Abstract: Methods for visualizing treatment outcomes are provided. In some embodiments, a method includes obtaining a first 2D image of a patient's teeth from a mobile device, and obtaining an arch model representing an outcome for the patient's teeth after orthodontic treatment. The method can include matching the patient's teeth in the first 2D image to corresponding teeth in the arch model, using a parametric model. The method can also include determining a target position for the patient's teeth, based on a position of the matched corresponding teeth in the arch model. The method can further include generating a second 2D image depicting the patient's teeth in the target position.

Abstract: An exemplary method, for preventing an outage in a network, includes collecting network metrics and producing statistical features of the network during a predetermined time window based on statistical analysis of the collected metrics. The method further includes selecting, from the produced statistical features, a first data set of relevant statistical features. Further, the method includes collecting information about a past event, extracting event data from the past event, and vectorizing the extracted event data to form a second data set. The method further includes concatenating the first data set and the second data set to form a third data set and classifying the third data set relative to a model of historical network performance to determine a probability of the outage. Further, the method includes modifying, in response to the probability being above a threshold, the network/activity within the network to avoid onset of the outage.

Abstract: Methods and systems for automatically determining an eruption status and/or primary or permanent tooth type of a target tooth. Methods may include determining tooth shape features of the target tooth from a 3D model of the patient's teeth. The methods may also include normalizing at least some of the tooth shape features using the tooth shape features of one or more reference teeth. The normalized tooth shape features may be applied to a classifier. Applying the normalized tooth shape features to the classifier may include applying either a first level binary classifier or a first level binary classifier and a second level binary classifier to the normalized tooth shape features.

Abstract: Provided herein are systems and methods for detecting the eruption state (e.g., tooth type and/or eruption status) of a target tooth. A patient's dentition may be scanned and/or segmented. A target tooth may be identified. Dental features, principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems. A detector can identify and/or output the eruption state of the target tooth, such as whether the target tooth is a fully erupted primary tooth, a permanent partially erupted/un-erupted tooth, or a fully erupted permanent tooth.

Abstract: A method includes receiving an image of a face, processing the image using a first trained machine learning model to determine a bounding shape around teeth in the image, cropping the image based on the bounding shape to produce a cropped image, processing the cropped image using an edge detection operation to generate edge data for the cropped image, and processing the cropped image and the edge data using a second trained machine learning model to label edges in the cropped image.

Abstract: An exemplary method, for preventing an outage in a network, includes collecting network metrics and producing statistical features of the network during a predetermined time window based on statistical analysis of the collected metrics. The method further includes selecting, from the produced statistical features, a first data set of relevant statistical features. Further, the method includes collecting information about a past event, extracting event data from the past event, and vectorizing the extracted event data to form a second data set. The method further includes concatenating the first data set and the second data set to form a third data set and classifying the third data set relative to a model of historical network performance to determine a probability of the outage. Further, the method includes modifying, in response to the probability being above a threshold, the network/activity within the network to avoid onset of the outage.

Abstract: A method is provided in an example embodiment and may include receiving information identifying an anomaly or a predicted outage of a component of a system and requesting a data service for buffered data generated by the component within a timeframe of receiving the information. The method further may include normalizing the buffered data into discrete time units into corresponding distinct units of time and analyzing the buffered data using a machine learning (ML) model to identify a root cause of the anomaly or the predicted outage. The method further may include identifying at least one solution to the identified root cause based on characteristics associated with the root cause of the anomaly or the predicted outage and providing the at least one solution to an automation component to avoid onset of an event that triggers outage of the component or the system.

Abstract: An exemplary method, for solving a problem with a network element within a network, includes first selecting a first set of pre-existing bots and first executing at least some of the selected first set of bots to change the network element and/or the network to solve the problem. The method further includes conducting a root cause analysis to generate recommendation(s) to correct the problem and in response to the first executing failing to solve the problem: second selecting, based on the recommendation(s), a second set of pre-existing bots to change the network element and/or the network to solve the problem, and second executing at least some of the selected second set of bots to solve the problem. Subsequently, the method includes generating, in response to the second executing failing to solve the problem, instructions to manually change the network element and/or the network to solve the problem.

Abstract: A method includes receiving an image of a face of a patient, the image including a depiction of teeth; processing the image of the face using one or more trained machine learning model, wherein the one or more trained machine learning model outputs a pixel-level classification of pixels in the image, the pixel level classification comprising a first set of pixels classified as being inside of a bounding shape that bounds a first plurality of teeth depicted in the image and a second set of pixels classified as being outside of the first bounding shape; cropping the image of the face of the patient, wherein the cropped image comprises depictions of the first plurality of teeth; and performing one or more operations on the cropped image of the face of the patient.

Abstract: Methods for visualizing treatment outcomes are provided. In some embodiments, a method includes obtaining a first 2D image of a patient's teeth from a mobile device, and obtaining an arch model representing an outcome for the patient's teeth after orthodontic treatment. The method can include matching the patient's teeth in the first 2D image to corresponding teeth in the arch model, using a parametric model. The method can also include determining a target position for the patient's teeth, based on a position of the matched corresponding teeth in the arch model. The method can further include generating a second 2D image depicting the patient's teeth in the target position.

Abstract: Methods for simulating orthodontic treatment are provided. In some embodiments, a method includes capturing a first representation, the first representation including a depiction of a patient's teeth. The method can include building a parametric model of the patient's teeth based on the first representation, using one or more case-specific parameters for one or more shapes associated with at least one of the patient's teeth. The method can also include simulating an outcome of a dental treatment plan for the patient's teeth to produce a simulated outcome of the dental treatment plan. The method can further include modifying the parametric model to provide a modified model representing the simulated outcome of the dental treatment plan.

