  This preliminary study focuses on the development of a medical image
segmentation algorithm based on artificial intelligence for calculating bone
growth in contact with metallic implants. %as a result of the problem of
estimating the growth of new bone tissue due to artifacts. %the presence of
various types of distortions and errors, known as artifacts.
  Two databases consisting of computerized microtomography images have been
used throughout this work: 100 images for training and 196 images for testing.
Both bone and implant tissue were manually segmented in the training data set.
The type of network constructed follows the U-Net architecture, a convolutional
neural network explicitly used for medical image segmentation.
  In terms of network accuracy, the model reached around 98\%. Once the
prediction was obtained from the new data set (test set), the total number of
pixels belonging to bone tissue was calculated. This volume is around 15\% of
the volume estimated by conventional techniques, which are usually
overestimated. This method has shown its good performance and results, although
it has a wide margin for improvement, modifying various parameters of the
networks or using larger databases to improve training.
