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BriefCase is a radiological computer aided triage and notification software indicated for use in the analysis of cervical spine CT images. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communication of suspected positive findings of linear lucencies in the cervical spine bone in patterns compatible with fractures.

BriefCase uses an artificial intelligence algorithm to analyse images and highlight cases with detected findings on a standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected findings. Notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device.

The results of BriefCase are intended to be used in conjunction with other patient information and based on their professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

BriefCase is a radiological computer-assisted triage and notification software device. The software system is based on an algorithm programmed component and is comprised of a standard off-the-shelf operating system, the Microsoft Windows server 2012 64bit, and additional applications, which include PostgreSQL. DICOM module and the BriefCase Image Processing Application. The device consists of the following three modules: (1) Aidoc Hospital Server (AHS); (2) Aidoc Cloud Server (ACS); and (3) Aidoc Worklist Application that is installed on the radiologist' desktop and provides the user interface in which notifications from the BriefCase software are received.

DICOM images are received, saved, filtered and de-identified before processing. Series are processed chronologically by running an algorithm on each series to detect suspected findings and then notifications on flagged series are sent to the Worklist desktop application, thereby prompting preemptive triage and prioritization.

The Worklist Application displays the pop-up text notifications of new studies with suspected findings when they come in. Notifications are in the form of a small pop-up containing patient name, accession number and the relevant pathology (e.g., CSF). A list of all incoming cases with suspected findings is also displayed. Hovering over a notification or a case in the worklist pops up a compressed, small black and white, unmarked image that is captioned "not for diagnostic use" and is displayed as a preview function. This compressed preview is meant for informational purposes only, does not contain any marking of the findings, and is not intended for primary diagnosis beyond notification.

Presenting the radiologist with notification facilitates earlier triage by prompting the user to assess the relevant original images in the PACS. Thus, the suspect case receives attention earlier than would have been the case in the standard of care practice alone.

Acceptance Criteria and Study Details for BriefCase (K190896)

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Acceptance Criteria for Performance (Implicitly Derived from "Performance Goal"):

• Sensitivity: > 80%
• Specificity: > 80%

Reported Device Performance:

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

• Sample Size for Test Set: 186 cases
• Data Provenance: Retrospective, multicenter, multinational. Data was collected from 3 clinical sites (2 US and 1 OUS - Outside US).

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

The provided text does not explicitly state the number of experts used to establish the ground truth for the test set or their specific qualifications (e.g., "radiologist with 10 years of experience"). It implicitly refers to "reviewers" establishing true positive CSF cases.

4. Adjudication Method for the Test Set

The provided text does not explicitly state the adjudication method used for the test set (e.g., 2+1, 3+1, none). It mentions "identified as positive both by the reviewers as well as the BriefCase device" when discussing True Positive cases, implying a comparison against an established ground truth, but not the process of establishing that ground truth.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

• MRMC Comparative Effectiveness Study: No, a traditional MRMC comparative effectiveness study comparing human readers with and without AI assistance was not explicitly described as the primary or secondary endpoint.
• Effect Size of Human Reader Improvement: This was not measured as part of the reported study. The study focused on the device's standalone performance and its impact on "time-to-notification" compared to "standard of care time-to-exam-open."

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, the primary endpoint focused on the standalone performance of the BriefCase software. Sensitivity and specificity were evaluated based on the algorithm's detection of cervical spine fractures. The device provides notifications and unannotated preview images, but the core performance metrics are from the algorithmic analysis.

7. The Type of Ground Truth Used

The ground truth for cervical spine fractures was established by reviewers. While "reviewer consensus" isn't explicitly stated, the implication is that the "reviewers" (likely radiologists) determined the presence or absence of CSF in the images, which served as the reference standard against which the device's performance was measured. It is not explicitly stated whether pathology or outcomes data was used.

8. The Sample Size for the Training Set

The document does not provide the sample size for the training set. It mentions the algorithm was "trained" on CSF images but does not specify the number of cases used for training.

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

The document does not provide details on how the ground truth for the training set was established. It only states that the algorithm was "trained on CSF... images."

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