  The bone age, reflecting the degree of development of the bones, can be used
to predict the adult height and detect endocrine diseases of children. Both
examinations of radiologists and variability of operators have a significant
impact on bone age assessment. To decrease human intervention , machine
learning algorithms are used to assess the bone age automatically. However,
conventional supervised deep-learning methods need pre-labeled data. In this
paper, based on the convolutional auto-encoder with constraints (CCAE), an
unsupervised deep-learning model proposed in the classification of the
fingerprint, we propose this model for the classification of the bone age and
baptize it BA-CCAE. In the proposed BA-CCAE model, the key regions of the raw
X-ray images of the bone age are encoded, yielding the latent vectors. The
K-means clustering algorithm is used to obtain the final classifications by
grouping the latent vectors of the bone images. A set of experiments on the
Radiological Society of North America pediatric bone age dataset (RSNA) show
that the accuracy of classifications at 48-month intervals is 76.15%. Although
the accuracy now is lower than most of the existing supervised models, the
proposed BA-CCAE model can establish the classification of bone age without any
pre-labeled data, and to the best of our knowledge, the proposed BA-CCAE is one
of the few trails using the unsupervised deep-learning method for the bone age
assessment.
