Abstract: Image processing engines can be utilized to inject studies into other commercial or independently-developed peer review systems which are designed to review the medical findings identified by a set of physicians. Image processing engines detect, confirm or verify findings by physicians or other engines, where the engines operate as peer reviewers. The engines can prospectively “learn” from the feedback when these images are reviewed by the physicians during diagnostic interpretation creating a closed-loop quality assurance process and fostering a community platform approach to engine development which is supported by the security, governance, access control, regulatory compliance and other features of the Peer Review System. Utilizing machine learning based on the data collected from peer review, the Peer Review System can adapt and improve its performance as well as the measured performance of the physicians using the system for diagnostic interpretation.

Abstract: An artificial intelligence findings system includes a findings engine that receives medical image data and generates findings based on the medical image data and image interpretation algorithms. An adjustment engine allows the user to adjust the findings to produce a report. A tracking module tracks findings and adjustments made to the findings by the user when producing the report. The tracking module produces tracking information. A machine learning engine receives the tracking information.

Abstract: A plurality of image processing engines are hosted within an image processing system. Each image processing engine performs one or more image processing operations or clinical content processing operations on medical images and clinical content. A user interface allows a user to configure the plurality of image processing engines for a particular study of images. The user interface allows the user to configure the plurality of image processing engines in any one of the following configurations: a series configuration where the image processing engines operate in series so that an output from one image processing engine serves as input to a next image processing engine; a parallel configuration where each image processing engine in the plurality of image processing engines operates without input from any other image processing engine in the plurality of image processing engines.

Abstract: A plurality of image processing engines are hosted within an image processing system. Each image processing engine performs one or more image processing operations or clinical content processing operations on medical images and clinical content. A user interface allows a user to configure the plurality of image processing engines for a particular study of images.

Abstract: A medical image viewing system allows multiple person viewing of rendered medical images, the medical image viewing system. A medical image processing system receives medical image data and manipulates the medical image data to produce first rendered medical image data that includes the medical image data plus image manipulation results. A first client system receives the first rendered medical image data from the medical image processing system. The first client system displays to a first end user on the first client system, a first 3D medical image that is based on the first rendered medical image data. The first 3D medical image is displayed within an augmented reality environment or a virtual reality environment so that within the augmented reality environment or the virtual reality environment the first end user has a first viewer location and a first viewing angle of the first 3D medical image.

Abstract: A medical image viewing system allows multiple person viewing of rendered medical images, the medical image viewing system. A medical image processing system receives medical image data and manipulates the medical image data to produce first rendered medical image data that includes the medical image data plus image manipulation results. A first client system receives the first rendered medical image data from the medical image processing system. The first client system displays to a first end user on the first client system, a first 3D medical image that is based on the first rendered medical image data. The first 3D medical image is displayed within an augmented reality environment or a virtual reality environment so that within the augmented reality environment or the virtual reality environment the first end user has a first viewer location and a first viewing angle of the first 3D medical image.

Abstract: Automated three-dimensional (3D) printing work flow through a network is provided. A network server receives through the network a request to convert 3D segmented medical image data to information print-ready for printing on a 3D printer. The 3D segmented medical image data is converted to a converted data file in a medical image data file format that allows multiple sub-segmentation of a particular human anatomy or region of interest. The medical image data file format allows for storage of mask segmentation information. Identifying information, including patient information, is removed from metadata of the 3D segmented medical image data when converting to the converted data file.

Abstract: A plurality of image processing engines are hosted within an image processing system. Each image processing engine performs one or more image processing operations or clinical content processing operations on medical images and clinical content. A user interface allows a user to configure the plurality of image processing engines for a particular study of images.

Abstract: An artificial intelligence findings system includes a findings engine that receives medical image data and generates findings based on the medical image data and image interpretation algorithms. An adjustment engine allows the user to adjust the findings to produce a report. A tracking module tracks findings and adjustments made to the findings by the user when producing the report. The tracking module produces tracking information. A machine learning engine receives the tracking information.

Abstract: Automated three-dimensional (3D) printing work flow through a network is provided. A network server receives through the network a request to convert 3D segmented medical image data to information print-ready for printing on a 3D printer. The 3D segmented medical image data is converted to a converted data file in a medical image data file format that allows multiple sub-segmentation of a particular human anatomy or region of interest. The medical image data file format allows for storage of mask segmentation information. Identifying information, including patient information, is removed from metadata of the 3D segmented medical image data when converting to the converted data file.

