Abstract: Systems and methods are provided for automatically marking locations within a radiograph of one or more dental pathologies, anatomies, anomalies or other conditions determined by automated image analysis of the radiograph by a number of different machine learning models. Image annotation data may be generated based at least in part on obtained results associated with output of the multiple machine learning models, where the image annotation data indicates at least one location in the radiograph and an associated dental pathology, restoration, anatomy or anomaly detected at the at least one location by at least one of the machine learning models. A number of different pathologies may be identified and their locations marked within a single radiograph image.

Abstract: Systems and methods are described for automatically evaluating a claim submitted to an insurance carrier. Claim information and at least one image associated with the claim may be received, where the image has been submitted to a carrier as supporting evidence of a service performed by a submitter of the claim. The system may provide image data and other claim information from the submitted claim as input to machine learning models configured to identify whether the image data, such as a radiograph, supports the other data in the claim submission, such as a treatment code.

Abstract: Systems and methods are described for utilizing machine learning techniques to analyze data associated with one or more dental practices to identify missed treatment opportunities, future treatment opportunities, or provider performance metrics. The treatment opportunities or performance metrics may be determined or identified based at least in part on a comparison of patient data, such as data stored in association with a dental office's practice management system, with the output of one or more machine learning models' processing of associated radiograph images of the dental office's patients. The one or more machine learning models may include models that identify, from image data of a radiograph, a dental condition depicted in the radiograph, which may be mapped by a computer system to a corresponding dental treatment recommended for the identified dental condition.

Abstract: Systems and methods are provided for automatically marking locations within a radiograph of one or more dental pathologies, anatomies, anomalies or other conditions determined by automated image analysis of the radiograph by a number of different machine learning models. Image annotation data may be generated based at least in part on obtained results associated with output of the multiple machine learning models, where the image annotation data indicates at least one location in the radiograph and an associated dental pathology, restoration, anatomy or anomaly detected at the at least one location by at least one of the machine learning models. A number of different pathologies may be identified and their locations marked within a single radiograph image.

Abstract: Systems and methods are described for automatically evaluating a claim submitted to an insurance carrier. Claim information and at least one image associated with the claim may be received, where the image has been submitted to a carrier as supporting evidence of a service performed by a submitter of the claim. The system may provide image data and other claim information from the submitted claim as input to machine learning models configured to identify whether the image data, such as a radiograph, supports the other data in the claim submission, such as a treatment code.

Abstract: Systems and methods are described for utilizing machine learning techniques to analyze data associated with one or more dental practices to identify missed treatment opportunities, future treatment opportunities, or provider performance metrics. The treatment opportunities or performance metrics may be determined or identified based at least in part on a comparison of patient data, such as data stored in association with a dental office's practice management system, with the output of one or more machine learning models' processing of associated radiograph images of the dental office's patients. The one or more machine learning models may include models that identify, from image data of a radiograph, a dental condition depicted in the radiograph, which may be mapped by a computer system to a corresponding dental treatment recommended for the identified dental condition.

Abstract: Systems and methods are described for automatically evaluating a claim submitted to an insurance carrier. Claim information and at least one image associated with the claim may be received, where the image has been submitted to a carrier as supporting evidence of a service performed by a submitter of the claim. The system may provide image data and other claim information from the submitted claim as input to machine learning models configured to identify whether the image data, such as a radiograph, supports the other data in the claim submission, such as a treatment code.

Abstract: Systems and methods are provided for presenting an interactive user interface that visually marks locations within a radiograph of one or more dental pathologies, anatomies, anomalies or other conditions determined by automated image analysis of the radiograph by a number of different machine learning models. Annotation data generated by the machine learning models may be obtained, and one or more visual bounding shapes generated based on the annotation data. A user interface may present at least a portion of the radiograph's image data, along with display of the visual bounding shapes appearing to be overlaid over the at least a portion of the image data to visually mark the presence and location of a given pathology or other condition.

Abstract: Systems and methods are provided for automatically marking locations within a radiograph of one or more dental pathologies, anatomies, anomalies or other conditions determined by automated image analysis of the radiograph by a number of different machine learning models. Image annotation data may be generated based at least in part on obtained results associated with output of the multiple machine learning models, where the image annotation data indicates at least one location in the radiograph and an associated dental pathology, restoration, anatomy or anomaly detected at the at least one location by at least one of the machine learning models. A number of different pathologies may be identified and their locations marked within a single radiograph image.

Abstract: Systems and methods are provided for presenting an interactive user interface that visually marks locations within a radiograph of one or more dental pathologies, anatomies, anomalies or other conditions determined by automated image analysis of the radiograph by a number of different machine learning models. Annotation data generated by the machine learning models may be obtained, and one or more visual bounding shapes generated based on the annotation data. A user interface may present at least a portion of the radiograph's image data, along with display of the visual bounding shapes appearing to be overlaid over the at least a portion of the image data to visually mark the presence and location of a given pathology or other condition.

Abstract: Systems and methods are described for automatically evaluating a claim submitted to an insurance carrier. Claim information and at least one image associated with the claim may be received, where the image has been submitted to a carrier as supporting evidence of a service performed by a submitter of the claim. The system may provide image data and other claim information from the submitted claim as input to machine learning models configured to identify whether the image data, such as a radiograph, supports the other data in the claim submission, such as a treatment code.

Abstract: Systems and methods are provided for electronically managing content. In one embodiment, information identifying content items found on a page is received from a client system through code embedded on the page. Systems and methods for associating advertisement data with content items are also disclosed.

Abstract: Systems and methods are provided for electronically managing content. In one embodiment, information identifying content items found on a page is received from a client system through code embedded on the page. Systems and methods for associating advertisement data with content items are also disclosed.

Abstract: Systems and methods are provided for electronically managing content. In one embodiment, information identifying content items found on a page is received from a client system through code embedded on the page. Systems and methods for associating advertisement data with content items are also disclosed.

Abstract: Systems and methods are provided for electronically managing content. In one embodiment, information identifying content items found on a page is received from a client system through code embedded on the page. Systems and methods for associating advertisement data with content items are also disclosed.

Abstract: Systems and methods are provided for electronically managing content licenses. In one embodiment, information identifying content items found on a page is received from a client system through code embedded on the page, and a licensing database is searched to determine if the content is licensed and to determine the terms or payment method of the license. Systems and methods for associating advertisement data with licensed content items are also disclosed.