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The Viz Subdural+ (Subdural Plus) device is intended for automatic labeling, visualization and quantification of collections in the subdural space from a set of Non-Contrast Head CT (NCCT) images. The software is intended to automate the current manual process of identifying, labeling and quantifying the volume of collections in the subdural space identified on NCCT images. Viz Subdural + provides volumes from NCCT images acquired at a single time point. The Viz Subdural+ software is intended for labeling subdural collections and reporting the grayscale value of the collection, widest width of the subdural collection, and midline shift. The device output should be reviewed along with the patient's original images by a physician qualified to interpret brain CT images.

Viz Subdural+ is a software-only device that uses a locked artificial intelligence machine learning (AI/ML) algorithm to process and analyze non-contrast CT (NCCT) scans of the head to automatically measure the collections in the subdural region in the brain and midline shift. The device output provides visual overlays of automatically measured subdural collections where the overlay opacity (intensity) corresponds to the grayscale value of the collection within the native NCCT, and reports the total volume and widest width of the subdural collections. The device also automates and reports the measure of midline shift. The results of the automated measurement are provided in a summary series and segmentation series in DICOM format. The summary series consists of a summary table of subdural collections, snapshot of each collection and a midline shift measurement. The first slice of the Subdural+ summary series summarizes the measurement results of each subdural collection (volume and widest width), total volume and midline shift in tabular format. The summary series also contains a snapshot of each subdural collection and a snapshot of the midline shift measurement. The segmentation series shows an RGB overlay where a subdural collection is identified by a colored overlay with the color intensity corresponding to the HU values of the original image on each slice of the input series of the segmented region. On slices with an overlay representing a measured subdural collection, the volume of the subdural collection is provided. The midline shift is overlaid and provided on the slice where the midline shift is measured. Images are automatically forwarded from the Healthcare Facility and sent to Viz.ai's Backend Server after acquisition at the CT scanner. Viz Subdural+ is hosted on Viz.ai's Backend Server and automatically analyzes applicable NCCT scans that are acquired on CT scanners and are forwarded to Viz.ai's Backend Server. The results of the analysis are exported in DICOM format and are sent to a DICOM destination (e.g., PACS) where they are available for review by radiologists, neurologists, neuro-surgeons, interventional neuroradiologists, or other appropriately trained professionals to assist in the measurement of subdural collection volume, widest subdural collection width and midline shift.

The Viz HDS device is intended for automatic labeling, visualization, and quantification of segmentable brain structures from a set of Non-Contrast CT (NCCT) head scans. The software is intended to automate the current manual process of identifying, labeling, and quantifying the volume of segmentable brain structures identified on NCCT images. Viz HDS provides volumes from NCCT scans acquired at a single time point. The Viz HDS software is indicated for use in the analysis of the following structures: Intracranial Hyperdensities, Lateral Ventricles and Midline Shift. The device output should be reviewed along with patient's original images by a physician.

Viz HDS is a software-only device that uses a locked artificial intelligence machine learning (AI/ML) algorithm to processes non-contrast head CT scans to outline intracranial hyperdensity areas, lateral ventricles (right and left), midline shift, and then quantify the volume of intracranial hyperdensity(ies), volume of lateral ventricle asymmetry ratio and distance of midline shift. Viz HDS analyzes the head NCCT series in DICOM format and produces a summary series and a segmentation series in DICOM format. The summary series is a two-slice output: a single slice from the NCCT series with segmented areas overlaid on it, and a summary table providing the calculated measurements. The segmentation series shows an RGB overlay, on each slice of the input series, of the lateral ventricles and hyperdensity(ies) segmentation masks and a midline shift. For slices including hyperdensities or ventricle/ventricles, its volume would be mentioned in a color legend that is also overlaid on the slice. The colors are only for visual differentiation between the segmented regions, the colors don't have a meaning on their own. The device output is exported in DICOM format, which is sent to a pre-configured PACS destination together with the original NCCT series for review by a physician to aid in the assessment of measuring intracranial hyperdensity(ies), lateral ventricles, and midline shift.

