Abstract: Automatically determining a medical recommendation for a patient based on multiple medical images from multiple different medical imaging modalities. In some embodiments, a method may include receiving a first and second medical images of a patient from first and second medical imaging modalities, mapping a first region of interest (ROI) on the first medical image to a second ROI on the second medical image, generating first annotation data related to the first ROI and second annotation data related to the second ROI, generating first medical clinical data related to the first ROI and second medical clinical data related to the second ROI, inputting, into a machine learning classifier, the first and second annotation data and the first and second medical clinical data, and automatically determining, by the machine learning classifier, a medical recommendation for the patient related to a medical condition of the patient.

Abstract: Automatically determining a medical recommendation for a patient based on multiple medical images from multiple different medical imaging modalities. In some embodiments, a method may include receiving a first and second medical images of a patient from first and second medical imaging modalities, mapping a first region of interest (ROI) on the first medical image to a second ROI on the second medical image, generating first annotation data related to the first ROI and second annotation data related to the second ROI, generating first medical clinical data related to the first ROI and second medical clinical data related to the second ROI, inputting, into a machine learning classifier, the first and second annotation data and the first and second medical clinical data, and automatically determining, by the machine learning classifier, a medical recommendation for the patient related to a medical condition of the patient.

Abstract: Automatically determining a medical recommendation for a patient based on multiple medical images from multiple different medical imaging modalities. In some embodiments, a method may include receiving a first and second medical images of a patient from first and second medical imaging modalities, mapping a first region of interest (ROI) on the first medical image to a second ROI on the second medical image, generating first annotation data related to the first ROI and second annotation data related to the second ROI, generating first medical clinical data related to the first ROI and second medical clinical data related to the second ROI, inputting, into a machine learning classifier, the first and second annotation data and the first and second medical clinical data, and automatically determining, by the machine learning classifier, a medical recommendation for the patient related to a medical condition of the patient.

Abstract: Automatically determining a medical recommendation for a patient based on multiple medical images from multiple different medical imaging modalities. In some embodiments, a method may include receiving a first and second medical images of a patient from first and second medical imaging modalities, mapping a first region of interest (ROI) on the first medical image to a second ROI on the second medical image, generating first annotation data related to the first ROI and second annotation data related to the second ROI, generating first medical clinical data related to the first ROI and second medical clinical data related to the second ROI, inputting, into a machine learning classifier, the first and second annotation data and the first and second medical clinical data, and automatically determining, by the machine learning classifier, a medical recommendation for the patient related to a medical condition of the patient.

Abstract: A method and system for visually annotating an image. Annotations and notes to images, such as digital medical and healthcare images, may be stored in a structured vector representation alongside image information in a single, non-volatile and portable file or in a separate file from the image. The annotations may be composed of point, line and polygon drawings and text symbols, labels or definitions and captions or descriptions. The annotations may be structured in a manner that facilitates grouping and manipulation as user defined groups. The annotations may be related to an image but not inextricably bound such that the original image is completely preserved. Annotations may further be selectively displayed on the image for context appropriate viewing. The annotations may be retrieved for purposes such as editing, printing, display, indexing and reporting for example, and may be displayed on an image for interactive use with an embedded self-contained user interface.

Abstract: A method and system for visually annotating an image. Annotations and notes to images, such as digital medical and healthcare images, may be stored in a structured vector representation alongside image information in a single, non-volatile and portable file or in a separate file from the image. The annotations may be composed of point, line and polygon drawings and text symbols, labels or definitions and captions or descriptions. The annotations may be structured in a manner that facilitates grouping and manipulation as user defined groups. The annotations may be related to an image but not inextricably bound such that the original image is completely preserved. Annotations may further be selectively displayed on the image for context appropriate viewing. The annotations may be retrieved for purposes such as editing, printing, display, indexing and reporting for example, and may be displayed on an image for interactive use with an embedded self-contained user interface.