Abstract: A method for use in additively manufacturing an orthodontic aligner may include receiving a digital model of an aligner, processing the digital model of the aligner to modify the digital model based on predicted deviations during fabrication to generate a first updated digital model of the aligner, determining that whether the predicted deviations of a physical aligner additively manufactured based on the first updated digital model of the aligner are acceptable, and outputting the first updated digital model of the aligner for fabrication of the physical aligner by additive manufacture of the physical aligner.

Abstract: Methods for designing orthodontic appliances are provided. In some embodiments, a method includes receiving a treatment plan for a patient's dentition and generating an aligner model representing an aligner configured to implement a treatment stage of the treatment plan. The aligner model can include a thickness map having a thickness parameter for each location of the aligner model. The method can include adjusting the thickness map by using the aligner model to evaluate an outcome when the aligner is applied to the patient's dentition, and iteratively reducing the value of each thickness parameter of the thickness map based on the evaluation until a resulting thickness is determined for each thickness parameter. The method can also include providing instructions to manufacture the aligner based on the aligner model with the resulting thickness for each thickness parameter of the thickness map.

Abstract: Methods and apparatuses (including software) for optimizing dental treatment plans, including for optimizing the treatment plans using dental aligners. These methods and apparatuses may optimize a treatment plan by estimating a difference between a target set of tooth position and a predicted set of tooth positions using the treatment plan using a prediction network trained to use multiple translational and rotational directions for individual teeth as well as reaction forces on the individual teeth based on adjacent teeth.

Abstract: Methods and systems for producing orthodontic appliances are provided herein utilizing iterative modeling techniques to increase the efficiency and efficacy of said appliances. Further disclosed herein are the orthodontic appliances fabricated from such methods.

Abstract: Embodiments relate to an aligner breakage solution that tests damage to an aligner using machine learning. A method includes of training a machine learning model to predict damage to an orthodontic aligner includes gathering a training dataset comprising digital designs for a plurality of orthodontic aligners, wherein each digital design is associated with a respective orthodontic aligner of the plurality of orthodontic aligners, and wherein each digital design comprises metadata indicating whether the associated respective orthodontic aligner was damaged during manufacturing of the associated respective orthodontic aligner. The method further includes training the machine learning model using the training dataset, wherein the machine learning model is trained to process data from a digital design for an orthodontic aligner and to output a probability that the orthodontic aligner associated with the digital design will be damaged during manufacturing of the orthodontic aligner.

Abstract: A support is coupled to an appliance to decrease warpage. The support may comprise a plurality of extensions coupled to the appliance to decrease warpage. The extensions can be coupled to the appliance at one or more of many locations, such as on an occlusal surface, a wall, an edge or an interior of the appliance. In some embodiments, the extensions are coupled to walls of the appliance such as a buccal wall and a lingual wall, and the extensions can be coupled to the walls of the appliance near edges of the walls, such as a gingivally facing edges of the walls that are oriented toward the gingiva when the appliance is worn.

Abstract: A method for generating and fabricating a dental device having a body. The body may have a first plurality of tooth receiving cavities shaped to receive teeth of a first side of an arch. The body may also have a second plurality of tooth receiving cavities shaped to receive teeth of a second side of an arch. A palatal portion may extend between the first plurality of tooth receiving cavities and the second plurality of tooth receiving cavities. The palatal portion may be shaped to apply an expansion force to the teeth of the first side of the arch and the teeth of the second side of the arch. The palatal portion may include a plurality of bands that extend between the first plurality of tooth receiving cavities and the second plurality of tooth receiving cavities, each respective band may have a respective different thickness.

Abstract: Embodiments relate to an aligner breakage solution that tests damage to an aligner using machine learning. A method includes processing data from a digital design for an orthodontic aligner by a trained machine learning model and outputting, by the trained machine learning model, a probability that the orthodontic aligner associated with the digital design will be damaged during manufacturing of the orthodontic aligner. The method further includes making a comparison of the probability that the orthodontic aligner associated with the digital design will be damaged during manufacturing of the orthodontic aligner to a probability threshold and determining whether the orthodontic aligner is a high risk orthodontic aligner based on a result of the comparison. Responsive to determining that the orthodontic aligner is a high risk orthodontic aligner, the method includes performing at least one of a) a corrective action or b) selecting a manufacturing flow for high risk orthodontic aligners.

Abstract: Embodiments relate to an aligner breakage solution that tests probability of aligner breakage at weak points. A method includes gathering a digital model representing an aligner for a dental arch of a patient, receiving material property information for a material to be used to manufacture the aligner, and analyzing one or more regions of the aligner. Analyzing a region of the aligner comprises simulating application of a load around the region, determining at least one of a stress, a strain or a strain energy density at the region, evaluating a strength of the aligner at the region, and determining whether the region satisfies a damage criterion based on the strength of the aligner at the region.

Abstract: A support is coupled to an appliance to decrease warpage. The support may comprise a plurality of extensions coupled to the appliance to decrease warpage. The extensions can be coupled to the appliance at one or more of many locations, such as on an occlusal surface, a wall, an edge or an interior of the appliance. In some embodiments, the extensions are coupled to walls of the appliance such as a buccal wall and a lingual wall, and the extensions can be coupled to the walls of the appliance near edges of the walls, such as gingivally facing edges of the walls that are oriented toward the gingiva when the appliance is worn.

