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This preliminary study focuses on the development of a medical imagesegmentation algorithm based on artificial intelligence for calculating bonegrowth in contact with metallic implants. %as a result of the problem ofestimating the growth of new bone tissue due to artifacts. %the presence ofvarious types of distortions and errors, known as artifacts.  Two databases consisting of computerized microtomography images have beenused 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 convolutionalneural network explicitly used for medical image segmentation.  In terms of network accuracy, the model reached around 98\%. Once theprediction was obtained from the new data set (test set), the total number ofpixels belonging to bone tissue was calculated. This volume is around 15\% ofthe volume estimated by conventional techniques, which are usuallyoverestimated. This method has shown its good performance and results, althoughit has a wide margin for improvement, modifying various parameters of thenetworks or using larger databases to improve training.

Development of an algorithm for medical image segmentation of bone tissue in interaction with metallic implants