(192 days)
syngo.CT LVO Detection is a radiological post-processing application for the analysis of CT angiography (CTA) head images. syngo.CT LVO Detection supports computer-aided triage, and it addresses vascular abortions in the CTA of the brain, commonly referred to as large vessel occlusion (LVO), in the ICA, M1, and M2 segment. It is intended for all patient populations of age ≥ 22 years, without any of the following contraindications: old infarcts or other diseases impacting the brain vasculature (for example, brain tumors), metal artifacts (for example, coils), surgical signs in the images. The output for triage is intended for informational purposes only. It is not intended for diagnostic use and does not alter the original medical image.
The subject device syngo.CT LVO Detection is an image processing software that utilizes artificial intelligence learning algorithms to support qualified clinicians (Radiologists, Neuroradiologists, Neurologists) in prioritization of CT-angiography images by algorithmically identifying findings suspicious of a large vessel occlusion and providing notification to the user. syngo.CT LVO Detection provides a reproducible detection of large vessel occlusions (LVO) on contrast-enhanced CT examinations of the head for detection of ICA, M1, and M2 vessel occlusions in patients suspected of having stroke related circulation occlusion. syngo.CT LVO Detection analyses CT-angiography (CTA) images of the head. The subject device provides a pipeline for the analysis and identification of potential LVO The output which can be send to an external notification device does not highlight or direct attention of the reading physician to any portion of the image.
Here's a detailed breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) clearance letter for syngo.CT LVO Detection:
Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance | Comments |
|---|---|---|
| Sensitivity > 80% | 90.6% [86.8% - 93.3%] (95% CI) | Exceeds the predefined acceptance threshold. |
| Specificity > 80% | 88.8% [84.7% – 91.9%] (95% CI) | Exceeds the predefined acceptance threshold. |
| 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.