Abstract: Disclosed herein are systems, methods, and devices for an image-based, computer vision approach for anthropometric measurement of a user using an automatically generated three-dimensional model of the user. Also disclosed herein are systems, methods, and devices associated with a telehealth proctoring platform that can be used to remotely proctor, monitor, and manage patients over the course of a medical treatment plan. The telehealth proctoring platform may be used to collect and retrieve various kinds of data associated with a patient, such as at-home diagnostic test data, the anthropometric measurements of the patient, or the generated three-dimensional models in order to remotely monitor and track changes to the body of a patient over time and make dynamic adjustments to the patient's medical treatment plan.

Abstract: Disclosed herein are systems, methods, and devices for an image-based, computer vision approach for anthropometric measurement of a user using an automatically generated three-dimensional model of the user. Also disclosed herein are systems, methods, and devices associated with a telehealth proctoring platform that can be used to remotely proctor, monitor, and manage patients over the course of a medical treatment plan. The telehealth proctoring platform may be used to collect and retrieve various kinds of data associated with a patient, such as at-home diagnostic test data, the anthropometric measurements of the patient, or the generated three-dimensional models in order to remotely monitor and track changes to the body of a patient over time and make dynamic adjustments to the patient's medical treatment plan.

Abstract: A medical diagnostic kit are described. In one example, a medical diagnostic kit includes a container, and a plurality of medical diagnostic test kits located within the container. Each of the plurality of medical diagnostic test kits can include equipment necessary to perform a particular self-administered medical diagnostic test. The plurality of medical diagnostic test kits can include at least a first medical diagnostic kit adapted to facilitate user completion of a first medical diagnostic test and a second medical diagnostic kit adapted to facilitate user completion of a second medical diagnostic test different from the first medical diagnostic test. The container can include a machine-readable code located on an external surface of the container.

Abstract: Systems, methods, and devices associated with collecting and processing user information and data to generate a data structure suitable for use by an artificial intelligence algorithm for automated classification, such as automated diagnosis and intervention of the user's condition. A custom treatment plan can be generated that is tailored to the user based on information known about the user.

Abstract: Disclosed herein are systems, methods, and devices for an image-based, computer vision approach for anthropometric measurement of a user using an automatically generated three-dimensional model of the user. Also disclosed herein are systems, methods, and devices associated with a telehealth proctoring platform that can be used to remotely proctor, monitor, and manage patients over the course of a medical treatment plan. The telehealth proctoring platform may be used to collect and retrieve various kinds of data associated with a patient, such as at-home diagnostic test data, the anthropometric measurements of the patient, or the generated three-dimensional models in order to remotely monitor and track changes to the body of a patient over time and make dynamic adjustments to the patient's medical treatment plan.

Abstract: At-home diagnostic tests kits may be used by individuals to self-administer a diagnostic test. The diagnostic test kit can include one or more machine-readable codes on the surface of the test kit which may indicate information about the diagnostic test, such as whether a proctor is required during administration of the test. A software can be used to match a user with a proctor, and provide monitoring and quality control over the proctors performance.

Abstract: Methods and systems for automatically determining image characteristics serving as a basis for a diagnosis associated with an image study type. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive an image study from the at least one data source over the interface. The image study being of the image study type and including a plurality of images. The electronic processor is also configured to determine an image characteristic for each of the plurality of images and determine whether each of the plurality of images was used to establish a diagnosis. The electronic processor is also configured to store the image characteristic for each of the plurality of images and an indicator of whether each of the plurality of images was used to establish the diagnosis in a data structure.

Abstract: Methods and systems for automatically analyzing clinical images using models developed using machine learning. One system includes a server having an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive training information from the at least one data source over the interface. The training information includes a plurality of images and graphical reporting associated with each of the plurality of images. Each graphical reporting includes a graphical marker designating a portion of one of the plurality of images and diagnostic information associated with the portion of the one of the plurality of images. The electronic processor is also configured to perform machine learning to develop a model using the training information. The electronic processor is also configured to receive an image for analysis and automatically process the image using the model to generate a diagnosis for the image.

Abstract: Methods and systems for automatically selecting an implant for a patient. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive at least one image of the patient from the data source over the interface. The electronic processor is also configured to receive an intended location of the implant with reference to the at least one image. The electronic processor is also configured to automatically determine an anatomical structure based on the at least one image. The electronic processor is also configured to determine a preference. The electronic processor is also configured to automatically select one or more suggested implants based on the intended location, the anatomical structure, and the preference. The electronic processor is also configured to display the one or more suggested implants through a graphical user interface.

Abstract: Embodiments are provided for analyzing and characterizing video data. According to certain aspects, an analysis machine may analyze video data and optional audio data corresponding thereto using one or more artificial neural networks (ANNs). The analysis machine may process an output of this analysis with a recurrent neural network and an additional ANN. The output of the additional ANN may include a prediction vector comprising a set of values representative of a set of characteristics associated with the video data.

