(137 days)
Viz ANEURYSM (Viz ANX) is a radiological computer-assisted triage and notification software device for analysis of CT images of the head. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and prioritizing studies with suspected aneurysms during routine patient care.
Viz ANEURYSM uses an artificial intelligence algorithm to analyze images and highlight studies with suspected aneurysms in a standalone application for study list prioritization or triage in parallel to ongoing standard of care. The device generates compressed preview images that are meant for informational purposes only and not intended for diagnostic use. The device does not alter the original medical image and is not intended to be used as a diagnostic device.
Analyzed images are available for review through the standalone application. When viewed through the standalone application the images are for informational purposes only and not for diagnostic use. The results of Viz ANEURYSM, in conjunction with other clinical information and professional judgment, are to be used to assist with triage/prioritization of medical images. Radiologists who read the original medical images are responsible for the diagnostic decision. Viz ANEURYSM is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.
Viz ANEURYSM is limited to detecting aneurysms at least 4mm in diameter.
Viz ANEURYSM (Viz ANX) is a radiological computer-assisted triage and notification software device for analysis of CTA images of the head. The software automatically receives and analyzes CT angiogram (CTA) imaging of the head for image features that indicate the presence of an aneurysm using an artificial intelligence algorithm, and prioritizes patient imaging in a standalone application for workflow triage and review by a radiologist in parallel to standard of care image interpretation.
Viz ANEURYSM is a combination of software modules that consists of an image analysis software algorithm and mobile application software module. The Viz ANEURYSM Image Analysis Algorithm is an artificial intelligence machine (Al/ML) software algorithm that analyzes CTA images of the head for an aneurysm. Images acquired during patient care are forwarded to Viz.ai's Backend server where they are analyzed by the Viz ANEURYSM artificial intelligence algorithm for an aneurysm.
Viz ANEURYSM includes a mobile software module that enables the end user to view cases identified by the Viz ANEURYSM algorithm to contain a suspected aneurysm. The Viz ANEURYSM mobile software module is implemented into Viz.ai's generic non-diagnostic DICOM image mobile viewing application, Viz VIEW, which displays CTA scans that are sent to the Backend server. When the Viz ANEURYSM mobile software module is enabled, studies determined by the algorithm to contain a suspected aneurysm are highlighted in the standalone mobile application for study list prioritization or triage in parallel to ongoing standard of care. The user can also view compressed preview images and a non-diagnostic preview of the analyzed CTA scan of the patient through the mobile application.
The preview images and additional patient imaging available through the standalone mobile application are meant for informational purposes only and not intended for diagnostic use. The results of Viz ANEURYSM, in conjunction with other clinical information and professional judgment, are to be used to assist with triage/prioritization of medical images. Radiologists who read the original medical images are responsible for the diagnostic decision.
Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:
1. Table of Acceptance Criteria & Reported Device Performance
| Metric | Acceptance Criteria (Lower Bound 95% CI) | Reported Device Performance (Point Estimate [95% CI]) |
|---|---|---|
| Sensitivity | > 80% | 0.93 [0.83, 0.98] |
| Specificity | > 80% | 0.89 [0.85, 0.93] |
2. Sample Size and Data Provenance
- Test Set Sample Size: 315 scans
- 67 positive scans (21.3%)
- 248 negative scans (78.7%)
- Data Provenance: Not explicitly stated regarding country of origin or whether it was retrospective or prospective.
3. Number of Experts and Qualifications for Ground Truth
- Number of Experts: Not explicitly stated as a specific number, but "trained neuro-radiologists" were used.
- Qualifications of Experts: "trained neuro-radiologists". Specific years of experience are not mentioned.
4. Adjudication Method for the Test Set
- The text states ground truth was "established by trained neuro-radiologists." It does not specify a detailed adjudication method (e.g., 2+1, 3+1 consensus). It implies a consensus, but the process is not detailed.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No MRMC comparative effectiveness study was done to show how much human readers improve with AI vs. without AI assistance.
- The study primarily focuses on the standalone performance of the AI algorithm.
- However, a time-to-notification analysis was performed, showing that the Viz ANEURYSM time-to-notification was faster than the standard of care time-to-notification for all 20 cases used in the time analysis.
- Average time to notification (device): 219.8 seconds (3.67 minutes)
- Median time to notification (device): 203.44 seconds (3.39 minutes)
- Average time to notification (Standard of Care): 2613.0 seconds (43.6 minutes)
- Median time to notification (Standard of Care): 1620.0 seconds (27.0 minutes)
6. Standalone (Algorithm Only) Performance Study
- Yes, a standalone performance study was done. The reported sensitivity, specificity, and AUC are all metrics of the algorithm's performance independent of human-in-the-loop assistance.
7. Type of Ground Truth Used
- Expert Consensus: Ground truth was established by "trained neuro-radiologists."
8. Sample Size for the Training Set
- The sample size for the training set is not provided in the document. The document only references the "image database" used for analysis, which appears to be the test set.
9. How 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 only describes the ground truth establishment for the test set used to demonstrate performance.
§ 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.