Viz HCM is intended to be used in parallel to the standard of care to analyze recordings of 12-lead ECG made on compatible ECG devices. Viz HCM is capable of analyzing the ECG, detecting signs associated with hypertrophic cardiomyopathy (HCM), and allowing the user to view the ECG and analysis results. Viz HCM is indicated for use on 12-lead ECG recordings collected from patients 18 years of age or older. Viz HCM is not intended for use on patients with implanted pacemakers. Viz HCM is limited to analysis of ECG data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis. Viz HCM identifies patients for further HCM follow-up and does not replace the current standard of care methods for diagnosis of HCM. The results of the device are not intended to rule-out HCM follow-up.

The Viz HCM ECG Analysis Algorithm (HCM Algorithm) is a machine learning-based software algorithm that analyzes 12-lead electrocardiograms (ECGs) for characteristics suggestive of hypertrophic cardiomyopathy (HCM). The mobile software module enables the end user to receive and toggle notifications for ECGs determined by the Viz HCM ECG Analysis Algorithm to contain signs suggestive of HCM. The Viz HCM is a Software as a Medical Device (SaMD) intended to analyze ECG signals collected as part of a routine clinical assessment, independently and in parallel to the standard of care. Viz HCM is a combination of software modules that consists of an ECG analysis software algorithm and mobile application software module.

Viz LVO is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of standard of care workflow. Viz LVO uses an artificial intelligence algorithm to analyze images for findings suggestive of a pre-specified clinical condition and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes CT angiogram images of the brain acquired in the acute setting, and sends notifications to a neurovascular specialist that a suspected large vessel occlusion has been identified and recommends review of those images. Images can be previewed through a mobile application. Viz LVO is intended to analyze terminal ICA and MCA-M1 vessels for LVOs. Images that are previewed through the mobile application are compressed and are for informational purposes only and not intended for diagnostic use beyond notification. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests. Viz LVO 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 diagnosis.

Viz LVO is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to analyze images for findings suggestive of a suspected large vessel occlusion and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation. Viz LVO was previously granted a de-novo as ContaCT (DEN170073); following the granting of the denovo the device name was changed to Viz LVO. Viz LVO is a combination of software modules that allow for detection and notification of patients with a suspected large vessel occlusion. Viz LVO consists of an algorithm and mobile application software module. The Viz LVO Image Analysis Algorithm (LVO Detection Algorithm) is a locked, artificial intelligence machine learning (AI/ML) software algorithm that analyzes CTA images of the head for a suspected large vessel occlusion (LVO). The LVO Detection Algorithm is hosted on Viz.ai's Backend Server and analyzes applicable stroke-protocoled CTA images of the head that are acquired on CT scanners and are forwarded to Viz.ai's Backend Server. Upon detection of a suspected LVO, the LVO Detection Algorithm sends a notification of the suspected finding. The Viz LVO Mobile Notification Software is a software module that enables the end user to receive and toggle notifications for suspected large vessel occlusions identified by the LVO Detection Algorithm. The LVO Mobile Notification Software module is implemented into Viz.ai's generic nondiagnostic DICOM image viewer, Viz VIEW (formerly referred to as the Imaging Viewing Software in the previous submission, DEN170073), which displays CT scans that are sent to Viz.ai's Backend Server. When the Viz LVO Mobile Notification Software module is enabled for a user, the user can receive and toggle the notifications for patients with a suspected LVO, view a unique list of patients with a suspected LVO (as determined by the LVO Detection Algorithm), and view the non-diagnostic CT scan of the patient through the Viz VIEW mobile application. Image viewing through the mobile application interface is for informational purposes only and is not for diagnostic use.

The Viz RV/LV Software device is designed to measure the maximal diameters of the right and left ventricles of the heart from a volumetric CTPA acquisition and report the ratio of those measurements. Viz RV/LV analyzes cases using an artificial intelligence algorithm to identify the location and measurements of the ventricles. The Viz RV/LV software provides the user with annotated images showing ventricular measurements. Its results are not intended to be used on a stand-alone basis for clinical decision-making or otherwise preclude clinical assessment of CTPA cases.

