Deep learning for automated skeletal bone age assessment in X-ray images

@article{Spampinato2017DeepLF,
  title={Deep learning for automated skeletal bone age assessment in X-ray images},
  author={Concetto Spampinato and Simone Palazzo and Daniela Giordano and Marco Aldinucci and Rosalia Leonardi},
  journal={Medical image analysis},
  year={2017},
  volume={36},
  pages={
          41-51
        }
}
Skeletal bone age assessment is a common clinical practice to investigate endocrinology, genetic and growth disorders in children. It is generally performed by radiological examination of the left hand by using either the Greulich and Pyle (G&P) method or the Tanner-Whitehouse (TW) one. However, both clinical procedures show several limitations, from the examination effort of radiologists to (most importantly) significant intra- and inter-operator variability. To address these problems, several… CONTINUE READING

Citations

Publications citing this paper.
Showing 1-10 of 20 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 49 references

Automated bone age

  • L. Davis, Theobald, B.-J, A. Bagnall
  • 2012
Highly Influential
5 Excerpts

Similar Papers

Loading similar papers…