Abstract: Systems and techniques for comparing orthodontic setups are disclosed. The method involves receiving an instant orthodontic setup and a reference orthodontic setup, both comprising three-dimensional (3D) representations of one or more teeth. The processing circuitry compares at least one aspect of the instant orthodontic setup with the corresponding aspect of the reference orthodontic setup to compute a comparison metric. The comparison metric represents the degree of similarity or dissimilarity between the two setups. The processing circuitry then outputs the computed comparison metric. These systems and techniques enable efficient and accurate comparison of orthodontic setups, aiding in the evaluation and assessment of treatment plans and facilitating informed decision-making in orthodontic procedures.

Abstract: Systems and techniques are disclosed for generating setups for orthodontic alignment treatment. The method involves receiving a digital representation of a patient's teeth and at least one value pertaining to the customization of orthodontic treatment. A prediction for one or more tooth movements for a setup is formed by executing a generator network comprising one or more neural networks. The generator network is further trained based on the formed prediction by performing operations that include predicting the tooth movements, quantifying the difference between the predicted tooth movements and reference tooth movements, generating a loss value based on the quantified difference, and modifying the generator network based on the loss value to form a modified generator network. These systems and techniques enable the efficient generation of setups for orthodontic alignment treatment, improving the accuracy and customization of the treatment process.

Abstract: Systems and techniques for encoding and reconstructing three-dimensional (3D) representations of oral care data are disclosed. The method involves receiving an input 3D representation of oral care data and utilizing the processing circuitry to execute an encoder of a trained autoencoder network. The encoder encodes the input 3D representation into a latent space representation with a lower dimensionality. Subsequently, the processing circuitry executes a decoder of the trained autoencoder network to reconstruct the latent space representation, generating an output 3D representation that closely resembles the original input. To quantify the accuracy of the reconstruction, the processing circuitry computes a reconstruction error, which measures the difference between at least one mesh element of the input 3D representation and the corresponding mesh element of the output 3D representation.

Abstract: Systems and techniques for classifying a 3D representation of oral care data are disclosed. The method involves receiving a first 3D representation comprising one or more mesh elements and providing it as input to a trained autoencoder network. The processing circuitry computes one or more mesh element features for the mesh elements and provides them to the trained autoencoder network. By executing the trained autoencoder network, the first 3D representation of oral care data is encoded into one or more latent space representations. These latent space representations are specifically designed for utilization by a machine learning model for the classification of the first 3D representation of oral care data. These systems and techniques enable accurate and efficient classification of 3D representations, enhancing the analysis and understanding of oral care data for improved diagnosis and treatment planning.

Abstract: Systems and techniques are disclosed for generating and transferring pose information in three-dimensional (3D) representations of oral care data. The method involves receiving reference 3D representations and trial 3D representations of oral care data using processing circuitry of a computing device. Pose transfer neural networks are executed to assign pose information from the reference 3D representations onto the trial 3D representations. By incorporating the assigned pose information, one or more resulting 3D representations of oral care data are generated, wherein aspects of the trial 3D representations are modified to generate the resulting 3D representations. These resulting 3D representations, enriched with pose information, are then provided to one or more automated processes. These systems and techniques enable accurate and efficient analysis of oral care data, facilitating improved treatment planning and decision-making in the field of oral healthcare.

Abstract: Techniques are described for automating the design of dental restoration appliances using machine learning models. An example computing device receives transform information associated with a current dental anatomy of a dental restoration patient, provides the transform information associated with the current dental anatomy of the dental restoration patient as input to a machine learning model trained with transform information indicating placement of a dental appliance component with respect to one or more teeth of corresponding dental anatomies, the dental appliance being used for dental restoration treatment for the one or more teeth, and executes the machine learning model using the input to produce placement information for the dental appliance component with respect to the current dental anatomy of the dental restoration patient.

Abstract: Systems and methods are disclosed for generating a three-dimensional (3D) representation of oral care data for use in oral care treatment. The systems and methods involve receiving an input 3D representation of a patient's dentition and encoding the 3D representation into a lower-dimensional first latent representation using a trained first machine learning (ML) module. Subsequently, a trained second ML module, comprising a trained transformer encoder model or a trained transformer decoder model, is executed to generate a second latent representation using the first latent representation. The second latent representation is then reconstructed into a 3D oral care representation (e.g., a tooth restoration design, an appliance component, a fixture model component, etc.) by a decoder. Finally, the processing circuitry outputs the reconstructed 3D representation of oral care data.

Abstract: Systems and technique are disclosed for generating transforms and oral care appliances for oral care treatment. The method involves receiving a first three-dimensional (3D) representation of oral care data and utilizing processing circuitry to execute a machine learning (ML) model that includes at least one transformer. The ML model generates at least one transform based on the input data. The processing circuitry applies the generated transform to the first 3D representation, placing it in a desired pose relative to a second 3D representation or a global coordinate system axis. Based on this application, the method further generates aspects of one or more oral care appliances associated with either the first 3D representation or the second 3D representation. These systems and techniques enable the efficient generation of transforms and oral care appliances, facilitating improved treatment planning and customization in oral care applications.

