A computer-implemented method for automated 3D root shape prediction comprising: a pre-processor receiving 3D data defined in a voxel space, the voxel space defining a 3D volume, the voxel space comprising a voxel representation of a crown, and processing the voxel representation such that it is in a scale, position and orientation that corresponds to voxel representations that are used to train a first 3D deep neural network ; the pre-processor providing the voxel representation of the crown to the input of the first 3D deep neural network , the first 3D deep neural network being trained on the basis of a training set of pre-processed clinical 3D data defining 3D representations of real teeth, the trained deep neural network being configured to predict an anatomically accurate voxel representation of a root corresponding to the crown or a voxel representation of a complete tooth; the first 3D deep neural network generating a voxel representation of a predicted root or of a complete tooth comprising the predicted root on the basis of the voxel representation of the crown, wherein the generation of the voxel representation of the predicted root or the complete tooth includes: determining voxel activations for voxels in a voxel space of the output of the first 3D deep learning network, each voxel activation representing a probability measure defining the probability that a voxel is at least part of the root or of the complete tooth; and, determining whether a voxel activation is part of the root or of the complete tooth by comparing the voxel activation with a voxel activation threshold value, preferably a threshold value representing a probability of more than 0.5. a pre-processor receiving 3D data defined in a voxel space, the voxel space defining a 3D volume, the voxel space comprising a voxel representation of a crown, and processing the voxel representation such that it is in a scale, position and orientation that corresponds to voxel representations that are used to train a first 3D deep neural network ; the pre-processor providing the voxel representation of the crown to the input of the first 3D deep neural network , the first 3D deep neural network being trained on the basis of a training set of pre-processed clinical 3D data defining 3D representations of real teeth, the trained deep neural network being configured to predict an anatomically accurate voxel representation of a root corresponding to the crown or a voxel representation of a complete tooth; the first 3D deep neural network generating a voxel representation of a predicted root or of a complete tooth comprising the predicted root on the basis of the voxel representation of the crown, wherein the generation of the voxel representation of the predicted root or the complete tooth includes: determining voxel activations for voxels in a voxel space of the output of the first 3D deep learning network, each voxel activation representing a probability measure defining the probability that a voxel is at least part of the root or of the complete tooth; and, determining whether a voxel activation is part of the root or of the complete tooth by comparing the voxel activation with a voxel activation threshold value, preferably a threshold value representing a probability of more than 0.5. determining voxel activations for voxels in a voxel space of the output of the first 3D deep learning network, each voxel activation representing a probability measure defining the probability that a voxel is at least part of the root or of the complete tooth; and, determining whether a voxel activation is part of the root or of the complete tooth by comparing the voxel activation with a voxel activation threshold value, preferably a threshold value representing a probability of more than 0.5. <EOS>