Abstract: The present disclosure relates to an automated process for the design of dental aligners. Specifically, the present disclosure relates to a method for generating an orthodontic treatment plan for at least one dental arch of a patient, comprising extracting control points of teeth of the at least one dental arch of the patient from received patient-related data, determining, based on the extracted control points, a target dental arch of the patient, calculating, based on the determined target dental arch of the patient, one or more teeth movement stages, and generating, by processing circuitry and based on the calculated one or more teeth movement stages, the orthodontic treatment plan for the at least one dental arch of the patient.

Abstract: The present disclosure relates to an automated process for the design of dental aligners. Specifically, the present disclosure relates to a method for generating an orthodontic treatment plan for at least one dental arch of a patient, comprising extracting control points of teeth of the at least one dental arch of the patient from received patient-related data, determining, based on the extracted control points, a target dental arch of the patient, calculating, based on the determined target dental arch of the patient, one or more teeth movement stages, and generating, by processing circuitry and based on the calculated one or more teeth movement stages, the orthodontic treatment plan for the at least one dental arch of the patient.

Abstract: The present disclosure relates to a method of pixel-based classification of medical images. The method includes training a neural network to perform the pixel-based classification, the training comprising performing a first training on a classifier using a first training set of medical images, pixels of the first training set of medical images being manually labeled as a biological structure type, applying the classifier after the first training to a second training set of medical, identifying and correcting incorrectly classified pixels of the second training set of medical images, and performing a second training on the classifier after the first training using the manually labeled first training set and the correctly classified second training set, and applying the classifier after the second training to the medical images to perform the pixel-based classification.

Abstract: The present disclosure relates to a dental image processing protocol for the design of dental aligners. Specifically, the dental image processing protocol aids in the determination of tooth movements during realignment, based on an initial position and a final position, and on characteristics of the periodontal environment. Therefore, planned tooth movements reflect both crown movement and root movement within biological structures of the alveolar process.

Abstract: The present disclosure relates to a dental image processing protocol for the design of dental aligners. Specifically, the dental image processing protocol aids in the determination of tooth movements during realignment, based on an initial position and a final position, and on characteristics of the periodontal environment. Therefore, planned tooth movements reflect both crown movement and root movement within biological structures of the alveolar process.

Abstract: The present disclosure relates to a dental image processing protocol for the design of dental aligners. Specifically, the dental image processing protocol aids in the determination of tooth movements during realignment, based on an initial position and a final position, and on characteristics of the periodontal environment. Therefore, planned tooth movements reflect both crown movement and root movement within biological structures of the alveolar process.