The Viz RV/LV is a software-only device that uses a locked artificial intelligence machine learning (AI/ML) algorithm to measure the maximal diameters of the right and left ventricles of the heart from a computed tomography pulmonary angiogram (CTPA) and report the ratio of those measurements. Viz RV/LV produces an Annotated Image Series (Figure 1) and an RV/LV Summary Report (Figure 2) in DICOM format. The Annotated Image Series shows an RGB overlay on each slice of the input scan: The red and blue solid lines indicate the maximum ventricular diameter for each ventricle. The dashed line indicates a diameter measured on a slice that is within 10 slices of the global maximum ventricular diameter. The interventricular septum is marked in solid green on all images where diameters are marked. The maximal diameter is presented along with solid lines on slices with global maximum diameter. The RV/LV Summary Report summarizes the results of the ventricle analysis and shows the slices with the maximum right and left ventricular diameters. The lines measuring the maximum RV and LV diameters are displayed over the original CTPA slice image, along with the lengths of the largest RV and LV diameters, and the RV/LV ratio. Viz RV/LV is hosted on Viz.ai's Backend Server and analyzes applicable CTPA scans that are acquired on CT scanners and are forwarded to Viz.ai's Backend Server. The results of the analysis are exported in DICOM format are sent to a PACS destination for review by thoracic radiologists, general radiologists, pulmonologists, cardiologists, or other similar physicians to assist in the assessment of right ventricle enlargement.

Viz SDH is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of standard of care workflow. Viz SDH uses an artificial intelligence algorithm to analyze images for findings suggestive of a prespecified clinical condition and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the head for subdural hemorrhage and sends notifications to a neurovascular or neurosurgical specialist that a suspected subdural hemorrhage has been identified and recommends review of those images can be previewed through a mobile application. Images that are previewed through the mobile application may be compressed and are for informational purposes only and not intended for diagnostic use beyond notification. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests. Viz SDH 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 diagnosis.

Viz SDH is a software-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to an appropriate specialist, such as a neurovascular specialist or neurosurgeon, independent of the standard of care workflow. The system automatically receives and analyses non-contrast CT (NCCT) studies of patients for image features that indicate the presence of a subdural hemorrhage (SDH) using an artificial intelligence algorithm, and upon detection of a suspected SDH, sends a notification so as to alert a specialist clinician of the case. Viz SDH is a combination of software modules that consists of an image analysis software algorithm and mobile application software module. The Viz SDH image analysis software algorithm is an artificial intelligence machine learning (AI/ML) software algorithm that analyzes non-contrast CT images of the head for a subdural hemorrhage. The Viz SDH Image Analysis Algorithm is hosted on Viz.ai's servers and analyzes applicable stroke-protocoled NCCT images of the head that are acquired on CT scanners and are forwarded to Viz.ai servers. Upon detection of a suspected subdural hemorrhage, the Viz SDH Image Analysis Algorithm sends a notification of the suspected finding. Viz SDH includes a mobile software module that enables the end user to receive and toggle notifications for suspected subdural hemorrhages identified by the Viz SDH Image Analysis Algorithm. The Viz SDH mobile notification software module is implemented into Viz.ai's nondiagnostic DICOM image viewer, Viz VIEW, which displays CT scans that are sent to Viz.ai's servers. When the Viz SDH mobile notification software module is enabled for a user, the user can receive and toggle the notifications for patients with a suspected subdural hemorrhage, view a unique patient list of patients with a suspected subdural hemorrhage, and view the nondiagnostic CT scan of the patient through the Viz VIEW mobile application. Image viewing through the mobile application interface is for non-diagnostic purposes only.

Viz ANEURYSM (Viz ANX) is a radiological computer-assisted triage and notification software device for analysis of CT images of the head. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and prioritizing studies with suspected aneurysms during routine patient care. Viz ANEURYSM uses an artificial intelligence algorithm to analyze images and highlight studies with suspected aneurysms in a standalone application for study list prioritization or triage in parallel to ongoing standard of care. The device generates compressed preview images that are meant for informational purposes only and not intended for diagnostic use. The device does not alter the original medical image and is not intended to be used as a diagnostic device. Analyzed images are available for review through the standalone application. When viewed through the standalone application the images are for informational purposes only and not for diagnostic use. The results of Viz ANEURYSM, in conjunction with other clinical information and professional judgment, are to be used to assist with triage/prioritization of medical images. Radiologists who read the original medical images are responsible for the diagnostic decision. Viz ANEURYSM 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 diagnosis. Viz ANEURYSM is limited to detecting aneurysms at least 4mm in diameter.

