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NinesMeasure is a semi-automatic tool indicated for use by trained radiologists to aid in the analysis and review of adult thoracic CT images. NinesMeasure provides quantitative information about pulmonary nodule size on a single study or over the time course of several thoracic studies by providing long and short axis diameter measurements in the axial plane. Based on analysis of DICOM images and provided input from a radiologist, indicating the location of the pulmonary nodule, the device uses artificial intelligence algorithms to automatically perform the measurements, and allows the axial measurements to be displayed and reviewed. NinesMeasure is limited for use on solid pulmonary nodules. The device is intended to be used as a measurement tool by a trained radiologist and is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm a diagnosis. The device does not alter the original medical image.

NinesMeasure is a semi-automatic, diagnostic patient imaging tool used to measure the size of selected pulmonary nodules in a radiological image. The software system is comprised of a set of software modules for performing image analysis at a specified image location to calculate measurements of pulmonary nodules on adult thoracic CT images. The system operates over a standard network interface and receives the DICOM images and coordinates of the pulmonary nodule to measure. The system then returns the measurements for the long and short axis diameters for review by a trained radiologist. NinesMeasure is designed to be used with a standard PACS, where the user can indicate a location of the pulmonary nodule to measure, and then review and edit the measurements on the DICOM image. The image analysis uses Artificial Intelligence (Al) technology to analyze chest CT images for computing the measurements. Specifically, the device utilizes a machine learning (ML) algorithm to compute segmentations of nodules, from which the long and short axis measurements are then calculated.

NinesAl is a parallel workflow tool indicated for use by hospital networks and trained clinicians to identify images of specific patients to a radiologist, independent of standard of care workflow, to aid in prioritizing and performing the radiological review. NinesAl uses artificial intelligence algorithms to analyze head CT images for findings suggestive of a pre-specified emergent clinical condition. The software automatically analyzes Digital Imaging and Communications in Medicine (DICOM) images as they arrive in the Picture Archive and Communication System (PACS) using machine learning algorithms. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the software analyzes head CT images of the brain to assess the suspected presence of intracranial hemorrhage and/or mass effect and identifies images with potential emergent findings in a radiologist's worklist. NinesAl is intended to be used as a triage tool limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm a diagnosis. Additionally, preview images displayed to the radiologist outside of the DICOM viewer are non-diagnostic quality and should only be used for informational purposes.

NinesAl notifies a radiologist of the presence of a suspected critical abnormality in a radiological image. The software system is a complete package comprised of image analysis software and a workstation module that is used to alert the radiologist. The image analysis can also be configured to send HL7 messages and DICOM secondary series. The image analysis uses Artificial Intelligence (AI) technology to analyze non contrast CT Head scans for the presence of Intracranial Hemorrhage and/or Mass Effect. More specifically, the device utilizes two machine learning (ML) algorithms to detect each finding respectively. NinesAl is a software device and does not come into contact with patients. All radiological studies are still reviewed by trained radiologists. NinesAl is meant to be used as an aid for case prioritization.