Abstract: A method for generating digital setups for an orthodontic treatment path. The method includes receiving a digital 3D model of teeth, performing interproximal reduction (IPR) on the model and, after performing the IPR, generating an initial treatment path with stages including an initial setup, a final setup, and a plurality of intermediate setups. The method also includes computing IPR accessibility for each tooth at each stage of the initial treatment path, applying IPR throughout the initial treatment path based upon the computed IPR accessibility, and dividing the initial treatment path into steps of feasible motion of the teeth resulting in a final treatment path with setups corresponding with the steps. The setups can be used to make orthodontic appliances, such as clear tray aligners, for each stage of the treatment path.

Abstract: Techniques are described for automating the design and manufacture a dental restoration appliance for restoring the dental anatomy of a patient. For example, a system processes a digital three-dimensional (3D) model of a future (desired) dental anatomy of a patient, the future dental anatomy representing an intended shape of at least one tooth of the patient. The system includes a landmark identifier configured to automatically compute, based on the digital 3D model of the future dental anatomy of the patient, one or more landmarks of the future dental anatomy of the patient. The system also includes a custom feature generator configured to automatically generate, based on the one or more landmarks, one or more custom appliance features for a dental appliance for restoring the at least one tooth of the patient. The system further includes a memory device configured to store a digital model of the dental appliance.

Abstract: A container, a wound dressing, and a wound dressing assembly are disclosed. The container may include one or more optical patterns that facilitate determining whether the container has been tampered with using an optical reader. The wound dressing may include one or more optical patterns that facilitate determining whether a stretchable substrate thereof is in a correctly-tensioned state using the optical reader. The wound dressing assembly may include multiple optical patterns that facilitate determining whether a cover of the wound dressing assembly is correctly positioned on a wound dressing of the wound dressing assembly using the optical reader.

Abstract: A filter cartridge for a respirator includes an RFID chip encoding a digital change management policy (DCMP) indicating a useful lifespan of the cartridge, so that the cartridge may be replaced at the end of the useful lifespan. In some examples, a system includes a respirator including a facepiece and an air blower; a removable contaminant capture cartridge installed within the respirator and configured to remove contaminants from the air as the air passes through the contaminant capture cartridge.

Abstract: Systems and techniques for training one or more neural networks to automatically determine placement of a digital representation of an orthodontic appliance are described including generating a prediction of one or more transformations that position the first digital representation of the orthodontic appliance within the first digital representation of the patients teeth, generating a predicted representation for the placement of the orthodontic appliance, generating a loss value that specifies a difference between the one or more predicted representations for the placement of the orthodontic appliance and the one or more reference representations of a placement of the orthodontic appliance that is generated from one or more reference transformations that have been applied to the orthodontic appliance, and modifying the neural network based on the loss value.

Abstract: Systems and techniques for training one or more neural networks to automatically validate digitally generated setups for orthodontic alignment treatment are disclosed including comparing one or more assigned labels with respective one or more aspects of a second representation, automatically generating output that specifies whether the first representation is correctly formed based on the comparing, and automatically training the neural network based on one or more labels assigned by the neural network.

Abstract: Systems and techniques for training one or more neural networks to automatically validate geometrical characteristics of a digital representation of a dental restoration appliance component are disclosed including analyzing one or more assigned labels, automatically generating output that specifies whether the dental restoration appliance is incorrect, automatically training the neural network based on the one or more result labels assigned by the neural network.

Abstract: Systems and techniques for training one or more neural networks to automatically determine placement of a digital representation of an orthodontic appliance are disclosed including comparing one or more aspects of the second representation of a 3D printed part with one or more respective aspects of a first representation of the 3D printed part, generating a reconstruction error based on the comparing, and when the reconstruction error is greater than a predetermined threshold, assigning one or more result labels that specify that the respective aspects of the 3D printed part were not correctly fabricated and when the reconstruction error is less than the predetermined threshold, assigning one or more result labels that specify that the respective aspects of the 3D printed part were correctly fabricated.

Abstract: Systems and techniques for training one or more machine learning models to generate digital representations of dental restoration tooth geometry are disclosed including generating one or more digital representations that define a restored state for a first digital representation, determining one or more differences between the one or more predicted representations for the restored state and the one or more reference representations of the restored state, and modifying the machine learning model based on the determined differences.

Abstract: Systems and techniques for training one or more machine learning models to automatically validate models of fixtures used in orthodontic alignment treatment are disclosed including assigning one or more labels to the first digital representation of the fixture, wherein the one or more labels specify whether the fixture model is correctly formed, wherein the training is performed based on an automatic comparison between a first digital representation of a fixture and a second digital representation of a fixture.

Abstract: Systems and techniques for training one or more encoders to automatically generate coordinate systems used in digital dentistry are disclosed including predicting one or more predicted transformations pertaining to one or more coordinate axes, determining a loss value that specifies a difference between the one or more predicted transformations and one or more respective reference transformations and modifying at least one aspect of the encoder structure based on the loss.

Abstract: Systems and techniques for training one or more neural networks to automatically generate tooth segmentation data used in digital dentistry are disclosed including predicting one or more segmentation labels pertaining to aspects of dental geometry, generating an accuracy score that specifies a difference between the one or more predicted representations and one or more respective reference and modifying at least one aspect of the neural network based on the accuracy score.