Abstract: Systems and methods for processing electronic imaging data obtained from medical imaging procedures, with use of trained artificial intelligence (AI) models, are disclosed herein. In an example, a use of a medical evaluation workflow involving an AI model includes: obtaining image data and non-image data associated with a medical imaging study; using at least one AI model to analyze the image data, with the trained AI model being validated with a defined governance standard to identify a characteristic or particular type of characteristic; identifying the characteristic with the AI model; and communicating the identified characteristic to a location associated with evaluation of the medical imaging study.

Abstract: Systems and methods for processing electronic imaging data obtained from medical imaging procedures are disclosed herein. Some embodiments relate to data processing mechanisms for medical imaging and diagnostic workflows involving the use of machine learning techniques such as deep learning, artificial neural networks, and related algorithms that perform machine recognition of specific features and conditions in imaging data. In an example, a deep learning model is selected for automated image recognition of a particular medical condition on image data, and applied to the image data to recognize characteristics of the particular medical condition. Based on the characteristics recognized by the automated image recognition on the image data, an electronic workflow for performing a diagnostic evaluation of the medical imaging study may be modified, updated, or prioritized.

Abstract: Systems and methods for processing electronic imaging data obtained from medical imaging procedures are disclosed herein. Some embodiments relate to data processing mechanisms for medical imaging and diagnostic workflows involving the use of machine learning techniques such as deep learning, artificial neural networks, and related algorithms that perform machine recognition of specific features and conditions in imaging data. In an example, a deep learning model is selected for automated image recognition of a particular medical condition on image data, and applied to the image data to recognize characteristics of the particular medical condition. Based on the characteristics recognized by the automated image recognition on the image data, an electronic workflow for performing a diagnostic evaluation of the medical imaging study may be modified, updated, or prioritized.

Abstract: Systems and methods for processing electronic imaging data obtained from medical imaging procedures are disclosed herein. Some embodiments relate to data processing mechanisms for medical imaging and diagnostic workflows involving the use of machine learning techniques such as deep learning, artificial neural networks, and related algorithms that perform machine recognition of specific features and conditions in imaging data. In an example, a deep learning model is selected for automated image recognition of a particular medical condition on image data, and applied to the image data to recognize characteristics of the particular medical condition. Based on the characteristics recognized by the automated image recognition on the image data, an electronic workflow for performing a diagnostic evaluation of the medical imaging study may be modified, updated, or prioritized.

Abstract: Systems and methods for processing electronic imaging data obtained from medical imaging procedures are disclosed herein. Some embodiments relate to data processing mechanisms for medical imaging and diagnostic workflows involving the use of machine learning techniques such as deep learning, artificial neural networks, and related algorithms that perform machine recognition of specific features and conditions in imaging data. In an example, a deep learning model is selected for automated image recognition of a particular medical condition on image data, and applied to the image data to recognize characteristics of the particular medical condition. Based on the characteristics recognized by the automated image recognition on the image data, an electronic workflow for performing a diagnostic evaluation of the medical imaging study may be modified, updated, or prioritized.

Abstract: Systems and methods for processing electronic imaging data obtained from medical imaging procedures are disclosed herein. Some embodiments relate to data processing mechanisms for medical imaging and diagnostic workflows involving the use of machine learning techniques such as deep learning, artificial neural networks, and related algorithms that perform machine recognition of specific features and conditions in imaging data. In an example, a deep learning model is selected for automated image recognition of a particular medical condition on image data, and applied to the image data to recognize characteristics of the particular medical condition. Based on the characteristics recognized by the automated image recognition on the image data, an electronic workflow for performing a diagnostic evaluation of the medical imaging study may be modified, updated, or prioritized.

Abstract: Systems and methods for coordinating a medical imaging workflow with the use of preparation and coordination actions, and similar pre-processing protocols are disclosed herein. Imaging procedure data from a medical imaging study, such as image data and order data produced from imaging procedures (e.g., radiological imaging procedures) at medical facilities is processed and presented for review to a preparing user. The preparing user is offered the ability to change the display characteristics of image presentation, supplement erroneous or incomplete data and information of the study, open a support request for the study, or associate prior or comparison images with the study. The changes provided by the preparing user within these or other portions of a preparation protocol may be used to affect a subsequent display of the medical imaging study, and in some examples, to affect the assignment of the study in the workflow to particular imaging users.

Abstract: Systems and methods for coordinating a medical imaging workflow with the use of preparation and coordination actions, and similar pre-processing protocols are disclosed herein. Imaging procedure data from a medical imaging study, such as image data and order data produced from imaging procedures (e.g., radiological imaging procedures) at medical facilities is processed and presented for review to a preparing user. The preparing user is offered the ability to change the display characteristics of image presentation, supplement erroneous or incomplete data and information of the study, open a support request for the study, or associate prior or comparison images with the study. The changes provided by the preparing user within these or other portions of a preparation protocol may be used to affect a subsequent display of the medical imaging study, and in some examples, to affect the assignment of the study in the workflow to particular imaging users.

