Gross G W, Boone J M, Bishop D M
Department of Radiology, Jefferson Medical College, Thomas Jefferson University, Philadelphia, Pa, USA.
Radiology. 1995 Jun;195(3):689-95. doi: 10.1148/radiology.195.3.7753995.
To develop a neural network to calculate skeletal age based on measurements taken from digitized hand radiographs.
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.
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%).
A simple neural network may assist radiologists in the assessment of skeletal age.
开发一种神经网络，基于数字化手部X光片的测量数据来计算骨骼年龄。
从521例健康患者的手部X光片数据库中，从7项线性测量数据计算出4个参数，并使用留一法训练神经网络以计算骨骼年龄。将结果与一位经验丰富的儿科放射科医生使用标准儿科骨骼图谱得出的结果进行比较。
神经网络得出的与生物年龄的平均差值为-0.261岁±1.82（标准差），放射科医生得出的为-0.232岁±1.54；该差异无统计学意义（P = 0.67，Wilcoxon符号秩检验）。在521例病例中的243例（47%）中，神经网络确定的骨骼年龄比放射科医生确定的更接近生物年龄。
一种简单的神经网络可能有助于放射科医生评估骨骼年龄。
