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510(k) Data Aggregation
(90 days)
BriefCase is a radiological computer aided triage and notification software indicated for use in the analysis of chest and abdominal CT images. The device is intended to assist hospital networks and appropriately trained medical specialists within the standard-of-care bone health setting in workflow triage by flagging and communication of suspected positive cases of Vertebral Compression Fractures (VCFx) findings.
BriefCase uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings on a standalone application in parallel to the ongoing standard of care image interpretation. The device does not alter the original medical image and is not intended to be used as a diagnosis 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 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/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 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", "for prioritization only" 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 users with worklist prioritization 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.
Here's a breakdown of the acceptance criteria and the study proving the device meets those criteria, based on the provided FDA 510(k) summary for Aidoc Medical, Ltd.'s BriefCase:
1. Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Performance Goals) | Reported Device Performance |
|---|---|
| Area Under the Curve (AUC) > 0.95 | AUC = 0.976 (95% CI: 0.961%, 0.991%) |
| Sensitivity > 80% | Sensitivity = 95.11% (95% CI: 90.92%, 97.74%) |
| Specificity > 80% | Specificity = 93.28% (95% CI: 87.63%, 96.88%) |
| Additional Metrics Reported: | |
| Negative Predictive Value (NPV) | 99.5% (95% CI: 99.1%- 99.8%) |
| Positive Predictive Value (PPV) | 55.2% (95% CI: 39.6%) |
| Positive Likelihood Ratio (PLR) | 14.1606 (95% CI: 7.528-26.637) |
| Negative Likelihood Ratio (NLR) | 0.0524 (95% CI: 0.028- 0.099) |
| Time-to-Notification (Primary Goal: Comparable to Predicate) | Mean = 117.2 seconds (95% CI: 98.64-135.85) |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: 318 cases
- Data Provenance: Retrospective, from 5 US-based clinical sites. The data was distinct in time or center from the cases used to train the algorithm.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
- Number of Experts: Three (3)
- Qualifications: Senior board-certified radiologists.
4. Adjudication Method for the Test Set
The document explicitly states that the ground truth was "determined by three senior board-certified radiologists." While it doesn't detail the exact adjudication method (e.g., majority vote, consensus after discussion), the phrasing suggests a consensual or majority-based approach among the three experts. It is not explicitly stated as 2+1 or 3+1, but the involvement of three implies an adjudication process to establish the single ground truth for each case.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done
No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted to specifically evaluate how much human readers improve with AI vs. without AI assistance. The study focused on the standalone performance of the AI algorithm and compared its time-to-notification to a predicate device.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done
Yes, a standalone performance study of the algorithm (BriefCase software) was performed. The primary endpoints (AUC, Sensitivity, Specificity) were evaluated for the algorithm's performance in identifying Vertebral Compression Fractures.
7. The Type of Ground Truth Used
The ground truth used was expert consensus based on the determination 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 document. It only mentions that the test set cases were "distinct in time or center from the cases used to train the algorithm."
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 indicates that the training data was separate from the test data.
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