AI Metrics is a thin client software application used with general purpose computing hardware for the display and visualization of medical image data, supporting images and anatomical datasets, such as CT and MR. AI Metrics is designed as a workflow automation application with analytical tools to help the user assess, categorize and document the extent of a disease and/or tumor response to therapy in accordance with user selected standards (e.g. RECIST 1.1) and assess changes in imaging findings over multiple time-points. AI Metrics supports the interpretation and evaluation of examinations and follow up documentation of findings within healthcare institutions, for example, in Radiology, Oncology, and other Medical Imaging environments. AI Metrics functionality provides for communication, storage, processing, rendering, and display of DICOM compliant image data derived from various sources, navigation through images, selection of regions of interest, generation of information from those regions, evaluation in accordance with user selected standards, and generation of a structured report. The user controls these functions with a system of interactive menus and semi-automated or manual workflow automation tools, including: • Manual annotation tools for users to select regions of interest (ROIs), • Semi-automatic lesion segmentation suggestions for user-selected ROIs, • Automatic measurement and display of long and short axis of segmented lesions, • Automatic tabulation and summation of measurements, • Semi-automatic lesion labelling suggestions for anatomical location (organ, body region, and laterality), • Automatic calculation of quantitative and qualitative metrics using annotation data in accordance with the selected criteria, and • Automatic generation of a structured report that includes the annotation data and calculated quantitative and qualitative metrics presented in a graph, table, key images, and structured text report.
A software program dedicated for installation on a central server as part of a picture archiving and communication system (PACS) to allow storage, retrieval, analysis, and management of a broad range of radiological images throughout the healthcare system. As a PACS-dedicated software, it is not primarily intended for diagnosis support, triage, or treatment planning. It does not incorporate adaptive/dynamic machine learning (ML) models.
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