Viz LVO ContaCT

Viz LVO is designed to expedite the identification and notification of suspected large vessel occlusions by utilizing an artificial intelligence algorithm to analyze acute CT angiogram images of the brain, specifically targeting terminal ICA and MCA-M1 vessels. The system operates as a parallel workflow tool, alerting neurovascular specialists via a mobile application to facilitate timely review of potential stroke cases, emphasizing that it is an adjunctive notification aid and not a standalone diagnostic instrument.

Viz LVO is a software tool designed for hospital networks and trained clinicians to enhance the triage process for potential acute stroke patients. It utilizes an artificial intelligence algorithm to analyze CT angiogram (CTA) images of the brain, specifically targeting the terminal internal carotid artery (ICA) and M1 segment of the middle cerebral artery (MCA-M1) for suspected large vessel occlusions (LVOs). The system operates in parallel with standard care workflows, sending notifications directly to neurovascular specialists upon detection of a suspected LVO, thereby prompting a prioritized review of the patient's images, which can be initially previewed in a compressed format via a dedicated mobile application. The core of Viz LVO is its locked AI/machine learning algorithm (LVO Detection Algorithm), which analyzes stroke-protocol CTA images received on a backend server. Notifications and image previews are managed through the Viz LVO Mobile Notification Software, integrated into the Viz VIEW mobile application. This system is strictly for notification and informational purposes; it does not provide a diagnosis, and clinicians must review full, non-compressed images on a diagnostic viewer and conduct a complete patient evaluation before making clinical decisions. This iteration of Viz LVO incorporates updates to its indications for use to further specify analyzed regions and embeds enhanced communication of the device's limitations directly within the mobile application interface, building upon the technology of its predicate device.

AI Regulatory Concerns

Powered by

Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. How does the Viz LVO algorithm's performance in identifying suspected LVOs in terminal ICA and MCA-M1 vessels compare to traditional radiologist review in terms of time-to-notification and accuracy?; What are the specific limitations of the AI/ML algorithm that are now communicated through the mobile application interface, and how might these impact clinical decision-making?; Can the Viz LVO system be integrated with various hospital Picture Archiving and Communication Systems (PACS) and Electronic Health Records (EHRs)?; What training is recommended or required for neurovascular specialists to effectively use the Viz LVO mobile application and interpret its notifications within the context of standard patient care?; Are there plans to expand the AI algorithm's capabilities to detect other critical findings on CTA brain images or analyze other vascular territories beyond the terminal ICA and MCA-M1 vessels?