Abstract: Methods and systems for automatically determining image characteristics serving as a basis for a diagnosis associated with an image study type. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive an image study from the at least one data source over the interface. The image study being of the image study type and including a plurality of images. The electronic processor is also configured to determine an image characteristic for each of the plurality of images and determine whether each of the plurality of images was used to establish a diagnosis. The electronic processor is also configured to store the image characteristic for each of the plurality of images and an indicator of whether each of the plurality of images was used to establish the diagnosis in a data structure.

Abstract: Methods and systems for automatically analyzing clinical images and determining when additional imaging may aid a diagnosis. One system includes at least one data source and a server. The server includes an electronic processor and an interface for communicating with the at least one data source. The electronic processor is configured to receive an image of a patient from the at least one data source over the interface. The electronic processor is also configured to automatically process the image to generate a diagnosis for the image. The electronic processor is also configured to automatically determine, based on the diagnosis, at least one additional image for supplementing the diagnosis. The electronic processor is also configured to automatically locate the at least one additional image for the patient and when the at least one additional image is available, automatically update the diagnosis based on the at least one additional image.

Abstract: Methods and systems for automatically selecting an implant for a patient. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive at least one image of the patient from the data source over the interface. The electronic processor is also configured to receive an intended location of the implant with reference to the at least one image. The electronic processor is also configured to automatically determine an anatomical structure based on the at least one image. The electronic processor is also configured to determine a preference. The electronic processor is also configured to automatically select one or more suggested implants based on the intended location, the anatomical structure, and the preference. The electronic processor is also configured to display the one or more suggested implants through a graphical user interface.

Abstract: Methods and systems for automatically determining diagnosis discrepancies for clinical images. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive a first diagnosis from the at least one data source over the interface. The first diagnosis is specified by a diagnosing physician for an anatomical structure represented in an image. The electronic processor is also configured to determine a second diagnosis for the anatomical structure. The second diagnosis is generated after the first diagnosis. The electronic processor is also configured to store the first diagnosis and the second diagnosis in a data structure. The electronic processor is also configured to automatically determine a discrepancy between the first diagnosis and the second diagnosis based on the data structure.

Abstract: Methods and systems for automatically analyzing clinical images using models developed using machine learning. One system includes a server having an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive training information from the at least one data source over the interface. The training information includes a plurality of images and graphical reporting associated with each of the plurality of images. Each graphical reporting includes a graphical marker designating a portion of one of the plurality of images and diagnostic information associated with the portion of the one of the plurality of images. The electronic processor is also configured to perform machine learning to develop a model using the training information. The electronic processor is also configured to receive an image for analysis and automatically process the image using the model to generate a diagnosis for the image.

Abstract: Methods and systems for automatically mapping biopsy locations to pathology results. One system includes a server including an electronic processor and an interface for communicating with at least one data source and at least one pathology result source. The electronic processor is configured to receive an image from the at least one data source over the interface. The electronic processor is also configured to receive a biopsy location. The biopsy location includes a three-dimensional position mapped to a position within the image. The electronic processor is also configured to automatically locate an electronic pathology result for the biopsy location within the at least one pathology result source over the interface. The electronic processor is also configured to generate an electronic correlation between the biopsy location and the electronic pathology result. The electronic processor is also configured to display the image with the biopsy location marked within the image.

Abstract: Methods and systems for automatically determining a clinical image or portion thereof for display to a diagnosing physician. One system includes an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive training information from the at least one data source and determine a subset of images included in each of the plurality of image studies displayed to one or more diagnosing physicians. The electronic processor is also configured to perform machine learning to develop a model based on the training information and the subset of images included in each of the plurality of image studies and receive the image study. The electronic processor is also configured to process the image study using the model to determine a subset of the plurality of images and flag the subset of the plurality of images for manual review by the diagnosing physician.

Abstract: Methods and systems for automatically analyzing clinical images using rules and image analytics. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive training information from the at least one data source over the interface. The training information includes a plurality of images and graphical reporting associated with each of the plurality of images. The electronic processor is also configured to perform machine learning to develop a model using the training information and receive an image for analysis. The electronic processor is also configured to determine a set of rules for the image and automatically process the image using the model and the set of rules to generate a diagnosis for the image.

Abstract: Methods and systems for performing image analytics using graphical reporting associated with clinical images. One system includes at least one data source and a server. The server includes an electronic processor and an interface for communicating with the data source. The electronic processor is configured to receive training information from the at least one data source over the interface. The training information includes a plurality of images and graphical reporting associated with each of the plurality of images. Each graphical reporting includes a graphical marker designating a portion of one of the plurality of images and diagnostic information associated with the portion of the one of the plurality of images. The electronic processor is also configured to perform machine learning to develop a model using the training information. The model is used to automatically analyze an image.

Abstract: Systems and methods for remote diagnostic imaging. The systems include a diagnostic imaging kiosk. The kiosk includes, for example, a first housing module and a second housing module. The first housing module is configured to house, among other things, a diagnostic imaging system (e.g., an X-ray system). The second housing module is configured to house electronics associated with the operation, control, and networking of the kiosk. The electronics include, for example, a primary controller, an X-ray controller, an X-ray generator, an internal display controller, an external display controller, one or more routers, and a digital radiology module. The kiosk is configured to communicatively connect to a remote technician's workstation, and a remote technician at the workstation is able to remotely control the kiosk through a packet-switched network.