(81 days)
BriefCase is a radiological computer aided triage and notification software indicated for use in the analysis of Chest X-Ray cases 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 suspect positive cases with Pneumothorax (Ptx) findings.
BriefCase uses an artificial intelligence algorithm to analyze images and flag suspect cases on a standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is notified 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 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) for image acquisition; (2) Aidoc Cloud Server (ACS) for image processing; and (3) Aidoc Worklist 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 passive notifications to the Worklist 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.g., a chest radiologist may choose to filter out notifications on LVO cases, and a neuro-radiologist would opt to divert PE notifications. 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 notification by center. Activating the filter does not impact the order in which notifications are presented in the Aidoc worklist application.
The Worklist application displays all incoming suspect cases, each notified case in a line. Hovering over a line in the worklist pops up a compressed, low-quality, grayscale, unannotated image that is captioned "hot 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 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 them, based on the provided FDA 510(k) summary for Aidoc Medical, Ltd.'s BriefCase:
1. Table of Acceptance Criteria and Reported Device Performance
| Metric | Acceptance Criteria (Pre-specified Performance Goals) | Reported Device Performance (BriefCase) |
|---|---|---|
| AUC | Exceeded 0.95 | 0.969 (95% CI: 0.954, 0.985) |
| Sensitivity | Exceeded 80% | 94.2% (95% CI: 89.9%, 97.8%) |
| Specificity | Exceeded 80% | 90.8% (95% CI: 88.1%, 93.1%) |
| Time-to-Notification (TP cases) | Substantially similar to predicate (13.8 seconds) | 13.1 seconds (95% CI: 10.6 - 15.7; Median 11.8) |
2. Sample Size and Data Provenance
- Test Set Sample Size: 619 Chest X-ray cases.
- Data Provenance: Retrospective, blinded, multicenter, multinational study.
- Country of Origin: 5 clinical study sites (4 US-based, 1 OUS). 89% of cases were collected from US sites.
- Retrospective/Prospective: Retrospective.
3. Number of Experts and Qualifications for Ground Truth
- Number of Experts: Two radiologists initially, with an additional third radiologist to resolve inconsistencies.
- Qualifications of Experts: Not explicitly stated beyond "radiologists." It implies they are trained medical professionals in radiology.
4. Adjudication Method for the Test Set
- Adjudication Method: 2+1 (Two radiologists performed initial ground truthing, and a third radiologist resolved inconsistencies).
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- MRMC Study: No, an MRMC comparative effectiveness study was not performed to assess how human readers improve with AI vs without AI assistance.
- Effect Size: Not applicable as no MRMC study was conducted. The study, instead, compares the device's standalone performance to pre-specified metrics and its time-to-notification to that of a predicate device.
6. Standalone (Algorithm Only) Performance
- Standalone Performance: Yes, standalone performance (algorithm only without human-in-the-loop) was evaluated against the primary endpoints of AUC, Sensitivity, and Specificity. The results reported (AUC, Sensitivity, Specificity) are for the BriefCase algorithm's performance in identifying Pneumothorax.
7. Type of Ground Truth Used
- Type of Ground Truth: Expert consensus of radiologists, adjudicated by a third radiologist.
8. Sample Size for the Training Set
- Training Set Sample Size: Not explicitly stated. The document mentions "No patient data were reused between the training and the clinical validation datasets," indicating a separate training set, but its size is not provided in this summary.
9. How Ground Truth for the Training Set was Established
- Training Set Ground Truth: Not explicitly detailed in this summary. However, given the methodology for the test set, it is highly probable that similar expert review and consensus methods would have been employed to establish ground truth for the training data to ensure consistency and quality. The document states the algorithm was trained on a "database of images," implying these images were labeled, presumably by experts.
§ 892.2080 Radiological computer aided triage and notification software.
(a)
Identification. Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
e.g., improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use;
(ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.