(29 days)
Rapid LVO is a radiological computer aided triage and notification software indicated for use in the analysis of CTA head images. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communication of suspected positive ICA or MCA-M1 Large Vessel Occlusion (LVO) findings in head CTA images.
Rapid LVO uses a software algorithm to analyze images and highlight cases with suspected LVO 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 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 LVO 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 LVO is a radiological computer-assisted triage and notification software device. The Rapid LVO module is a contrast enhanced CTA module which operates within the integrated Rapid Platform to provide triage and notification of suspected ICA and MCA-M1 Large Vessel Occlusion (LVO) based on the following definitions:
ICA Occlusion: A high-grade stenosis or occlusion of the intracranial portion of the ICA.
MCA-M1 Occlusion: A high-grade stenosis or occlusion of the horizontal segment of the MCA-M1, defined as the segment which extends from the ICA terminus until the vessel has turned upward into the Sylvian fissure. This includes post-bifurcation M1 segments in some patients.
The LVO module uses traditional programming algorithms. The output of the module is a priority notification to clinicians indicating the suspicion of LVO based on positive findings. The Rapid LVO module uses the basic services supplied by the Rapid Platform including DICOM processing, job management, imaging module execution and imaging output including the notification and compressed image.
The Rapid LVO device, a radiological computer-aided triage and notification software for detecting Large Vessel Occlusions (LVO) in CTA head images, was evaluated against specific acceptance criteria.
- Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criterion | Reported Device Performance |
|---|---|
| Sensitivity (Se): Lower bound of 95% Confidence Interval (CI) ≥ 80% | 0.96 (95% CI: 0.91 - 0.97) |
| Specificity (Sp): Lower bound of 95% Confidence Interval (CI) ≥ 80% | 0.98 (95% CI: 0.93 - 0.99) |
| Time to Notification: ≤ 3.5 minutes | 3.18 minutes (95% CI: 3.11 - 3.25) |
Additionally, the following performance metrics were reported:
- Positive Predictive Value (PPV): 0.98
- Negative Predictive Value (NPV): 0.96
- Receiver Operating Characteristic (ROC) AUC: 0.99
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Sample Size and Data Provenance for the Test Set
- Sample Size: 217 scans (135 positive LVO cases, 82 negative LVO cases).
- Data Provenance: The data was collected from 8 sites/studies, with locations in both the US and OUS (Outside the US). The document does not explicitly state if the data was retrospective or prospective, but clinical validation testing typically uses retrospective data for ground truth establishment.
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Number of Experts and Qualifications for Ground Truth
- Number of Experts: Three expert neuroradiologists.
- Qualifications: They are described as "expert neuroradiologists." Specific years of experience are not provided.
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Adjudication Method for the Test Set
- The ground truth was established using a "2:3 concurrence" method. This implies that at least two out of the three expert neuroradiologists had to agree on the presence or absence of an LVO for a case to be assigned its ground truth label.
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Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No MRMC comparative effectiveness study was mentioned. The study focused on the standalone performance of the algorithm.
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Standalone Performance Study
- Yes, a standalone performance study was conducted. The reported sensitivity, specificity, PPV, NPV, and ROC AUC are all measures of the algorithm's performance without human-in-the-loop assistance.
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Type of Ground Truth Used
- The ground truth was established by "expert neuroradiologists" using a "2:3 concurrence" method. This indicates expert consensus was the basis for the ground truth.
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Sample Size for the Training Set
- The document does not explicitly state the sample size used for the training set. It only details the validation set used for performance testing.
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How Ground Truth for the Training Set Was Established
- The document does not provide details on how the ground truth for the training set was established. It only describes the ground truth establishment for the validation/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.