(29 days)
BriefCase-Triage is a radiological computer aided triage and notification software indicated for use in the analysis of CT scans that include the cervical spine, in adults or transitional adolescents aged 18 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communication of linear lucencies in the cervical spine bone in patterns compatible with fractures.
BriefCase-Triage uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings 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-Triage 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-Triage is a radiological computer-assisted triage and notification software device.
The software is based on an algorithm programmed component and is intended to run on a linuxbased server in a cloud environment.
The BriefCase-Triage receives filtered DICOM Images, and processes them chronologically by running the algorithms on each series to detect suspected cases. Following the Al processing, the output of the algorithm analysis is transferred to an image review software (desktop application). When a suspected case is detected, the user receives a pop-up notification and is presented with a compressed, low-quality, grayscale image that is captioned "not for diagnostic use, for prioritization only" which is displayed as a preview function. This 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 worklist prioritization facilitates efficient 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.
The algorithm was trained during software development on images of the pathology. As is customary in the field of machine learning algorithm development consisted of training on manually labeled ("tagged") images. In that process, critical findings were tagged in all CTs in the training data set.
The Aidoc BriefCase-Triage device, intended for triaging cervical spine CT scans for fractures, underwent a retrospective, blinded, multicenter study to evaluate its performance.
Here's a breakdown of the acceptance criteria and study details:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Sensitivity (performance goal ≥ 80%) | 92.1% (95% CI: 87.5%, 95.4%) |
| Specificity (performance goal ≥ 80%) | 92.6% (95% CI: 89.0%, 95.4%) |
| Time-to-Notification (comparable to predicate) | 15.1 seconds (95% CI: 14.1-16.2) |
2. Sample size used for the test set and the data provenance
- Sample Size: 487 cases
- Data Provenance: Retrospective, multicenter study from 6 US-based clinical sites. The cases collected for the pivotal dataset were all distinct in time or center from the cases used to train the algorithm.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Number of Experts: Three
- Qualifications: Senior board-certified radiologists
4. Adjudication method for the test set
The document does not explicitly state the adjudication method (e.g., 2+1, 3+1). However, it implies that the ground truth was "as determined by three senior board-certified radiologists," suggesting a consensus-based approach among these experts.
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
There is no MRMC comparative effectiveness study presented in this document that evaluates human readers' improvement with AI assistance versus without AI assistance. The study focuses on the standalone performance of the AI device and its time-to-notification compared to a predicate device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, a standalone algorithm-only performance study was done. The primary endpoints (sensitivity and specificity) and secondary endpoints (time-to-notification, PPV, NPV, PLR, NLR) directly measure the performance of the BriefCase-Triage software itself.
7. The type of ground truth used
The ground truth was established by expert consensus (determined by three senior board-certified radiologists).
8. The sample size for the training set
The document states, "The algorithm was trained during software development on images of the pathology." However, it does not specify the sample size for the training set. It only mentions that the test pivotal study data was sequestered from algorithm development activities.
9. How the ground truth for the training set was established
The ground truth for the training set was established by manually labeled ("tagged") images. The document states, "critical findings were tagged in all CTs in the training data set." This implies expert annotation of the training data.
§ 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.