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BriefCase-Triage is a radiological computer aided triage and notification software indicated for use in the analysis of abdominal 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 Intra-abdominal free gas (IFG) pathologies.

BriefCase-Triage uses an artificial intelligence algorithm to analyze images and highlight cases with the detected findings 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-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.

The algorithm was trained during software development on images of the pathology. As is customary in the field of machine learning, deep learning algorithm development consisted of training on labeled ("tagged") images. In that process, each image in the training dataset was tagged based on the presence of the critical finding.

Here's a detailed breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) clearance letter for BriefCase-Triage:

Acceptance Criteria and Reported Device Performance

Note: The document explicitly states that the primary endpoints were "sensitivity and specificity with an 80% performance goal." The reported performance for both sensitivity and specificity (94.2% and 94.6% respectively) significantly exceeds this 80% goal. The time-to-notification for the subject device is significantly faster than the predicate, demonstrating improved "time savings to the standard of care review."

Study Information

1. Sample Size Used for the Test Set and Data Provenance:
* Sample Size: 394 cases
* Data Provenance:
* Country of Origin: US (6 clinical sites)
* Retrospective/Prospective: Retrospective
* Additional Detail: Cases were distinct in time or center from the training data.

2. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:
* Number of Experts: 3
* Qualifications: Senior board-certified radiologists

3. Adjudication Method for the Test Set:
* The document states "as determined by three senior board-certified radiologists." While it doesn't explicitly state "2+1" or "3+1," this implies a consensus-based approach among the three experts. Without further detail, it's reasonable to infer a consensus was reached, or a specific rule for disagreement (e.g., majority) was applied.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
* No, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted to assess how much human readers improve with AI vs. without AI assistance. The study focuses purely on the standalone performance of the AI algorithm.

5. Standalone Performance Study (Algorithm Only):
* Yes, a standalone study was performed. The "Pivotal Study Summary" describes evaluating "the software's performance to the ground truth," indicating a standalone performance assessment of the algorithm without human-in-the-loop performance measurement.

6. Type of Ground Truth Used:
* Expert consensus (as determined by three senior board-certified radiologists).

7. Sample Size for the Training Set:
* The document states, "The algorithm was trained during software development on images of the pathology." However, it does not provide a specific sample size for the training set.

8. How the Ground Truth for the Training Set Was Established:
* "each image in the training dataset was tagged based on the presence of the critical finding." This indicates that human experts (or a similar method to the test set ground truth) labeled the images in the training set for the presence of the pathology. However, the specific number and qualifications of these experts are not explicitly stated for the training set.

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