A novel method using neural networks for age estimation in children

In this study, an automated system using a novel method and artificial neural networks (ANN) to assess bone age of children from hand radiographs is proposed. Bone age is estimated accurately by utilizing distal radius, ulna and their epiphysis for skeletal maturity assessment. With the system designed using the method and ANN, bone age assessment become possible without any guidance of radiologists and applicable in a very short time. Not only sharp and high quality radiographs but also degraded ones can be used for skeletal maturation assessment in this system. Moreover, the system is not required the radiographs exposed in any exact standard, angle or distance. The proposed system is tested with 32 hand radiographs of various ages which are assessed by two radiologists. As a result, bone ages are assessed mean error of 0.52 year by the system.