(117 days)
BriefCase is a radiological computer aided triage and notification software indicated for use in the analysis of head CT Angio (CTA) images. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communication of suspected positive cases of Brain Aneurysm (BA) findings above 5 mm size.
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 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 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); (2) Aidoc Cloud Server (ACS); and (3) Aidoc Worklist Application that is installed on the user's desktop and provides the user interface in which notifications from the BriefCase software are received and the worklist is presented.
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 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 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., BA). 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 users with notification facilitates earlier triage by prompting them 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 acceptance criteria and the study that proves the device meets them, based on the provided text:
Acceptance Criteria and Device Performance
| Acceptance Criteria (Performance Goal) | Reported Device Performance |
|---|---|
| Sensitivity > 80% | 88.5% (95% CI: 80.4%, 94.1%) |
| Specificity > 80% | 89.5% (95% CI: 84.0%, 93.7%) |
Conclusion: The device (BriefCase for Brain Aneurysm triage) met both primary acceptance criteria, exceeding the 80% performance goal for both sensitivity and specificity.
Study Details:
2. Sample size used for the test set and the data provenance:
- Test Set Sample Size: 268 cases (96 positive cases, 172 negative cases).
- Data Provenance: Retrospective, multinational study from five study sites, including 2 US-based study sites. The text does not specify the other countries of origin.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Number of Experts: 3 experts.
- Qualifications: Two US Board-certified radiologists and a third one (whose specific qualifications beyond being available to resolve inconsistencies are not stated, but presumably also a radiologist).
4. Adjudication method for the test set:
- Adjudication Method: "Ground truthing was performed by two US Board-certified radiologists and a third one to resolve inconsistencies." This implies a 2+1 adjudication method, where two experts independently establish the ground truth, and a third expert resolves any disagreements between the first two.
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:
- MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study evaluating human readers' improvement with AI assistance was not explicitly detailed. The study focused on the standalone performance of the AI (sensitivity, specificity) and its impact on workflow efficiency (time to notification vs. time to exam open). The secondary endpoint compared time metrics, not reader diagnostic performance.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:
- Standalone Performance: Yes, the primary endpoint evaluated the standalone performance of the BriefCase software in identifying head CTs containing Brain Aneurysm, reporting its sensitivity and specificity. The described study evaluates the algorithm's performance without a human in the diagnostic loop.
7. The type of ground truth used:
- Ground Truth Type: Expert consensus. The ground truth was established by two US Board-certified radiologists, with a third to resolve inconsistencies, based on reports on images with and without Brain Aneurysm findings.
8. The sample size for the training set:
- Training Set Sample Size: The text states, "No patient data were reused between the training and the pivotal datasets," and "The subject BriefCase for BA triage and the predicate device BriefCase for ICH triage (K203508 initially cleared under K180647) are identical in all aspects and defer only with respect to the training of the algorithm on BA and ICH findings, respectively." However, the specific sample size of the training set used for the BA algorithm is not provided in the given text. It only mentions that the algorithm was trained on a "database of images."
9. How the ground truth for the training set was established:
- Training Set Ground Truth Establishment: Similar to the training set sample size, the text broadly states the device uses "Artificial intelligence Deep-learning algorithm with database of images" and that the algorithms were "trained on BA and ICH findings." However, the specific methodology for establishing the ground truth for the training set (e.g., number of experts, qualifications, adjudication method) is not explicitly detailed in the provided document.
§ 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.