{'fraction_non_alphanumeric': 0.03326996197718631, 'fraction_numerical': 0.025665399239543727, 'mean_word_length': 4.691891891891892, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Pediatric skeletal age: determination with neural networks.

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.