(134 days)
BriefCase is a radiological computer aided triage and notification software indicated for use in the analysis of contrast-enhanced chest CTs (not dedicated CTPA protocol) in adults or transitional adolescents age 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 cases of incidental Pulmonary Embolism (iPE) pathologies. The device is intended to be used on singleenergy exams only.
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 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); (2) Aidoc Cloud Server (ACS); and (3) Aidoc Worklist Application that is installed on the user's desktop and provides the 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., iPE). 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 diaqnosis 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.
The provided text describes the acceptance criteria and a study proving the device meets these criteria for Aidoc Medical, Ltd.'s BriefCase device (K213886), which is software for the triage and notification of incidental Pulmonary Embolism (iPE) in contrast-enhanced chest CTs.
Here's a breakdown of the requested information:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Performance Goal) | Reported Device Performance |
|---|---|
| Sensitivity > 80% | 89.7% (95% CI: 80.8%, 95.5%) |
| Specificity > 80% | 90.1% (95% CI: 81.5%, 95.6%) |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: 159 cases (78 positive cases, 81 negative cases).
- Data Provenance:
- Country of Origin: 3 clinical study sites (2 in the US, 1 OUS - Outside US). Specific countries for OUS are not mentioned, but "OUS" generally implies regions outside the United States.
- Retrospective or Prospective: Retrospective.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
The document does not explicitly state the number of experts used to establish the ground truth for the test set, nor does it specify their exact qualifications (e.g., "radiologist with 10 years of experience"). However, it implies that the ground truth for iPE cases was established based on expert consensus for the initial study (K201020, as stated under "Time saving data were presented in the original iPE 510(k) Summary (K201020) and remain applicable to this device."), and for this submission, the new performance data for Philips and Toshiba CT scanners were evaluated against this established ground truth.
4. Adjudication Method for the Test Set
The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1) for establishing the ground truth of the test set cases.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size
- Was an MRMC study done? The document does not describe a Multi-Reader Multi-Case (MRMC) comparative effectiveness study to re-evaluate human readers' improvement with AI assistance.
- Effect Size of Human Reader Improvement: However, it does reference time-saving data from a previous submission (K201020), stating: "the contribution of the BriefCase software is in reducing the time span until an exam is opened to several minutes for cases with suspect findings (4.7 min BriefCase time to notification compared to 223.3 min time-to-exam-open in the standard of care)." This indicates a significant reduction in time to notification and potential triage, suggesting an improvement in workflow and presumably reader efficiency or timeliness of review for flagged cases. This isn't a direct MRMC study comparing diagnostic accuracy improvement, but rather a workflow efficiency improvement measure.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done
Yes, the primary endpoints (Sensitivity and Specificity) represent the standalone performance of the algorithm in identifying iPE cases. The study evaluated the software's performance "in identifying Contrast-enhanced chest CTs... containing Incidental Pulmonary Embolism".
7. The Type of Ground Truth Used
The ground truth for the test set was established as "images with iPE versus without iPE." While the specific methodology for establishing this ground truth is not detailed in this document, it is typical for such studies in medical imaging to use expert consensus (e.g., multiple experienced radiologists reviewing cases and reaching agreement, possibly with reference to pathology reports or clinical outcomes for confirmation in ambiguous cases). The reference to the "original iPE 510(k) Summary (K201020)" suggests an established, validated ground truth from that prior work.
8. The Sample Size for the Training Set
The document does not provide information on the sample size used for the training set. It only describes the performance study which used a test set.
9. How the Ground Truth for the Training Set Was Established
The document does not provide information on how the ground truth for the training set was established. It mentions the algorithm uses "artificial intelligence algorithm with database of images" but does not detail the annotation process for this database.
§ 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.