Align Technology, Inc.

Andrey Cherkas

Anatoliy Parpara

Stephan Albert Alexandre Dumothier

Israel Velazquez

Alexey Kalinichenko

Jack Shaw

Ivan Kharpalev

Danila Chesnokov

Yury Slynko

Alexey Geraskin

Paren Shah

Chunhua Li

Jun Sato

Rohit Tanugula

Reza Shirazi Aghjari

Andrew Jang

Yuxiang Wang

Luyao Cai

Anna Akopova

Arno Kukk

Viktoria Medvinskaya

Shiva Prasad Sambu

Svetlana Makarenkova

Kangning Su

Kwan Ho

Enrique Soltero Borrego

Ben Castricone

Ari Siletz

Paren Indravadan Shah

Viktor Sazhaev

Kristina Anikina

Mikhail Yudashkin

A method includes manufacturing an orthodontic aligner and assessing a quality of the orthodontic aligner. Assessing the quality of the orthodontic aligner comprises receiving a digital representation of the orthodontic aligner, and analyzing the digital representation of the orthodontic aligner to identify a quality-related property of the orthodontic aligner. The analyzing comprises comparing the digital representation of the orthodontic aligner with data based on a digital model associated with the orthodontic aligner and calculating one or more difference between a feature determined from the data and a feature of the digital representation of the orthodontic aligner. Assessing the quality of the orthodontic aligner further includes determining, based on the quality-related property, that the orthodontic aligner comprises a possible manufacturing flaw, and classifying the orthodontic aligner as requiring further inspection by a technician based on determining that the orthodontic aligner comprises the possible manufacturing flaw.

Methods for manufacturing and inspecting orthodontic aligners

Systems and methods for performing automated quality control for 3D printed molds for orthodontic aligners are described. A method includes generating a plurality of images of the 3D printed mold for the orthodontic aligner using one or more imaging devices, wherein each image of the plurality of images depicts a distinct region of the 3D printed mold for the orthodontic aligner; processing the plurality of images by a processing device to identify one or more types of manufacturing defects of the 3D printed mold for the orthodontic aligner, wherein for each type of manufacturing defect a probability that an image comprises a defect of that type of manufacturing defect is determined; and classifying, by the processing device, the 3D printed mold for the orthodontic aligner as defective based on identifying the one or more types of manufacturing defects.

Automated quality control for three-dimensional (3D) printed mold for orthodontic aligner

A virtual filler is inserted in a virtual model of a dental site between a first tooth and a second tooth in the virtual model. One or more of the first tooth or the second tooth represent a natural tooth of a patient. Points of the first tooth, the second tooth and the virtual filler are transformed into a voxel volume. A geometry of an updated virtual filler is determined by transforming a surface of the voxel volume into a polygonal mesh.

Virtual fillers for virtual models of dental arches

Multiple techniques for detecting defects in customized dental appliances are disclosed. In one technique, processing logic obtains one or more images of a customized dental appliance, obtains a digital model associated with the customized dental appliance, and identifies an area of the one or more images that comprises a representation of the customized dental appliance. Processing logic then registers the one or more images to the digital model, compares the area of the one or more images of the customized dental appliance with the digital model of the customized dental appliance, determines a difference between the area of the one or more images that comprises the representation of the customized dental appliance and the digital model of the customized dental appliance at a region, and determines whether the difference satisfies a defect criterion.

Image-based defect detection of dental appliances

Provided herein are systems and methods for determining hook and or positioning feature placements during dental treatment planning. A patient's dentition may be scanned and/or segmented. A target tooth may be identified. Hook and or positioning feature placement may be determined based on satisfying manufacturing and clinical constraints. The hook or positioning features can be input into a dental treatment planning system.

Dental appliance hook placement and visualization

A method includes obtaining image(s) of a dental device, determining file(s) including at least one of a first model of the dental device or a second model of a mold, determining an intended property for the dental device based on at least one of the first model or the second model, and determining an actual property of the dental device from the image(s). The method further includes determining whether a cutline variation, an arch variation, and/or a bend of the dental device is detected between the dental device and the first model and/or the second model by comparing the intended property with the actual property. The method further includes determining whether there is a possible defect in the dental device based on whether the cutline variation exceeds a cutline variation threshold, the arch variation exceeds an arch variation threshold, and/or the bend exceeds a bend threshold.

