Pediatric skeletal age: determination with neural networks.
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.
Gross
G W
GW
Department of Radiology, Jefferson Medical College, Thomas Jefferson University, Philadelphia, Pa, USA.
Boone
J M
JM
Bishop
D M
DM
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