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BriefCase-Triage is a radiological computer aided triage and notification software indicated for use in the analysis of contrast-enhanced images that include the lungs in adults or transitional adolescents age 18 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communication of suspect cases of incidental Pulmonary Embolism (iPE) pathologies.

BriefCase-Triage uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for suspect cases. 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-Triage 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-Triage is a radiological computer-assisted triage and notification software device. The software is based on an algorithm programmed component and is intended to run on a linux-based server in a cloud environment.

The Briefcase-Triage receives filtered DICOM Images, and processes them chronologically by running the algorithms on each series to detect suspected cases. Following the AI processing, the output of the algorithm analysis is transferred to an image review software (desktop application). When a suspected case is detected, the user receives a pop-up notification and is presented with a compressed, low-quality, grayscale image that is captioned "not for diagnostic use, for prioritization only" which is displayed as a preview function. This 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 users with worklist prioritization facilitates efficient 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.

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

1. Table of Acceptance Criteria and Reported Device Performance

Note on Additional Operating Points (AOPs): The document also describes four additional operating points (AOPs 1-4) designed to maximize specificity while maintaining a lower bound 95% CI of 80% for sensitivity. These AOPs achieved similar performance metrics, further demonstrating the device's flexibility.

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

• Sample Size: 498 cases
• Data Provenance: Retrospective, blinded, multicenter study. Cases were collected from 6 US-based clinical sites. The test data was distinct in time or center from the cases used for algorithm training and was sequestered from algorithm development activities.

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

• Number of Experts: Three (3)
• Qualifications of Experts: Senior board-certified radiologists.

4. Adjudication Method for the Test Set

The specific adjudication method (e.g., 2+1, 3+1) is not explicitly stated in the provided text. It only mentions that the ground truth was "determined by three senior board-certified radiologists."

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing AI assistance versus without AI assistance for human readers was not described for this device. The study primarily evaluated the standalone performance of the AI algorithm and its time-to-notification compared to the predicate device, not the improvement in human reader performance with AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-loop Performance) was done

Yes, a standalone performance study was done. The "Pivotal Study Summary" sections describe the device's sensitivity, specificity, and other metrics based on its algorithm's performance in identifying iPE cases compared to the ground truth established by radiologists. The device is intended to provide notifications and not for diagnostic use, suggesting its performance is evaluated in its autonomous triage function.

7. The Type of Ground Truth Used

The ground truth used was expert consensus, specifically "determined by three senior board-certified radiologists."

8. The Sample Size for the Training Set

The sample size for the training set is not explicitly stated in the provided text. It only mentions that the algorithm was "trained during software development on images of the pathology."

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

The ground truth for the training set was established through manual labeling ("tagging") by experts. The text states: "deep learning algorithm development consisted of training on manually labeled ("tagged") images. In that process, critical findings were tagged in all CTs in the training data set."

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