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 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 identifying fraud, waste or abuse in health insurance claims submitted to insurance companies by healthcare providers. Insurance claim information and at one image associated with the insurance claim may be received, where the image has been submitted by a healthcare provider to an insurance carrier as supporting evidence of a medical service performed by the healthcare provider. The system may generate a digital signature representing the image, then may compare the digital signature generated for the image to previously generated digital signatures of other images that have been submitted in association with other insurance claims. The system may then determine a likelihood that the given insurance claim is associated with fraud, waste or abuse, based in part on whether the digital signature is identical or close to one or more of the previously generated digital signatures.

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 automatically identifying fraud, waste or abuse in health insurance claims submitted to insurance companies by healthcare providers. Insurance claim information and at one image associated with the insurance claim may be received, where the image has been submitted by a healthcare provider to an insurance carrier as supporting evidence of a medical service performed by the healthcare provider. The system may generate a digital signature representing the image, then may compare the digital signature generated for the image to previously generated digital signatures of other images that have been submitted in association with other insurance claims. The system may then determine a likelihood that the given insurance claim is associated with fraud, waste or abuse, based in part on whether the digital signature is identical or close to one or more of the previously generated digital signatures.

Abstract: Systems and methods are provided for generating labeled image data for improved training of an image classifier, such as a multi-layered machine learning model configured to identify target image objects in image data. When the initially trained classifier is unable to identify a particular object in input image data, such as an object that did not appear in initial training data, feature information determined by the classifier for the given image data may be provided to a clustering model. The clustering model may group image data having similar features into different clusters or groups, which may in turn be labeled at the group level by an annotator. The image data assigned to the different clusters, along with the associated labels, may subsequently be used as training data for training a classifier to identify the labeled objects in images.

Abstract: Systems and methods are described for automatically identifying fraud, waste or abuse in health insurance claims submitted to insurance companies by healthcare providers. Insurance claim information and at one image associated with the insurance claim may be received, where the image has been submitted by a healthcare provider to an insurance carrier as supporting evidence of a medical service performed by the healthcare provider. The system may generate a digital signature representing the image, then may compare the digital signature generated for the image to previously generated digital signatures of other images that have been submitted in association with other insurance claims. The system may then determine a likelihood that the given insurance claim is associated with fraud, waste or abuse, based in part on whether the digital signature is identical or close to one or more of the previously generated digital signatures.

Abstract: Systems and methods are described for automatically identifying fraud, waste or abuse in health insurance claims submitted to insurance companies by healthcare providers. Insurance claim information and at one image associated with the insurance claim may be received, where the image has been submitted by a healthcare provider to an insurance carrier as supporting evidence of a medical service performed by the healthcare provider. The system may generate a digital signature representing the image, then may compare the digital signature generated for the image to previously generated digital signatures of other images that have been submitted in association with other insurance claims. The system may then determine a likelihood that the given insurance claim is associated with fraud, waste or abuse, based in part on whether the digital signature is identical or close to one or more of the previously generated digital signatures.

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 generating labeled image data for improved training of an image classifier, such as a multi-layered machine learning model configured to identify target image objects in image data. When the initially trained classifier is unable to identify a particular object in input image data, such as an object that did not appear in initial training data, feature information determined by the classifier for the given image data may be provided to a clustering model. The clustering model may group image data having similar features into different clusters or groups, which may in turn be labeled at the group level by an annotator. The image data assigned to the different clusters, along with the associated labels, may subsequently be used as training data for training a classifier to identify the labeled objects in images.