(79 days)
Viz ICH is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of care workflow.
Viz ICH uses an artificial intelligence algorithm to analyze images for findings suggestive of a prespecified clinical condition and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the brain acquired in the acute setting, and sends notifications to a neurovascular or neurosurgical specialist that a suspected intracranial hemorrhage has been identified and recommends review of those images. Images can be previewed through a mobile application.
lmages that are previewed through the mobile application may be compressed and are for informational purposes only and not intended for diagnostic use beyond notification. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests. Viz ICH 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 ICH is contraindicated for analyzing non-contrast CT scans that are acquired on scanners from manufacturers other than General Electric (GE) or its subsidiaries (i.e. GE Healthcare). This contraindication applies to NCCT scans that conform to all applicable Patient Inclusion Criteria, are of adequate technical image quality, and would otherwise be expected to be analyzed by the device for a suspected ICH.
Viz ICH is a software-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to an appropriate specialist, such as a neurovascular specialist or neurosurgeon, independent of the standard of care workflow. The system automatically receives and analyzes non-contrast CT (NCCT) studies of patients for image features that indicate the presence of an intracranial hemorrhage (ICH) using an artificial intelligence algorithm, and upon detection of a suspected ICH, sends a notification so as to alert a specialist clinician of the case.
Viz ICH consists of backend and mobile application component software. The Backend software includes a DICOM router and backend server. The DICOM router transmits NCCT images of the head acquired on a local healthcare network to the Backend Server. The Backend Server receives, stores, processes and serves received NCCT scans. The Backend Server also includes an artificial intelligence algorithm that analyzes the received NCCT images for image characteristics that indicate an intracranial haemorrhage (ICH) and, upon detection, sends a notification of the suspected finding to pre-determined specialists.
The Viz ICH Mobile Application software receives notifications generated by the Backend of suspected image findings and allows the notification recipient to view the analyzed NCCT images through a non-diagnostic viewer, as well as patient information that was embedded in the image metadata. Image viewing through the mobile application is for informational purposes only and is not intended for diagnostic use.
Here's a summary of the acceptance criteria and study details for Viz ICH, based on the provided FDA 510(k) summary:
1. Table of Acceptance Criteria and Reported Device Performance
| Metric | Acceptance Criteria (Pre-specified performance goal) | Reported Performance (95% CI) |
|---|---|---|
| Sensitivity | ≥ 80% | 93% (87%-97%) |
| Specificity | ≥ 80% | 90% (84%-94%) |
| AUC | Not explicitly stated as an acceptance criterion, but 0.96 was demonstrated as clinical utility | 0.96 |
| Time to Alert | Not explicitly stated as an acceptance criterion for the device, but comparative data was provided | 0.49 ± 0.15 minutes (device) vs. 38.2 ± 84.3 minutes (Standard of Care) |
2. Sample size used for the test set and the data provenance
- Sample Size: 261 non-contrast Computed Tomography (NCCT) scans (studies). Approximately equal numbers of positive (47%) and negative (53%) cases were included.
- Data Provenance: Retrospective study. Data obtained from two clinical sites in the U.S.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Number of Experts: Not explicitly stated, 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 document implies a consensus-based ground truth ("ground truth, as established by trained neuro-radiologists"). However, the specific adjudication method (e.g., 2+1, 3+1) is not detailed.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done
- No, a multi-reader multi-case (MRMC) comparative effectiveness study with human readers was not described. The study focused on the standalone performance of the AI algorithm and a comparison of notification times.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Yes, a standalone performance study of the image analysis algorithm was conducted. The sensitivity and specificity reported are for the algorithm only.
7. The type of ground truth used
- Expert consensus, established by "trained neuro-radiologists," in the detection of intracranial hemorrhage (ICH).
8. The sample size for the training set
- The sample size for the training set is not provided in the document. The information focuses only on the test set.
9. How the ground truth for the training set was established
- The method for establishing ground truth for the training set is not described in the provided document.
§ 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.