Methods of manufacturing and inspecting customized dental appliances

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.

Aligner damage prediction using machine learning

A method of manufacturing and analyzing a quality of an orthodontic aligner is described. An orthodontic aligner is manufactured by printing a mold associated with a dental arch of a patient based on a digital model of the mold, forming an orthodontic aligner over the mold, and trimming the orthodontic aligner. A quality of the orthodontic aligner is then assessed by imaging the orthodontic aligner to generate a first digital representation of the orthodontic aligner, comparing the first digital representation of the orthodontic aligner to a digital file associated with the orthodontic aligner, determining whether a cutline variation is detected between the orthodontic aligner and the digital file, and determining whether there is a manufacturing defect of the orthodontic aligner based on whether the cutline variation exceeds a cutline variation threshold.

Methods of manufacturing and assessing orthodontic aligners

A method for manufacturing an orthodontic aligner includes printing a mold associated with a dental arch of a patient based on a digital model of the mold, forming the orthodontic aligner over the mold, and trimming the orthodontic aligner. The method further includes assessing a quality of the orthodontic aligner by receiving a digital representation of the orthodontic aligner, the digital representation having been generated based on imaging of the orthodontic aligner, analyzing the digital representation of the orthodontic aligner to identify a quality-related property of the orthodontic aligner, determining, based on the quality-related property, that the orthodontic aligner comprises a manufacturing flaw, and classifying the orthodontic aligner as requiring further inspection by a technician based on determining that the orthodontic aligner comprises the manufacturing flaw.

Systems and methods for providing orthodontic aligners and other dental appliances

Multiple techniques for detecting defects in customized dental appliances are disclosed. In one technique, processing logic obtains one or more images of a customized dental appliance, obtains a digital model associated with the customized dental appliance, and performs segmentation on the one or more images to identify an area of the one or more images that comprises a representation of the customized dental appliance. Processing logic then registers the one or more images to the digital model, compares the area of the one or more images of the customized dental appliance with the digital model of the customized dental appliance, determines a difference between the area of the one or more images that comprises the representation of the customized dental appliance and the digital model of the customized dental appliance at a region, and determines whether the difference satisfies a defect criterion.

Vision and geometric approaches to detect defects in dental appliances

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.

Aligner damage prediction and mitigation

Implementations describe systems and methods for machine based defect detection of three-dimensional (3D) printed objects. A method of one embodiment of the disclosure includes providing a first illumination of a 3D printed object using a first light source arrangement. A plurality of images of the 3D printed object are then generated using one or more imaging devices. Each image may depict a distinct region of the 3D printed object. The plurality of images may then be processed by a processing device using a machine learning model trained to identify one or more types of manufacturing defects of a 3D printing process. The machine learning model may provide a probability that an image contains a manufacturing defect. The processing device may then determine, without user input, whether the 3D printed object contains one or more manufacturing defects based on the results provided by the machine learning model.

Machine based three-dimensional (3D) object defect detection

A system for inspecting a customized dental device associated with a dental application for manufacturing defects is disclosed. The system obtains images of the customized device and identifies an identifier of the customized device. The system determines a digital file associated with the customized device based on the identifier, the digital file including a first digital model of the customized device and/or a second digital model of a mold used during manufacture of the customized device. The system determines an intended property for the customized device based on at least one of the first digital model or the second digital model, determines an actual property of the customized device from the images, determines whether there is a manufacturing defect in the customized device by comparing the intended property for the customized device with the actual property of the customized device, and outputs an output associated with the determination.

Aligner image based quality control system

Two or more adjacent teeth in a virtual model are determined. The two or more adjacent teeth include a first tooth adjacent to a second tooth in the virtual model. The virtual filler is inserted in the virtual model between the first tooth and the second tooth. Points in the virtual model are selected. The points and the virtual filler are transformed into a voxel volume. A geometry of an updated virtual filler is determined by transforming a surface of the voxel volume into a polygonal mesh.

System and methods herein determine two or more adjacent teeth in a virtual model. The system and methods determine a characteristic of the teeth indicative of a spatial relationship between the teeth. The system and methods further determine that the characteristic satisfies a criterion for a virtual filler. The system and methods determine a geometry of the virtual filler based on the spatial relationship between the teeth.

Growing community of inventors

Krasnoznamensk, Russia

Andrey Cherkas