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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.

The Viz SDH device's acceptance criteria and performance are detailed in the provided document.

1. Table of Acceptance Criteria and Reported Device Performance

The study also reported an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.96.
The time to notification for SDH was 1.15 ± 0.57 minutes.

2. Sample Size and Data Provenance

• Sample Size for Test Set: 542 non-contrast CT (NCCT) scans (studies).
• Data Provenance: The scans were obtained from three clinical sites in the U.S. The studies included approximately twice as many negative cases as positive cases (66.1% without SDH and 33.9% with SDH).
• Retrospective/Prospective: Not explicitly stated, but the description of "obtained from three clinical sites" and "included in the analysis" typically suggests retrospective data collection for regulatory studies of this nature.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not explicitly stated.
• Qualifications of Experts: Trained neuro-radiologists.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method used for establishing ground truth from the neuro-radiologists. It only mentions that ground truth was "established by trained neuro-radiologists."

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

No MRMC comparative effectiveness study was mentioned where human readers' improvement with AI vs. without AI assistance was evaluated. The performance data presented is for the standalone algorithm; however, there is a comparison of the time to notification with a predicate device (Viz ICH) which was previously shown to be clinically meaningful in reducing notification time compared to standard of care.

6. Standalone Algorithm Performance Study

Yes, a standalone performance study was conducted. The sensitivity, specificity, and AUC values reported are for the Viz SDH algorithm without human-in-the-loop performance.

7. Type of Ground Truth Used

The ground truth was established by expert consensus, specifically by trained neuro-radiologists, who compared the Viz SDH's output to the actual NCCT images.

8. Sample Size for the Training Set

The sample size for the training set is not provided in the document. The performance data section focuses entirely on the test set.

9. How Ground Truth for the Training Set Was Established

The document does not provide information on how the ground truth for the training set was established. It only mentions the approach for the test set.

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