(131 days)
BriefCase is a radiological computer aided triage and notification software indicated for use in the analysis of contrast-enhanced chest CTs (but not dedicated CTPA protocol). The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communication of suspect cases of incidental Pulmonary Embolism (iPE) pathologies. For the iPE pathology, the software is only intended to be used on single-energy exams. The device is intended to work with GE and Siemens scanners only.
BriefCase uses an artificial intelligence algorithm to analyze images and flaq suspect cases on a standalone desktop application 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 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 is comprised 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, installed on the radiologist' desktop and provides the user interface in which notifications from the BriefCase software are received.
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 Worklist desktop application, thereby prompting preemptive triage and prioritization by the attending radiologist. 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 the pop-up text notifications of new suspected studies when they come in. Notifications are in the form of a small pop-up containing patient name, accession number and the relevant pathology (e.g., iPE). A list of all incoming suspect cases is also displayed. Hovering over a notification or a case in the worklist pops up a compressed, lowquality, 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.
Presenting the radiologist with notification 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 text:
Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Sensitivity $\ge$ 80% | 90.5% (95% CI: 81.4%, 96.2%) |
| Specificity $\ge$ 80% | 88.7% (95% CI: 83.3%, 92.8%) |
Study Details
2. Sample size used for the test set and the data provenance:
- Test Set Sample Size: 268 cases (74 positive, 194 negative)
- Data Provenance: Retrospective, from 2 clinical study sites in the US.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
The document does not explicitly state the number of experts used to establish the ground truth or their specific qualifications (e.g., "radiologist with 10 years of experience"). It mentions "reviewers" identified cases as positive, implying expert review.
4. Adjudication method for the test set:
The document does not specify an adjudication method (e.g., 2+1, 3+1) for establishing the ground truth of the test set. It only mentions that positive cases were "identified as positive both by the reviewers as well as the BriefCase device" when discussing the time-to-notification metric, implying expert consensus or a similar process for ground truth.
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:
- Was a MRMC study done? No, a traditional MRMC comparative effectiveness study was not explicitly described. The study focused on the standalone performance of the AI and a comparison of time-to-notification (AI) versus time-to-exam-open (standard of care).
- Effect size of human reader improvement: While not an MRMC study measuring reader improvement, the secondary endpoint evaluated the potential clinical benefit of worklist prioritization. The study found a statistically significant mean difference of 220.9 minutes (95% CI: 122.0-319.9; Median: 63.2, IQR: 219.8) between the standard of care time-to-exam-open (223.3 minutes) and the BriefCase time-to-notification (4.7 minutes) for true positive iPE cases. This suggests that if radiologists act on notifications, they could potentially review critical cases hours earlier than without the AI.
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, reporting sensitivity and specificity metrics against the established ground truth.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
The ground truth for the test set was established by "reviewers" who identified cases with incidental Pulmonary Embolism (iPE). This strongly implies expert consensus or expert review as the method for ground truth determination.
8. The sample size for the training set:
The document does not explicitly state the sample size for the training set. It mentions the "algorithm programmed component" and "training of the algorithm on iPE," but not the specific number of cases or how they were used for training.
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 notes that the subject device's algorithm differed from the predicate device's due to its "training...on iPE images." This implies that training data also had established ground truth for iPE, likely through similar expert review processes.
§ 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.