(205 days)
BriefCase is a radiological computer aided triage and notification software indicated for use in the analysis of abdominal CT images. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communication of suspected positive findings of Intra-abdominal free gas (IFG) 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 (LVQ) 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., IFG). 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.
Here's an analysis of the provided text, outlining the acceptance criteria and the study proving the device meets them:
1. Table of Acceptance Criteria and Reported Device Performance
| Performance Metric | Acceptance Criteria (Performance Goal as stated in text) | Reported Device Performance (95% Confidence Interval) |
|---|---|---|
| Sensitivity | > 80% | 91.0% (81.5%, 96.7%) |
| Specificity | > 80% | 88.9% (81.7%, 94.0%) |
| Time-to-Notification (Median for True Positive IFG Cases) | - (Desired to be significantly faster than standard of care) | 4.2 minutes (3.7-5.0 minutes) |
| Time Saved (Mean difference compared to standard of care Time-to-Exam-Open for True Positive IFG Cases) | - (Desired to be clinically significant) | 89.7 minutes (45.5-133.9 minutes) |
Note: The document explicitly states "Sensitivity and specificity exceeded the 80% performance goal." This implies the 80% threshold was the acceptance criterion for these metrics. For time-to-notification and time saved, while no explicit numerical acceptance criterion is given, the text highlights the statistical significance and clinical benefit of the time savings, demonstrating the device's fulfillment of its intended purpose to assist in workflow triage and prioritization.
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: 184 cases (67 positive for IFG, 117 negative for IFG)
- Data Provenance: Retrospective, blinded, multicenter, multinational study. Cases were collected from 3 clinical study sites (2 in the US and 1 Outside the US, OUS).
- Supplemental Data: An additional supplemental dataset with cases from various scanner manufacturers was also provided to demonstrate consistent performance across scanners.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document does not specify the exact number of experts used to establish the ground truth for the test set or their specific qualifications (e.g., "radiologist with 10 years of experience"). It only mentions that the study involved a "retrospective, blinded, multicenter, multinational study with the BriefCase software with the primary endpoint to evaluate the software's performance in identifying abdominal CTs containing Intra-abdominal Free Gas...". The implication is that the ground truth was derived from expert review, but details are not provided.
4. Adjudication Method for the Test Set
The document does not explicitly state the adjudication method used (e.g., 2+1, 3+1). It refers to the ground truth being established by "reviewers" in the context of True Positive cases, but the process of reaching a consensus or final ground truth decision is not detailed.
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
No, a multi-reader multi-case (MRMC) comparative effectiveness study evaluating human readers' improvement with AI vs. without AI assistance was not reported. The study focused on the standalone performance of the AI algorithm and the time-to-notification compared to the standard of care "time-to-exam-open." The time savings analysis suggests a potential for improved workflow efficiency but does not directly measure improved reader performance or diagnostic accuracy when assisted by AI.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done
Yes, a standalone performance study of the algorithm only was done. The reported sensitivity (91.0%) and specificity (88.9%) are metrics of the algorithm's performance in identifying IFG cases independently. The "time-to-notification" metric also pertains to the algorithm's speed in generating a notification.
7. The Type of Ground Truth Used
The ground truth used was expert consensus (implied from "identified as positive both by the reviewers as well as the BriefCase device"). This is based on the interpretation of abdominal CT images by medical professionals, though the specific process of consensus building is not detailed.
8. The Sample Size for the Training Set
The document does not specify the sample size used for the training set. It mentions the algorithm is "trained on IFG and ICH images" but provides no further details on the training data.
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 broadly states the algorithm is "trained on IFG and ICH 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.