(93 days)
Rapid CTA 360 is a radiological computer aided triage and notification software indicated for use in the analysis of CTA adult head images. The device is intended to assist hospital networks and trained clinicians in workflow triage by flagging and communication of suspected positive Large and Medium Vessel Occlusion findings in head CTA images including the ICA (C1-C5), MCA (M1-M3), ACA, PCA, Basilar and Vertebral vascular segments.
Rapid CTA 360 uses an AI software algorithm to analyze images and highlight cases with suspected occlusion on a server or standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected LVO and MVO findings. Notifications include compressed preview images. These are meant for informational purposes only and are 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 Rapid CTA 360 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.
Rapid CTA 360 device is a radiological computer-assisted Triage and Notification Software device using AI/ML. The Rapid CTA 360 processing module operates within the integrated Rapid Platform to provide triage and notification of suspected large and medium vessel neuro-occlusions. The Rapid CTA 360 software analyzes input Head and Neck CTA images that are provided in DICOM format and provides notification of suspected positive results. The device does not alter the original medical image and is not intended to be used as a diagnostic device.
Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) Clearance Letter:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criterion | Description | Reported Device Performance |
|---|---|---|
| Primary Endpoint: Sensitivity | Ability of the device to correctly identify true positive cases of Large and Medium Vessel Occlusion (LVO and MVO). | 0.921 (95% CI: 0.880, 0.949) |
| Primary Endpoint: Specificity | Ability of the device to correctly identify true negative cases (no LVO or MVO). | 0.890 (95% CI: 0.832, 0.929) |
| Secondary Endpoint: Time to Notification | The time taken by the device to provide a notification of suspected occlusion. | 3.2 minutes (min: 1.92 min to 5.35 min) |
| Sensitivity Analysis (High Grade Stenosis) | Sensitivity specifically for cases involving high grade stenosis (a potential confounder). | 87.4% (95% CI: 0.829-0.908) |
| Specificity Analysis (High Grade Stenosis) | Specificity specifically for cases involving high grade stenosis (a potential confounder). | 89.0% (95% CI: 0.832-0.929) |
2. Sample size used for the test set and the data provenance
- Test Set Sample Size: 403 CTA cases
- Data Provenance: The data was collected from multiple sites (not explicitly stated which countries, but the training data was primarily US, which might suggest a similar distribution for the test set or at least a representative one). The cases were selected to cover patient demographics (age, gender), manufacturer distributions (GE, Toshiba, Siemens, Philips scanners), and confounders. The data was "collected and blinded prior to use, per internal data management procedures which includes isolation of development and product validation cohorts," implying a retrospective collection, but carefully separated from the training data.
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 of Experts: Not explicitly stated beyond "experts."
4. Adjudication method for the test set
- Adjudication Method: 2 out of 3 (2:3 concurrence). This means that for a case to be considered positive or negative for ground truth, at least two of the three experts had to agree on the finding.
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 MRMC comparative effectiveness study involving human readers with and without AI assistance was mentioned in the provided text. The study focused on the standalone performance of the AI device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Yes, a standalone performance validation was explicitly stated as being conducted: "Final device validation included standalone performance validation, per the special controls."
7. The type of ground truth used
- Ground Truth Type: Expert consensus. The document states, "ground truth established by 3 experts (2:3 concurrence)."
8. The sample size for the training set
- Training Set Sample Size: 6264 cases
9. How the ground truth for the training set was established
- The document implies that the ground truth for the training set was established through expert review and annotation, as the cases were used for "Algorithm development, including training and testing." It mentions the selection criteria for cases (demographics, scanner manufacturers, confounders) which would likely lead to expert-verified labels as ground truth, but the exact method (e.g., specific number of experts, adjudication) for the training set is not detailed in the same way as for the test set.
§ 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.