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BriefCase is a radiological computer aided triage and notification software indicated for use in the analysis of nonenhanced head CT images in adults or transitional adolescents aged 18 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communication of suspected positive findings of Intracranial hemorrhage (ICH) pathologies.

BriefCase uses an artificial intelligence algorithm to analyze 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 notification 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 consists of a standard offthe-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/Orchestrator) for image acquisition; (2) Aidoc Cloud Server (ACS) for image processing; and (3) Aidoc Desktop Application for workflow integration.

DICOM images are received, saved, filtered and de-identified before processing. Filtration matches metadata fields with keywords. Series are processed chronologically by running the algorithms on each series to detect suspected cases. The software then flags suspect cases by sending notifications to the desktop application, thereby facilitating triage and prioritization by the user. As the BriefCase software platform harbors several triage algorithms, the user may opt to filter out notifications by pathology, e.q., a chest radiologist may choose to filter out alerts on ICH cases, and a neuro-radiologist would opt to divert PE alerts. Where several medical centers are linked to a shared PACS, a user may read cases for a certain center but not for another, and thus may opt to filter out alerts by center. Activating the filter does not impact the order in which notifications are presented in the Aidoc desktop application.

The desktop application feed displays all incoming suspect cases, each notified case in a line. Hovering over a line in the feed pops up a compressed, low-quality, grayscale, unannotated 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.

Here's a breakdown of the acceptance criteria and study details for the Aidoc BriefCase device, based on the provided text:

Acceptance Criteria and Device Performance

1. A table of acceptance criteria and the reported device performance

Study Details

2. Sample size used for the test set and the data provenance

• Test Set Sample Size: 220 cases
• Data Provenance: Retrospective, multicenter study from 5 US-based clinical sites.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

The information provided does not specify the number of experts used to establish ground truth or their qualifications. It only states that cases were "identified as positive both by the reviewers as well as the BriefCase device" (implying human review for ground truth).

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

The document does not explicitly state the adjudication method used for the test set.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study involving human readers with and without AI assistance was not conducted or reported. The study evaluated the standalone performance of the AI and its time-to-notification compared to a predicate AI device.
• Effect Size: Not applicable, as no MRMC study with human readers was performed. The device is a "workflow triage" tool and not intended for primary diagnosis by itself.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Yes, a standalone performance evaluation of the algorithm was conducted and reported, demonstrating its sensitivity and specificity in identifying ICH. The "Primary Endpoint" results (Sensitivity and Specificity) are indicative of the algorithm's standalone performance.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

The ground truth for the test set was established by "reviewers." While not explicitly stated as "expert consensus," this implies a form of human expert review. It is not mentioned that pathology or outcomes data were used as ground truth for ICH detection in this context.

8. The sample size for the training set

The document states that the subject device was trained on a "larger data set" compared to the predicate device, but the specific sample size for the training set is not provided.

9. How the ground truth for the training set was established

The document does not specify how the ground truth for the training set was established. It only mentions that the device uses an "artificial intelligence, deep-learning algorithm trained on medical images."

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