Search Results

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 Acceptance Criteria and Performance Study

This document describes the acceptance criteria for the Viz ICH device and the study conducted to demonstrate its performance.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance

• Test Set Sample Size: 387 Non-contrast Computed Tomography (NCCT) scans (studies).
• Data Provenance: Two clinical sites in the U.S. (Retrospective, as the study was to evaluate the performance of an already developed algorithm on existing data).

3. Number, Qualifications, and Adjudication of Experts for Ground Truth

• Number of Experts: Not explicitly stated, but referred to as "trained neuro-radiologists."
• Qualifications of Experts: "Trained neuro-radiologists." Specific years of experience are not mentioned.
• Adjudication Method: Not explicitly stated, but "ground truth as established by trained neuro-radiologists" implies a consensus or majority vote among multiple experts.

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

• Was an MRMC study done? No. The study focuses on the standalone performance of the AI algorithm.
• Effect size of human readers with AI vs. without AI assistance: Not applicable as no MRMC study was conducted. However, the study does mention the average time to alert:
  • Viz ICH: 0.49 ± 0.08 minutes
  • Standard of Care: 18.3 ± 14.2 minutes
    This reduction in alert time implies a significant improvement in the speed of notification for human specialists.

5. Standalone Performance

• Was a standalone (algorithm only) performance study done? Yes. The provided performance data (sensitivity, specificity, AUC) directly reflects the algorithm's performance compared to ground truth.

6. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus, specifically "ground truth as established by trained neuro-radiologists" for the presence or absence of intracranial hemorrhage.

7. Sample Size for Training Set

• Sample Size for Training Set: Not explicitly stated in the provided document.

8. How Ground Truth for Training Set Was Established

• How Ground Truth for Training Set Was Established: Not explicitly stated in the provided document. It is generally assumed that the training data ground truth would also be established by clinical experts, similar to the test set.

Ask a specific question about this device