Abstract: Embodiments relate to an aligner breakage solution that tests progressive damage to an aligner. A method includes gathering a digital model representing an aligner for a dental arch of a patient, and simulating progressive damage to the aligner. Simulating progressive damage for a region of the aligner comprises simulating, using at least the digital model, a sequence of loads on the aligner, determining an amount of damage to the region of the aligner for each load, and after each simulation of a load on the aligner, updating the digital model based on the amount of damage to the region of the aligner. The method further includes determining whether a damage criterion is satisfied for at least one region of the aligner and determining whether to implement one or more corrective actions for the aligner.

Abstract: Computer-implemented methods and systems for designing oral appliances. These methods and systems may include receiving patient data and a treatment plan (including a proposed one or more dental appliances), and determining, using the patient data and the treatment plan, an improved appliance design for treating the patient. These methods and systems may include evaluating the appliance design based on simulation and finalizing the appliance design based on the evaluation.

Abstract: Embodiments relate to an aligner breakage solution. A method includes obtaining a digital design of a polymeric aligner for a dental arch of a patient. The polymeric aligner is shaped to apply forces to teeth of the dental arch. The method also includes performing an analysis on the digital design of the polymeric aligner using at least one of a) a trained machine learning model, b) a numerical simulation, c) a geometry evaluator or d) a rules engine. The method may also include determining, based on the analysis, whether the digital design of the polymeric aligner includes probable points of damage, wherein for a probable point of damage there is a threshold probability that breakage, deformation, or warpage will occur. The method may also include, responsive to determining that the digital design of the polymeric aligner comprises probable points of damage, performing corrective actions based on the probable points of damage.

Abstract: The disclosed computer-implemented method for selecting locations of mandibular relocation features (MRFs) on oral appliances may include determining candidate locations for placing an MRF on a first oral appliance for a first stage of mandibular relocation (MR) treatment and determining candidate locations for placing an MRF on a second oral appliance for a second stage. The method may include selecting from the candidate locations for the first stage and the second stage based on decreasing a distance between the selected pair of candidate locations. The method may further include processing multiple MRFs in parallel. The method may also include increasing a number of initial candidate locations. Locations for intermediate stages may be selected based on interpolating between the first and second stages or based on decreasing a distance between the selected pair if the interpolation fails. The intermediate stages may be processed using a binary search or a linear search.

Abstract: A support is coupled to an appliance to decrease warpage. The support may comprise a plurality of extensions coupled to the appliance to decrease warpage. The extensions can be coupled to the appliance at one or more of many locations, such as on an occlusal surface, a wall, an edge or an interior of the appliance. In some embodiments, the extensions are coupled to walls of the appliance such as a buccal wall and a lingual wall, and the extensions can be coupled to the walls of the appliance near edges of the walls, such as a gingivally facing edges of the walls that are oriented toward the gingiva when the appliance is worn.

Abstract: Methods and systems for producing orthodontic appliances are provided herein utilizing iterative modeling techniques to increase the efficiency and efficacy of said appliances. Further disclosed herein are the orthodontic appliances fabricated from such methods.

Abstract: Embodiments relate to an aligner breakage solution that tests damage to an aligner using machine learning. A method includes processing data from a digital design for an orthodontic aligner by a trained machine learning model and outputting, by the trained machine learning model, a probability that the orthodontic aligner associated with the digital design will be damaged during manufacturing of the orthodontic aligner. The method further includes making a comparison of the probability that the orthodontic aligner associated with the digital design will be damaged during manufacturing of the orthodontic aligner to a probability threshold and determining whether the orthodontic aligner is a high risk orthodontic aligner based on a result of the comparison. Responsive to determining that the orthodontic aligner is a high risk orthodontic aligner, the method includes performing at least one of a) a corrective action or b) selecting a manufacturing flow for high risk orthodontic aligners.

Abstract: Embodiments relate to an aligner breakage solution. A method includes obtaining a digital design of a polymeric aligner for a dental arch of a patient. The polymeric aligner is shaped to apply forces to teeth of the dental arch. The method also includes performing an analysis on the digital design of the polymeric aligner using at least one of a) a trained machine learning model, b) a numerical simulation, c) a geometry evaluator or d) a rules engine. The method may also include determining, based on the analysis, whether the digital design of the polymeric aligner includes probable points of damage, wherein for a probable point of damage there is a threshold probability that breakage, deformation, or warpage will occur. The method may also include, responsive to determining that the digital design of the polymeric aligner comprises probable points of damage, performing corrective actions based on the probable points of damage.

Abstract: Embodiments relate to an aligner breakage solution that tests probability of aligner breakage at weak points. A method includes gathering a digital model representing an aligner for a dental arch of a patient, receiving material property information for a material to be used to manufacture the aligner, and analyzing one or more regions of the aligner. Analyzing a region of the aligner comprises simulating application of a load around the region, determining at least one of a stress, a strain or a strain energy density at the region, evaluating a strength of the aligner at the region, and determining whether the region satisfies a damage criterion based on the strength of the aligner at the region.

Abstract: Embodiments relate to an aligner breakage solution that tests progressive damage to an aligner. A method includes gathering a digital model representing an aligner for a dental arch of a patient, and simulating progressive damage to the aligner. Simulating progressive damage for a region of the aligner comprises simulating, using at least the digital model, a sequence of loads on the aligner, determining an amount of damage to the region of the aligner for each load, and after each simulation of a load on the aligner, updating the digital model based on the amount of damage to the region of the aligner. The method further includes determining whether a damage criterion is satisfied for at least one region of the aligner and determining whether to implement one or more corrective actions for the aligner.