(86 days)
StrokeSENS LVO is a radiological computer-aided triage and notification (CADt) software indicated for use in the analysis of CTA head images. The device is intended to assist hospital networks and trained clinicians in workflow triage by flagging and communication of suspected positive findings of Large Vessel Occlusion (LVO) in head CTA images.
StrokeSENS LVO uses a software algorithm to identify suspected LVO findings. In the case of a suspected LVO, the system will send a notification to a pre-configured destination(s), notifying the clinicians of the existence of a suspected LVO that requires review. The notification system is intended to be used in parallel to the standard of care workflow to notify clinicians of the existence of a potential LVO earlier than being notified as part of the standard of care workflow.
Notifications may include a compressed preview of images. Notifications are meant for informational purposes only and are not intended for diagnostic use beyond notification. The StrokeSENS LVO device does not alter the original medical image and is not intended to be used as a diagnostic device.
The results of StrokeSENS LVO are intended to be used in conjunction with other patient information and based on professional judgement, to assist with triage / prioritization of medical images. Notified clinicians are responsible for viewing full images per standard of care.
StrokeSENS LVO is intended to assist hospital networks and trained clinicians in workflow triage by flagging and communication of suspected positive findings of Large Vessel Occlusion (LVQ) in head CTA images. StrokeSENS LVO uses a software alqorithm based on machine learning to identify suspected LVO findings. In the case of a suspected LVO, the system will send a notification to a preconfigured destination(s), notifying the clinicians of a suspected LVQ that requires review.
StrokeSENS LVO DICOM-compliant software system consists of two main components: 1) the StrokeSENS LVO Processing Engine and 2) a compatible Radiological Software Platform:
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- The StrokeSENS LVO Processing Engine is responsible for receiving, processing, and analyzing image data and communicating results. Primarily, the Processing Engine consists of a software algorithm (a sequence of instructions/operations) that is responsible for analyzing contrast-enhanced CT (CTA) image data of the head to identify characteristics that are consistent with LVO. The software algorithm is a binary classifier, providing a binary output of either positive or negative for suspected LVO, based on a pre-defined threshold. The output is returned to the Radiological Software Platform for the purposes of triage and notification. The Processing Engine is integrated into, or installed adjacent to, a compatible Radiological Software Platform.
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- The compatible Radiological Software Platform is configured to retrieve/receive contrast-enhanced head CT (CTA) images from the CT scanner or PACS, and automatically transmit, or make available, a copy of the image data for processing and analysis by the LVO Processing Engine. After successful processing of a case via the StrokeSENS LVO Processing Engine, the results are returned to the Radiological Software Platform for the intended purpose of triage and notification.
Here's a breakdown of the acceptance criteria and study details for the StrokeSENS LVO device:
1. Table of Acceptance Criteria and Reported Device Performance
| Test | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Sensitivity | Sensitivity > 80% | 89.4% (95% CI = 85.3%, 93.5%) |
| Specificity | Specificity > 80% | 87.4% (95% CI = 82.6%, 92.2%) |
| Processing Time |
§ 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.