Pediatric skeletal age: Determination with neural networks

G. W. Gross, John M Boone, D. M. Bishop

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

PURPOSE: To develop a neural network to calculate skeletal age based on measurements taken from digitized hand radiographs. MATERIALS AND 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%). CONCLUSION: A simple neural network may assist radiologists in the assessment of skeletal age.

Original languageEnglish (US)
Pages (from-to)689-695
Number of pages7
JournalRadiology
Volume195
Issue number3
StatePublished - 1995

Keywords

  • Bones, growth and development
  • Children, skeletal system
  • Computers, neural network

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology

Fingerprint Dive into the research topics of 'Pediatric skeletal age: Determination with neural networks'. Together they form a unique fingerprint.

Cite this