(112 days)
BriefCase is a radiological computer aided triage and notification software indicated for use in the analysis of head CTA images. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communication of suspected positive findings of Large Vessel Occlusion (LVO) pathologies.
BriefCase uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings on a standalone desktop application 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 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. Series are processed chronologically by running an algorithm on each series to detect suspected findings and then notifications on flagged series are sent to the Worklist desktop application, thereby prompting preemptive triage and prioritization. The user may opt to filter out notifications by pathology, e.g. a chest radiologist may choose to filter out notifications on Large Vessel Occlusion (LVO) cases, and a neuro-radiologist would opt to divert Pulmonary Embolism (PE) notifications. In addition, 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 studies with suspected findings 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., LVO). A list of all incoming cases with suspected findings is also displayed. Hovering over a notification or a case in the worklist pops up a compressed, small black and white, unmarked 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.
Based on the provided text, here's a detailed description of the acceptance criteria and the study that proves the device meets them:
1. Table of Acceptance Criteria and Reported Device Performance
| Metric | Acceptance Criteria (Performance Goal) | Reported Device Performance |
|---|---|---|
| Sensitivity | > 80% | 88.8% (95% CI: 81.9%, 93.8%) |
| Specificity | > 80% | 87.2% (95% CI: 82.5%, 91.1%) |
| Time-to-notification | Comparable to predicate device | 3.8 min (95% CI: 3.6-4.0) |
| Positive Likelihood Ratio (PLR) | Not explicitly stated as an AC, but reported | 6.9 (95% CI: 5.0-9.6) |
| Negative Likelihood Ratio (NLR) | Not explicitly stated as an AC, but reported | 0.13 (95% CI: 0.1-0.2) |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: 383 cases
- Data Provenance: The data was collected from 3 US-based clinical sites and was retrospective.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts
The document does not explicitly state the number of experts or their specific qualifications (e.g., years of experience) used to establish the ground truth for the test set. It only mentions "reviewers" identified "True Positive cases."
4. Adjudication Method for the Test Set
The document does not explicitly state the adjudication method used for the test set.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size of Human Improvement
A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted. The study evaluated the standalone performance of the AI algorithm and compared its notification time to a predicate device. It did not assess human reader performance with and without AI assistance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
Yes, a standalone study was done. The pivotal study evaluated the BriefCase software's performance in identifying LVOs, reporting its sensitivity and specificity, which implies an algorithm-only evaluation. The statement "The BriefCase time-to-notification includes the time to get the DICOM exam, de-identify it, upload it to the cloud, analyze and send a notification on a positive suspect case back to the worklist application" further supports an algorithm-only performance measurement before any human interaction.
7. The Type of Ground Truth Used
The ground truth was established by "reviewers." The document implies it's an expert consensus or expert interpretation of the images, as "True Positive cases (i.e., identified as positive both by the reviewers as well as the BriefCase device)" are mentioned. It does not mention pathology or outcomes data as ground truth.
8. The Sample Size for the Training Set
The document does not specify the sample size for the training set. It only states that the device uses an "artificial intelligence algorithm trained on medical images" and an "artificial intelligence algorithm with database of images."
9. How the Ground Truth for the Training Set Was Established
The document does not explicitly state how the ground truth for the training set was established. It only mentions that the algorithm was "trained on medical images."
§ 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.