Abstract: An artificial intelligence findings system includes an artificial intelligence findings system. An image identification engine receives new image data pertaining to a patient and past reports and image data pertaining to the patient. The image identification engine uses the past image data pertaining to the patient to identify for study images of patient anatomical structures, anatomical anomalies and anatomical features within the new image data. A findings engine receives new image data and processes the new image data to generate findings based on the new image data and based on the identified for study images of patient anatomical structures, anatomical anomalies and anatomical features within the new image data. A communication interface provides the findings to a diagnostic review system that presents the findings to a user. An adjustment engine receives notifications of changes to findings made by a user when using the medical image interpretation system.

Abstract: An artificial intelligence findings system includes a findings engine that receives medical image data and generates findings based on the medical image data and image interpretation algorithms that take into account stored preferences for a user. A client viewer presents the findings to the user. An adjustment engine allows the user to adjust the findings to produce a report. A tracking module tracks findings and adjustments made to the findings by the user when producing the report. The tracking module produces tracking information. A machine learning engine receives the tracking information and based on the adjustments made to the findings by the user adjusts the stored preferences for the user.

Abstract: Multiple person viewing of rendered medical image is achieved by manipulating an image by the server to produce first rendered medical image data that includes the medical image data plus image manipulation results. A first 3-D medical image that is based on the first rendered medical image data is displayed within an augmented reality environment or a virtual reality environment so that the first end user has a first viewer location and a first viewing angle of the first 3-D medical image. A second 3-D medical image that is based on second rendered medical image data is displayed within the augmented reality environment or the virtual reality environment so that the second end user has at least one of a second viewer location and a second viewing angle that is different from at least one of the first viewer location and the first viewing angle of the first 3-D medical image.

Abstract: Multiple person viewing of rendered medical image is achieved by manipulating an image by the server to produce first rendered medical image data that includes the medical image data plus image manipulation results. A first 3-D medical image that is based on the first rendered medical image data is displayed within an augmented reality environment or a virtual reality environment so that the first end user has a first viewer location and a first viewing angle of the first 3-D medical image. A second 3-D medical image that is based on second rendered medical image data is displayed within the augmented reality environment or the virtual reality environment so that the second end user has at least one of a second viewer location and a second viewing angle that is different from at least one of the first viewer location and the first viewing angle of the first 3-D medical image.

Abstract: A work flow is provided through a network. A network server receives through the network a request to convert 3D segmented medical image data to information print-ready for printing on a 3D printer or rendered for viewing. Access to converted data is limited to one or more authorized users. The 3D segmented medical image data to a converted data file. The converted data file is in a medical image data file format that allows multiple sub-segmentation of a particular human anatomy or region of interest. The medical image data file format allows for storage of mask segmentation information, color palette, shadowing, transparency and opacity.

Abstract: A work flow is provided through a network. A network server receives through the network a request to convert 3D segmented medical image data to information print-ready for printing on a 3D printer or rendered for viewing. Access to converted data is limited to one or more authorized users. The 3D segmented medical image data to a converted data file. The converted data file is in a medical image data file format that allows multiple sub-segmentation of a particular human anatomy or region of interest. The medical image data file format allows for storage of mask segmentation information, color palette, shadowing, transparency and opacity.

Abstract: An artificial intelligence findings system includes an artificial intelligence findings system. An image identification engine receives new image data pertaining to a patient and past reports and image data pertaining to the patient. The image identification engine uses the past image data pertaining to the patient to identify for study images of patient anatomical structures, anatomical anomalies and anatomical features within the new image data. A findings engine receives new image data and processes the new image data to generate findings based on the new image data and based on the identified for study images of patient anatomical structures, anatomical anomalies and anatomical features within the new image data. A communication interface provides the findings to a diagnostic review system that presents the findings to a user. An adjustment engine receives notifications of changes to findings made by a user when using the medical image interpretation system.

Abstract: An artificial intelligence findings system includes a findings engine that receives medical image data and generates findings based on the medical image data and image interpretation algorithms that take into account stored preferences for a user. A client viewer presents the findings to the user. An adjustment engine allows the user to adjust the findings to produce a report. A tracking module tracks findings and adjustments made to the findings by the user when producing the report. The tracking module produces tracking information.

Abstract: Image processing engines can be utilized to inject studies into other commercial or independently-developed peer review systems which are designed to review the medical findings identified by a set of physicians. Image processing engines detect, confirm or verify findings by physicians or other engines, where the engines operate as peer reviewers. The engines can prospectively “learn” from the feedback when these images are reviewed by the physicians during diagnostic interpretation creating a closed-loop quality assurance process and fostering a community platform approach to engine development which is supported by the security, governance, access control, regulatory compliance and other features of the Peer Review System. Utilizing machine learning based on the data collected from peer review, the Peer Review System can adapt and improve its performance as well as the measured performance of the physicians using the system for diagnostic interpretation.