Abstract: Systems and methods of simulating dental treatments are disclosed. A method may include capturing a first 2D image of a patient's face, including the patient's teeth, building a parametric 3D model of the patient's teeth based on the 2D image, developing a simulated outcome of a dental treatment of the patient's teeth by rendering the 3D model with the patient's teeth in one or more positions and/or orientations corresponding to the treatment goals of the dental treatment plan, and rendering a second 2D image of the patient's face with teeth according to a simulated outcome of the dental treatment plan. As noted herein, the dental treatment plan may include orthodontic and/or restorative elements. The simulated outcome may correspond to estimated outcomes and/or intended outcomes of the dental treatment plan.

Abstract: A method includes receiving an image of a face of a patient, the image including a depiction of teeth; processing the image of the face using one or more trained machine learning model, wherein the one or more trained machine learning model outputs a pixel-level classification of pixels in the image, the pixel level classification comprising a first set of pixels classified as being inside of a bounding shape that bounds a first plurality of teeth depicted in the image and a second set of pixels classified as being outside of the first bounding shape; cropping the image of the face of the patient, wherein the cropped image comprises depictions of the first plurality of teeth; and performing one or more operations on the cropped image of the face of the patient.

Abstract: Systems and methods of simulating dental treatments are disclosed. A method may include capturing a first 2D image of a patient's face, including the patient's teeth, building a parametric 3D model of the patient's teeth based on the 2D image, developing a simulated outcome of a dental treatment of the patient's teeth by rendering the 3D model with the patient's teeth in one or more positions and/or orientations corresponding to the treatment goals of the dental treatment plan, and rendering a second 2D image of the patient's face with teeth according to a simulated outcome of the dental treatment plan. As noted herein, the dental treatment plan may include orthodontic and/or restorative elements. The simulated outcome may correspond to estimated outcomes and/or intended outcomes of the dental treatment plan.

Abstract: A machine learning model is trained to define bounding shapes around teeth in images. The machine learning model is trained by receiving a training dataset comprising a plurality of images, each image of the plurality of images comprising a face and a provided bounding shape around teeth in the image. The training dataset is input into an untrained machine learning model. The untrained machine learning model is trained based on the training dataset to generate a trained machine learning model that defines bounding shapes around teeth in images, wherein for an input image the trained machine learning model is to output a mask that defines a bounding shape around teeth of the input image, wherein the mask indicates, for each pixel of the input image, whether that pixel is inside of a defined bounding shape or is outside of the defined bounding shape.

Abstract: Systems and methods of simulating dental treatments are disclosed. A method may include capturing a first 2D image of a patient's face, including the patient's teeth, building a parametric 3D model of the patient's teeth based on the 2D image, developing a simulated outcome of a dental treatment of the patient's teeth by rendering the 3D model with the patient's teeth in one or more positions and/or orientations corresponding to the treatment goals of the dental treatment plan, and rendering a second 2D image of the patient's face with teeth according to a simulated outcome of the dental treatment plan. As noted herein, the dental treatment plan may include orthodontic and/or restorative elements. The simulated outcome may correspond to estimated outcomes and/or intended outcomes of the dental treatment plan.

Abstract: Systems and methods of simulating dental treatments are disclosed. A method may include capturing a first 2D image of a patient's face, including the patient's teeth, building a parametric 3D model of the patient's teeth based on the 2D image, developing a simulated outcome of a dental treatment of the patient's teeth by rendering the 3D model with the patient's teeth in one or more positions and/or orientations corresponding to the treatment goals of the dental treatment plan, and rendering a second 2D image of the patient's face with teeth according to a simulated outcome of the dental treatment plan. As noted herein, the dental treatment plan may include orthodontic and/or restorative elements. The simulated outcome may correspond to estimated outcomes and/or intended outcomes of the dental treatment plan.

Abstract: Systems and methods of simulating dental treatments are disclosed. A method may include capturing a first 2D image of a patient's face, including the patient's teeth, building a parametric 3D model of the patient's teeth based on the 2D image, developing a simulated outcome of a dental treatment of the patient's teeth by rendering the 3D model with the patient's teeth in one or more positions and/or orientations corresponding to the treatment goals of the dental treatment plan, and rendering a second 2D image of the patient's face with teeth according to a simulated outcome of the dental treatment plan. As noted herein, the dental treatment plan may include orthodontic and/or restorative elements. The simulated outcome may correspond to estimated outcomes and/or intended outcomes of the dental treatment plan.

Abstract: A machine learning model is trained to define bounding shapes around teeth in images. The machine learning model is trained by receiving a training dataset comprising a plurality of images, each image of the plurality of images comprising a face and a provided bounding shape around teeth in the image. The training dataset is input into an untrained machine learning model. The untrained machine learning model is trained based on the training dataset to generate a trained machine learning model that defines bounding shapes around teeth in images, wherein for an input image the trained machine learning model is to output a mask that defines a bounding shape around teeth of the input image, wherein the mask indicates, for each pixel of the input image, whether that pixel is inside of a defined bounding shape or is outside of the defined bounding shape.

Abstract: Provided herein are systems and methods for detecting the eruption state (e.g., tooth type and/or eruption status) of a target tooth. A patient's dentition may be scanned and/or segmented. A target tooth may be identified. Dental features, principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems. A detector can identify and/or output the eruption state of the target tooth, such as whether the target tooth is a fully erupted primary tooth, a permanent partially erupted/un-erupted tooth, or a fully erupted permanent tooth.