Viz ANEURYSM (Viz ANX) is a radiological computer-assisted triage and notification software device for analysis of CTA images of the head. The software automatically receives and analyzes CT angiogram (CTA) imaging of the head for image features that indicate the presence of an aneurysm using an artificial intelligence algorithm, and prioritizes patient imaging in a standalone application for workflow triage and review by a radiologist in parallel to standard of care image interpretation. Viz ANEURYSM is a combination of software modules that consists of an image analysis software algorithm and mobile application software module. The Viz ANEURYSM Image Analysis Algorithm is an artificial intelligence machine (Al/ML) software algorithm that analyzes CTA images of the head for an aneurysm. Images acquired during patient care are forwarded to Viz.ai's Backend server where they are analyzed by the Viz ANEURYSM artificial intelligence algorithm for an aneurysm. Viz ANEURYSM includes a mobile software module that enables the end user to view cases identified by the Viz ANEURYSM algorithm to contain a suspected aneurysm. The Viz ANEURYSM mobile software module is implemented into Viz.ai's generic non-diagnostic DICOM image mobile viewing application, Viz VIEW, which displays CTA scans that are sent to the Backend server. When the Viz ANEURYSM mobile software module is enabled, studies determined by the algorithm to contain a suspected aneurysm are highlighted in the standalone mobile application for study list prioritization or triage in parallel to ongoing standard of care. The user can also view compressed preview images and a non-diagnostic preview of the analyzed CTA scan of the patient through the mobile application. The preview images and additional patient imaging available through the standalone mobile application are meant for informational purposes only and not intended for diagnostic use. The results of Viz ANEURYSM, in conjunction with other clinical information and professional judgment, are to be used to assist with triage/prioritization of medical images. Radiologists who read the original medical images are responsible for the diagnostic decision.

Viz ICH is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of standard of care workflow. Viz ICH uses an artificial intelligence algorithm to analyze images for findings suggestive of a prespecified clinical condition and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the brain acquired in the acute setting, and sends notifications to a neurovascular or neurosurgical specialist that a suspected intracranial hemorrhage has been identified and recommends review of those images can be previewed through a mobile application. lmages that are previewed through the mobile application may be compressed and are for informational purposes only and not intended for diagnostic use beyond notification. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests. Viz ICH 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 diagnosis.

Viz ICH is a software-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to an appropriate specialist, such as a neurovascular specialist or neurosurgeon, independent of the standard of care workflow. The system automatically receives and analyzes non-contrast CT (NCCT) studies of patients for image features that indicate the presence of an intracranial hemorrhage (ICH) using an artificial intelligence algorithm, and upon detection of a suspected ICH, sends a notification so as to alert a specialist clinician of the case. Viz ICH is a combination of software modules that consists of an image analysis software algorithm and mobile application software module. The Viz ICH image analysis software algorithm is an artificial intelligence machine (AI/ML) software algorithm that analyzes non-contract CT images of the head for an intracranial hemorrhage. The Viz ICH Image Analysis Algorithm is hosted on Viz.ai's servers and analyzes applicable stroke-protocoled NCCT images of the head that are acquired on CT scanners and are forwarded to Viz.ai servers. Upon detection of a suspected intracranial hemorrhage, the Viz ICH Image Analysis Algorithm sends a notification of the suspected finding. Viz ICH includes a mobile software module that enables the end user to receive and toggle notifications for suspected intracranial hemorrhages identified by the Viz ICH Image Analysis Algorithm. The Viz ICH mobile notification software module is implemented into Viz.ai's non-diagnostic DICOM image viewer, Viz VIEW, which displays CT scans that are sent to Viz.ai's servers. When the Viz ICH mobile notification software module is enabled for a user, the user can receive and toggle the notifications for patients with a suspected intracranial hemorrhage, view a unique patient list of patients with a suspected intracranial hemorrhage, and view the non-diagnostic CT scan of the patient through the Viz VIEW mobile application. Image viewing through the mobile application interface is for nondiagnostic purposes only.

