Uses AI algorithms for image finding segmentation, anatomic labelling, and longitudinal tracking
Combines AI with guided workflows and automated reports that include a graph, table, key images, and structured text
Supports multiple modules consisting of disease-specific AI tools, algorithms, and guided clinical and research workflows
Guided workflows with conformity checks and built-in rules adhere to therapy response assessment criteria commonly used in clinical trials (e.g., RECIST 1.1, Lugano). The systematic workflow processes automatically perform the necessary calculations (e.g., total tumor burden), determine the therapy response category, and generate reports. The user controls these functions with a system of interactive menus and semi-automated or manual workflow automation tools and algorithms.
The AI Metrics Advanced Cancer Module is a software application for viewing, manipulation, storage, annotation, analysis, and comparison of medical images pertaining to cancer masses from multiple imaging modalities and/or multiple time-points. The application supports data and images such as CT and MR. The AI Metrics Advanced Cancer Module provides analytical tools to help the user assess and document the extent of a disease, assess responses to therapy in accordance with user selected standards, and assess changes in imaging findings over multiple time-points.
The Image Research Management System (IRMS) supports radiology departments and cancer centers in providing read services for clinical trials and research studies. The IRMS provides the tools needed to manage service requests and coordinate reads. Through a set of interconnected dashboards, the IRMS collects and shares relevant patient and trial information with the AI Metrics Image Analysis Platform, providing context to the reader and ensuring the correct study protocol is followed. The IRMS also provides a customizable worklist and functionality to assign readers, export radiomics data and segmentation / annotation masks for all annotated regions-of-interest, and generate billing reports.
Augmented intelligence (AI) algorithms are utilized to assist the user in achieving a high degree of efficiency and accuracy for segmentation, anatomic labelling, and longitudinal co-registration of findings.