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Brainomix Triage 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.

Brainomix Triage LVO 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 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 (LVO) has been identified and recommends review of those images. Images can be previewed through a mobile application or via email. Brainomix Triaqe LVO is intended to analyze terminal ICA and MCA-M1 vessels for LVOs.

lmages 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 noncompressed 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.

Brainomix Triage 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.

Brainomix 360 Triage LVO is a radiological computer aided triage and notification (CADt) software package compliant with the DICOM standard and running on an off-the-shelf physical or virtual server.

The Triage LVO module is a CTA processing module which operates within the integrated Brainomix 360 Platform to provide triage and notification of suspected LVO. Brainomix 360 Triage LVO is a stand-alone software device which uses machine learning algorithms that uses advanced non adaptive imaging algorithms, artificial intelligence, and large data analytics to automatically identify suspected LVO on CTA imaging in the acute setting. The output of the module is a priority notification to clinicians indicating the suspicion of LVO based on positive findings. Specifically, Brainomix 360 Triage LVO is optimized to evaluate occlusions of the intracranial internal carotid artery (ICA) and proximal middle cerebral artery (M1 segment). The Triage LVO module uses the basic services supplied by the Brainomix 360 Platform including DICOM processing, job management, imaging module execution and imaging output including the notification and compressed image.

Brainomix 360 Triage LVO notification capabilities enable clinicians to review and preview images via mobile app notification. Alternatively, intended users can also access the notification (a "Suspected LVO" flag) and straightened images via the Brainomix 360 web user interface. Images that are previewed via mobile app are compressed, are for preview informational purposes only, and not intended for diagnostic use beyond notification.

The device is intended for use as an additional tool for assisting study triage within existing patient pathways. It does not replace any part of the current standard of care. It is designed to assist in prioritization of studies for reading within a worklist, in addition to any other pre-existing formal or informal methods of study prioritization in place. Specifically, it does not remove cases from a reading queue and operates in parallel to the standard of care. This device is not intended to replace the usual methods of communication and transfer of information in the current standard of care.

Brainomix 360 Triage LVO notification capabilities enable clinicians to preview compressed and informational images through via mobile application with preview of unprocessed image attachments. Alternatively, the user may review unprocessed images via web user interface on a radiology workstation.

Here's a breakdown of the acceptance criteria and the study proving the device's performance, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: 308 CTA scans (studies)
• Data Provenance: Retrospective study. Data were obtained from 14 different hospitals and clinics in the U.S. The majority of patients were scanned at Mayo Clinic Rochester (N=129) and Boston Medical Centre (N=179), with 56 scans transferred from 11 hospitals in the Massachusetts area. The patient cohort was enriched to ensure an approximately equal balance of LVO positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age and gender) for generalizability.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Number of Experts: Two ground truthers, with a third ground truther used in the event of disagreement.
• Qualifications: All truthers were US board-certified neuroradiologists.

4. Adjudication Method for the Test Set

• Adjudication Method: 2+1 (Two ABR-certified neuroradiologists reviewed each case, and a third neuroradiologist provided consensus in the event of disagreement).

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was it done? No, the document only describes a standalone performance evaluation of the device.
• Effect size of human readers improving with AI vs. without AI assistance: Not applicable, as no MRMC study was performed or reported.

6. Standalone Performance Study

• Was it done? Yes, a standalone performance evaluation was done. The study assessed the device's image analysis in terms of sensitivity and specificity against a ground truth established by expert neuroradiologists.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus by US board-certified neuroradiologists.

8. Sample Size for the Training Set

• Sample Size: Over 1600 CT brain imaging studies.

9. How the Ground Truth for the Training Set was Established

• Ground Truth Establishment: The dataset used to train the algorithm was labeled by "trained radiologists" regarding the presence of LVO.

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