The present invention relates to a computer-implemented method for clustering defects of defective bones and manufacturing a patient-specific
orthopaedic implant, including at least the following steps: - acquiring
computed tomography CT images of a defective or damaged bone and feeding a
database; - given a set of defective bones retrieved from said defective or damaged bone
database, reconstructing a synthetic shape of the original shape of the defective bone; - computing a
bone erosion depth map of said synthetic shape of the original shape of the defective bone, where said
bone erosion depth map, for a given defective bone, comprises a set of
erosion values over the
region of interest of the reconstructed bone, defining an
erosion vector; - transforming said
erosion depth map into an erosion matrix which comprises the erosion vectors of the defective bones and the vertices over the surface of the
region of interest of said synthetic shape of the original shape of the defective bone; - feeding said erosion matrix to an unsupervised
machine learning
algorithm that groups data into a tree of nested clusters, to identify clusters of relevant bone defects and to generate synthetic three-dimensional bone models representative of each defect cluster; - associating a defect
label to each cluster; - training a defect classifier using said erosion matrix and said defect
label to classify clusters of defects of the bones; - feeding a CAD environment with the outputs of said defect classifier; - creating the
orthopaedic implant based on the outputs fed to the CAD environment.