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Pediatric skeletal age: determination with neural networks.

机译：小儿骨骼年龄：用神经网络确定。

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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.
机译：目的：建立一个神经网络，根据数字化的手部X光片的测量结果计算骨骼年龄。材料与方法：从健康患者获得的521张手部X射线照片的数据库中，从七个线性测量值中计算出四个参数，并使用折刀法来训练神经网络以计算骨骼年龄。将结果与使用标准儿科骨骼图谱的经验丰富的儿科放射科医生进行了比较。结果：神经网络与生物学年龄的平均差异为-0.261年+/- 1.82（标准差），而放射科医生的平均差异为-0.232年+/- 1.54;这种差异没有显着差异（P = 0.67，Wilcoxon符号秩检验）。在521例病例中，有243例（47％）由神经网络确定的骨骼年龄比放射科医师确定的更接近生物学年龄。结论：一个简单的神经网络可以帮助放射科医生评估骨骼年龄。

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