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VUNO Med-Chest X-ray Triage/VUNO Med-CXR Link Triage is a radiological computer-assisted triage and notification software that analyzes adult chest X-ray images for the presence of prespecified suspected critical findings (pleural effusion and/or pneumothorax). VUNO Med-Chest X-ray Triage/VUNO Med-CXR Link Triage uses an artificial intelligence algorithm to analyze images for features suggestive of critical findings and provides case-level output available in the PACS/ workstation for worklist prioritization or triage. As a passive notification for prioritization-only software tool within standard of care workflow, VUNO Med-Chest X-ray Triage/VUNO Med-CXR Link Triage does not send a proactive alert directly to the appropriately trained medical specialists. VUNO Med-Chest X-ray Triage/VUNO Med-CXR Link Triage is not intended to direct attention to specific portions of an image or to anomalies other than pleural effusion and/or pneumothorax. Its results are not intended to be used on a stand-alone basis for clinical decision-making.

VUNO Med-Chest X-ray Triage/VUNO Med-CXR Link Triage is an automated computerassisted triage and notification software that analyzes adult chest X-ray images for the presence of pleural effusion and pneumothorax. It is based on an artificial intelligence analysis model, specifically a convolutional network (CNN), which employs deep learning technology to learn features from data. The training data is sourced from 4 distinct sites of South Korea and India data provider, including medical imaging centers, data partners, and medical hospitals, and over 13 different modality manufacturers such as GE. Philps, FUJI, Canon, Samsung, SIEMENS, etc. A "locked" algorithm is used, and the same input gives the same results every time. The software receives an image of a frontal chest radiograph and automatically analyzes it for the presence of pre-specified critical findings. If any findings are suspected, the image is flagged, and a passive notification is provided to the user. Subsequently, trained radiologists or healthcare professionals should make the final decision which is the standard of care at present. A user interface is provided for visualization, displaying the loaded image and any detected findings. The data can be transmitted from Picture Archive and Communications Systems (PACS) using the DICOM protocol.

The VUNO Med-DeepBrain is intended for automatic labeling, quantification of segmentable brain structures from a set of MR images. The software is intended to automate the current manual process of identifying, labeling and quantifying segmentable brain structures identified on MR images. The users are trained healthcare professionals who work with medical imaging. The product is used in an office-like environment.

The VUNO Med-DeepBrain provides brain structural information based on the brain MR image. Input images for analysis are 3D T1 weighted brain MR images and 2D T2 flair brain MR images. Once the recommended images are uploaded, automated brain segmentation is performed and provides volumetric data of brain regions. It is displayed in the viewer with a color map. VUNO Med-DeepBrain is intended for automatic labeling, visualization, and volumetric quantification of segmentable brain structures and lesions from a set of MR images. It takes a 3D T1 MR image as input and gives segmented brain structures and lesions, and volumetric quantification. The user interface is provided for the visualization. The segmented structures are displayed as a color map and the user can view regions by selecting the name of the region. The 2D T2 Flair MR image is taken for lesion quantification. In addition, the uploaded image can be compared to the normative percentile and prior images when applicable. The user can download and print the result in a report format. The data can be received and sent through the Picture Archive and Communications Systems (PACS) using the DICOM protocol.