Sdc U.S. Smilepay Spv

Ortho Future Technologies (pty) Ltd

Tim Wucher

Jordan Katzman

Alfred Meyer Dippenaar

Martin Wucher

Christopher Yancey

Ryan Amelon

John Dargis

Josh Long

Andrew Wright

Aleksey Gurtovoy

Ramsey Jones

Jared Lafer

Martin Chobanyan

Drew Marschner

Eric Wagnon

Charlotte Xia

Justin Kreil

Systems and methods for generating a 3D model of a dentition are disclosed herein. A method includes generating a first 3D model of a dentition. The dentition includes a model tooth in a first position. The method includes receiving a digital representation comprising a patient tooth. The patient tooth corresponds to the model tooth of the first 3D model. The method includes determining a virtual camera parameter associated with the digital representation. The method includes adjusting the virtual camera parameter to determine a 3D position of the corresponding patient tooth. The method includes moving the model tooth of the first 3D model from the first position to a second position. The second position corresponds to the 3D position of the corresponding patient tooth. The method includes generating a second 3D model comprising the model tooth of the first 3D model in the second position.

Positioning individual three-dimensional model teeth based on two-dimensional images

A method for obtaining an orthodontic treatment plan is provided. The method comprises obtaining a treatment plan for treatment of a tooth of a patient, the treatment plan including two or more stage force values, wherein each of the two or more stage force values is based on a target orthodontic force value that is based on one or more determinate points of periodontal ligament (PDL) behavior data. For each of the two or more stage force values, the method includes determining configuration parameters for configuring an orthodontic appliance to apply the stage force value to a tooth of the patient; and, using the configuration parameters to manufacture an orthodontic appliance configured to apply the stage force value.

Orthodontic treatment using an orthodontic treatment plan

Systems and methods for fabricating a dental aligner are provided. The system includes a computing system to receive and prepare a digital model of a dental aligner by adding one or more support structures to the digital model. The system includes a fabrication system to manufacture the dental aligner with the one or more support structures. The system includes a laser system to receive the fabricated dental aligner, orient the dental aligner relative to the laser system, and laser cut an interface between the support structures and the dental aligner to remove the one or more support structures from the dental aligner.

Laser-based support structure removal

A system includes one or more processors coupled to a non-transitory memory, where the one or more processors are configured to generate, using a training set comprising one or more two-dimensional (2D) training images of a dental arch and a three-dimensional (3D) dental arch model, a machine-learning model configured to generate 3D models of dental arches from 2D images of the dental arches, receive one or more 2D images of a dental arch of a user obtained by a user device of the user, and execute the machine-learning model using the one or more 2D images as input to generate a 3D model of the dental arch of the user.

Systems and methods for constructing a three-dimensional model from two-dimensional images

Systems and methods for generating a three-dimensional (3D) model of a user's dental arch based on two-dimensional (2D) images of dental impressions include a model training system that provides a machine learning model using training image(s) of a dental impression of a respective dental arch and a 3D training model of the respective dental arch. A model generation system receives first image(s) of a first dental impression of a user's dental arch and second image(s), which may be of the first dental impression or a second dental impression of the dental arch. The model generation system generates a first and second 3D model of the dental arch by applying the first image(s) and second image(s) to the machine learning model.

Systems and methods disclosed herein use a first machine learning architecture and a second machine learning architecture where the first machine learning architecture executes on a first processor and receives a first image representing a mouth of a user, determines user feedback for outputting to the user based on a first machine learning model, and outputs the user feedback for capturing a second image representing the mouth of the user. The second machine learning architecture executes on a second processor and receives the first image and the second image, and generates a 3D model of at least a portion of a dental arch of the user based on the first image and the second image where the 3D model is generated based on a second machine learning model of the second machine learning architecture.

Machine learning architecture for imaging protocol detector

Systems and methods for generating a 3D model of a dentition are disclosed herein. A method includes generating a first 3D model of a dentition including a plurality of model teeth. The method includes receiving at least one digital representation comprising patient teeth. The method includes determining a virtual camera parameter. The virtual camera parameter corresponds with a camera used to capture the digital representation. The method includes comparing, based on the virtual camera parameter, a first position of a model tooth with a position of a corresponding patient tooth of the digital representation. The method includes moving the model tooth from the first position to a second position. The second position is based on the position of the corresponding patient tooth. The method includes generating a second 3D model comprising the model tooth of the first 3D model in the second position.