Abstract: Systems and methods for processing electronic imaging data obtained from medical imaging procedures are disclosed herein. Some embodiments relate to data processing mechanisms for medical imaging and diagnostic workflows involving the use of machine learning techniques such as deep learning, artificial neural networks, and related algorithms that perform machine recognition of specific features and conditions in imaging data. In an example, a deep learning model is selected for automated image recognition of a particular medical condition on image data, and applied to the image data to recognize characteristics of the particular medical condition. Based on the characteristics recognized by the automated image recognition on the image data, an electronic workflow for performing a diagnostic evaluation of the medical imaging study may be modified, updated, or prioritized.

Abstract: The subject matter of this specification can be implemented in, among other things, a system for interfacing with multiple medical imaging modalities that includes a manifest generator for generating a manifest of medical images. The manifest can be used to determine an ordering or a layout of the medical images generated by the modalities, optionally as a function of the modality type, anatomical area, and other variables. In some embodiments, the various medical images and metadata may be received at an image order management system that parses the metadata and assembles the metadata into the manifest files that may be transmitted independently to remote interpretation sites, which in turn may be equipped with image viewer applications that analyze the manifest files and determine a rearranged ordering and/or grouping of the medical images, wherein the rearrangement is executed as a function of modality, anatomy, orientation and other variables.

Abstract: In one example, a two points on a medical image can be identified. In this example, three or more anatomical features can be identified and labeled. For example, these labels can be displayed with other medical images that show the same anatomical features. For example, points in the L1 and L5 vertebrae can be identified, and labels for the L1-L5 vertebrae can be displayed.

Abstract: The subject matter of this specification can be implemented in, among other things, a system for interfacing with multiple medical imaging modalities that includes a manifest generator for generating a manifest of medical images. The manifest can be used to determine an ordering or a layout of the medical images generated by the modalities, optionally as a function of the modality type, anatomical area, and other variables. In some embodiments, the various medical images and metadata may be received at an image order management system that parses the metadata and assembles the metadata into the manifest files that may be transmitted independently to remote interpretation sites, which in turn may be equipped with image viewer applications that analyze the manifest files and determine a rearranged ordering and/or grouping of the medical images, wherein the rearrangement is executed as a function of modality, anatomy, orientation and other variables.

Abstract: The subject matter of this specification can be implemented in, among other things, a system for interfacing with multiple medical imaging modalities that includes a normalization module for normalizing hanging protocols for displaying medical images. The normalization can be executed as a function of similar image characteristics shared between multiple sequences of medical images.

Abstract: The subject matter of this specification can be implemented in, among other things, a system for interfacing with multiple medical imaging modalities that includes a manifest generator for generating a manifest of medical images. The manifest can be used to determine an ordering or a layout of the medical images generated by the modalities, optionally as a function of the modality type, anatomical area, and other variables. In some embodiments, the various medical images and metadata may be received at an image order management system that parses the metadata and assembles the metadata into the manifest files that may be transmitted independently to remote interpretation sites, which in turn may be equipped with image viewer applications that analyze the manifest files and determine a rearranged ordering and/or grouping of the medical images, wherein the rearrangement is executed as a function of modality, anatomy, orientation and other variables.

Abstract: The subject matter of this specification can be implemented in, among other things, a system for interfacing with multiple medical imaging modalities that includes a normalization module for normalizing hanging protocols for displaying medical images. The normalization can be executed as a function of similar image characteristics shared between multiple sequences of medical images.

Abstract: In one embodiment, an image processing system can be configured to generate and adjust a radiological image. Upon receiving input via a user interface control indicating a specified adjustment in slab thickness of the radiological image, and upon receiving input via a user interface control indicating a specified reconstruction technique, the image processing system can generate an adjusted radiological image based on the received input, and can present the image on a display.

Abstract: In one example, a two points on a medical image can be identified. In this example, three or more anatomical features can be identified and labeled. For example, these labels can be displayed with other medical images that show the same anatomical features. For example, points in the L1 and L5 vertebrae can be identified, and labels for the L1-L5 vertebrae can be displayed.

Abstract: The subject matter of this specification can be implemented in, among other things, a system for interfacing with multiple medical imaging modalities that includes a manifest generator for generating a manifest of medical images. The manifest can be used to determine an ordering or a layout of the medical images generated by the modalities, optionally as a function of the modality type, anatomical area, and other variables. In some embodiments, the various medical images and metadata may be received at an image order management system that parses the metadata and assembles the metadata into the manifest files that may be transmitted independently to remote interpretation sites, which in turn may be equipped with image viewer applications that analyze the manifest files and determine a rearranged ordering and/or grouping of the medical images, wherein the rearrangement is executed as a function of modality, anatomy, orientation and other variables.