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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.

I am sorry, but the provided text does not contain the requested information about acceptance criteria and the study that proves the device meets them. The document is a 510(k) summary for Viz LVO, a medical device, and it primarily focuses on establishing substantial equivalence to a predicate device (ContaCT) for a change in its indications for use.

Here's what the document does state regarding performance data:

• "Performance data was not included as part of the premarket notification. Supporting software verification and validation (V&V) testing were provided to demonstrate implementation of the device changes."

This indicates that this specific submission (K223042) did not involve new clinical performance studies to establish acceptance criteria or demonstrate device performance beyond software verification and validation to support the changes to the device. The substantial equivalence is based on the previously cleared predicate device (ContaCT DEN170073).

Therefore, I cannot provide:

1. A table of acceptance criteria and reported device performance.
2. Sample size and data provenance for a test set.
3. Number and qualifications of experts for ground truth.
4. Adjudication method.
5. MRMC comparative effectiveness study results.
6. Details of a standalone performance study.
7. Type of ground truth used.
8. Sample size for the training set.
9. How ground truth for the training set was established.

To find this information, you would typically need to consult the original 510(k) submission or de novo application for the predicate device, ContaCT (DEN170073), which likely contained the initial performance studies and acceptance criteria.

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