jefferson University, Philadelphia, Pa, USA.
authors.forename: G W
authors.initials: GW
authors.lastname: Gross
authors.forename: J M
authors.initials: JM
authors.lastname: Boone
authors.forename: D M
authors.initials: DM
authors.lastname: Bishop
mesh_terms: D000293:Adolescent; D000365:Age Determination by Skeleton; D001842:Bone and Bones / Q000000981:diagnostic imaging; D002648:Child; D002675:Child, Preschool; D005260:Female; D006225:Hand / Q000000981:diagnostic imaging; D006801:Humans; D007223:Infant; D008297:Male; D016571:Neural Networks, Computer; D012189:Retrospective Studies