Disclosed is a machine learning architecture for a two-dimensional image protocol detector configured to receive a first image representing at least a portion of a mouth of a user, and output user feedback for capturing a second image representing a portion of the mouth of the user, where the machine learning architecture outputs the user feedback in response to an image quality score of the first image not satisfying an image quality threshold.

Systems and methods for generating a three-dimensional (3D) model of a user's dental arch based on two-dimensional (2D) images include a model training system that receives data packets of a training set. Each data packet may include data corresponding to training images of a respective dental arch and a 3D training model of the respective dental arch. The model training system identifies, for a data packet, correlation points between the one or more training images and the 3D training model of the respective dental arch. The model training system generates a machine learning model using the correlation points for the data packets of the training set. A model generation system receives one or more images of a dental arch. The model generation system generates a 3D model of the dental arch by applying the images of the dental arch to the machine learning model.

A system and method for orthodontic appliance configuration are provided. A method includes obtaining a force value which approximates a target orthodontic force value having been determined based on patient data and PDL behavior data associated with the patient data. The patient data includes patient treatment data and patient characteristic data, the patient treatment data at least including one or both of a tooth type indicator and a tooth position indicator associated with a tooth of a patient to be treated and a treatment movement value which describes the required tooth movement to effect treatment of the tooth. The force value is used to determine configuration parameters for configuring an orthodontic appliance to apply the force value to the tooth of the patient. The configuration parameters are output.

Orthodontic appliance configuration

A system and method for orthodontic treatment staging are described. A method includes receiving patient data including patient treatment data and patient characteristic data. One or more stage movement values and corresponding stage force values are determined. Each stage force value is based on a target orthodontic force value that is based on one or more determinate points of PDL behavior data associated with at least a subset of the patient data. The PDL behavior data includes or is based on force measurement data points and corresponding displacement measurement data points having been measured in-vivo while applying a force to a particular tooth of a human or animal subject. The sum of the one or more stage movement values approximates a treatment movement value. The one or more stage movement values and corresponding stage force values are output as a treatment plan for treatment of a tooth of the patient.

Orthodontic treatment staging

Systems and methods for generating a three-dimensional (3D) model of a user's dental arch based on two-dimensional (2D) images of dental impressions include a model training system that provides a machine learning model using training image(s) of a dental impression of a respective dental arch and a 3D training model of the respective dental arch. A model generation system receives first image(s) of a first dental impression of a user's dental arch and second image(s), which may be of the first dental impression or a second dental impression of the dental arch. The model generation system generates a first and second 3D model of the dental arch by applying the first image(s) and second image(s) to the machine learning model. A model merging system merges the first 3D model and the second 3D model to generate a merged model of the dental arch.

A measurement device and associated systems and methods are described. The device includes a body and an attachment mechanism fixed to and extending from the body. The attachment mechanism is configured for attachment to a part of a human or animal subject. The or a part of the body is configured for gripping by a user. The device incudes a processor in communication with a force sensor module and a position sensor module. The force sensor module is configured to measure a force applied to the attachment mechanism, in use, and output force measurement data relating to the measured force. The position sensor module is configured to measure the position of the part of the human or animal relative to other parts thereof, in use, and output position measurement data. The processor is configured to store the force measurement data and position measurement data in a memory component associated therewith.

Measurement device

A system and method for determining a target orthodontic force are provided. A method includes receiving patient data associated with a patient and including patient characteristic data and patient treatment data including one or both of a tooth type indicator and a tooth position indicator associated with a tooth of the patient to be corrected. Periodontal ligament (PDL) behavior data including or being based on force measurement data points and corresponding displacement measurement data points having been measured in-vivo while applying a force to a tooth of a human or animal subject and associated with at least a subset of the patient data is retrieved. A target orthodontic force value that is specific to the patient data is determined by inputting the PDL behavior data into an algorithm which determines the target orthodontic force based on the PDL behavior data and the target orthodontic force value is output.

System and method for determining a target orthodontic force

A medical apparatus for use in the corrective treatment of malocclusion and other dentofacial defects is provided. The apparatus has at least one stimulator which is configured to apply a stimulus to a part of the dento-oral complex and includes at least one actuator controllable by means of electronic signals. A feedback system is also provided and is configured to measure parameters relating to the biomechanical tissue response resulting from the stimulus applied by the at least one stimulator, to analyze the parameters by means of a processor, and to adjust the stimulus applied by the at least one stimulator by means of the required control signals in order to apply a stimulus representing an optimal orthodontic force.

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Windhoek, Namibia

Tim Wucher