Viz ICH is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of care workflow. Viz ICH uses an artificial intelligence algorithm to analyze images for findings suggestive of a prespecified clinical condition and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the brain acquired in the acute setting, and sends notifications to a neurovascular or neurosurgical specialist that a suspected intracranial hemorrhage has been identified and recommends review of those images. Images can be previewed through a mobile application. lmages that are previewed through the mobile application may be compressed and are for informational purposes only and not intended for diagnostic use beyond notification. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests. Viz ICH 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 diagnosis. Viz ICH is contraindicated for analyzing non-contrast CT scans that are acquired on scanners from manufacturers other than General Electric (GE) or its subsidiaries (i.e. GE Healthcare). This contraindication applies to NCCT scans that conform to all applicable Patient Inclusion Criteria, are of adequate technical image quality, and would otherwise be expected to be analyzed by the device for a suspected ICH.

Viz ICH is a software-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to an appropriate specialist, such as a neurovascular specialist or neurosurgeon, independent of the standard of care workflow. The system automatically receives and analyzes non-contrast CT (NCCT) studies of patients for image features that indicate the presence of an intracranial hemorrhage (ICH) using an artificial intelligence algorithm, and upon detection of a suspected ICH, sends a notification so as to alert a specialist clinician of the case. Viz ICH consists of backend and mobile application component software. The Backend software includes a DICOM router and backend server. The DICOM router transmits NCCT images of the head acquired on a local healthcare network to the Backend Server. The Backend Server receives, stores, processes and serves received NCCT scans. The Backend Server also includes an artificial intelligence algorithm that analyzes the received NCCT images for image characteristics that indicate an intracranial haemorrhage (ICH) and, upon detection, sends a notification of the suspected finding to pre-determined specialists. The Viz ICH Mobile Application software receives notifications generated by the Backend of suspected image findings and allows the notification recipient to view the analyzed NCCT images through a non-diagnostic viewer, as well as patient information that was embedded in the image metadata. Image viewing through the mobile application is for informational purposes only and is not intended for diagnostic use.

Viz CTP is an image processing software package to be used by trained professionals, including but not limited to physicians and medical technicians. The software runs on a standard "off-the-shelf" computer or a virtual platform, such as VMware, and can be used to perform image processing, analysis, and communication of computed tomography (CT) perfusion scans of the brain. Data and images are acquired through DICOM-compliant imaging devices. Viz CTP provides both analysis and communication capabilities for dynamic imaging datasets that are acquired with CT Perfusion imaging protocols. Analysis includes calculation of parameters related to tissue flow (perfusion) and tissue blood volume. Results of image processing which include CT perfusion parameter maps generated from a raw CTP scan are exported in the standard DICOM format and may be viewed on existing radiological imaging viewers.

Viz CTP is a standalone software package that is comprised of several modules including DICOM receiving and sending modules, a study processor, image analysis algorithm, as well as software system components including a DICOM storage database and system health-monitoring. Viz CTP allows for bi-directional communication of data and may be implemented to allow a DICOM-compliant device to send files directly from the imaging modality, through a node on a local network, or from a PACS server. The device is designed to automatically receive, identify, extract, and analyze a CTP study of the head embedded in DICOM image data. The software outputs parametric maps related to tissue blood flow (perfusion) and tissue blood volume that are written back to the source DICOM. Following such analysis, the software automatically sends the results of analysis to a preconfigured destination point. The software allows for repeated use and continuous processing of data and can be deployed on a supportive infrastructure that meets the minimum system requirements. Viz CTP image analysis includes calculation of the following perfusion related parameters: - Cerebral Blood Flow (CBF) - Cerebral Blood Volume (CBV) - Mean Transit Time (MTT) - Residue function time-to-peak (TMax) - Arterial Input Function (AIF) The primary users of Viz CTP are medical imaging professionals who analyze dynamic CT perfusion studies. The results of image analysis produced by Viz CTP should be viewed through appropriate diagnostic viewers when used in clinical decision making.