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Found 3 results
510(k) Data Aggregation
(182 days)
Viz HDS, Viz Volume Plus, Viz ICH+
The Viz HDS device is intended for automatic labeling, visualization, and quantification of segmentable brain structures from a set of Non-Contrast CT (NCCT) head scans. The software is intended to automate the current manual process of identifying, labeling, and quantifying the volume of segmentable brain structures identified on NCCT images. Viz HDS provides volumes from NCCT scans acquired at a single time point. The Viz HDS software is indicated for use in the analysis of the following structures: Intracranial Hyperdensities, Lateral Ventricles and Midline Shift. The device output should be reviewed along with patient's original images by a physician.
Viz HDS is a software-only device that uses a locked artificial intelligence machine learning (AI/ML) algorithm to processes non-contrast head CT scans to outline intracranial hyperdensity areas, lateral ventricles (right and left), midline shift, and then quantify the volume of intracranial hyperdensity(ies), volume of lateral ventricle asymmetry ratio and distance of midline shift.
Viz HDS analyzes the head NCCT series in DICOM format and produces a summary series and a segmentation series in DICOM format. The summary series is a two-slice output: a single slice from the NCCT series with segmented areas overlaid on it, and a summary table providing the calculated measurements. The segmentation series shows an RGB overlay, on each slice of the input series, of the lateral ventricles and hyperdensity(ies) segmentation masks and a midline shift. For slices including hyperdensities or ventricle/ventricles, its volume would be mentioned in a color legend that is also overlaid on the slice. The colors are only for visual differentiation between the segmented regions, the colors don't have a meaning on their own. The device output is exported in DICOM format, which is sent to a pre-configured PACS destination together with the original NCCT series for review by a physician to aid in the assessment of measuring intracranial hyperdensity(ies), lateral ventricles, and midline shift.
Here's a breakdown of the acceptance criteria and the study details for the Viz HDS device, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria and Device Performance for Viz HDS
| Measurement Trait | Acceptance Criteria (Upper 95% CI Bound) | Reported Device Performance (Upper 95% CI Bound) |
|---|---|---|
| Hyperdensities Total Volume (MAE) | ≤ 7.5 mL | 70% (Lower CI Bound) |
| Both Lateral Ventricles (DICE Score) | > 70% (Lower CI Bound) | > 70% (Lower CI Bound) |
Note: The text explicitly states "less than 7.5 mL" and "greater than 70%", confirming the device met the specified criteria.
2. Sample Size and Data Provenance for Test Set
- Sample Size for Test Set: Not explicitly stated in the provided text.
- Data Provenance: Not explicitly stated in the provided text. The text mentions "clinical site" for stratification, but not the origin of the data itself (e.g., country, specific hospitals).
- Retrospective or Prospective: Not explicitly stated.
3. Number and Qualifications of Experts for Ground Truth (Test Set)
- Number of Experts: Not explicitly stated. The text mentions "trained radiologists" was involved in establishing the ground truth.
- Qualifications of Experts: The experts were "trained radiologists." No further details on their experience (e.g., "10 years of experience") are provided.
4. Adjudication Method (Test Set)
- Adjudication Method: Not explicitly stated. The text only mentions that "ground truth as established by trained radiologists." It does not detail how disagreements among radiologists, if any, were resolved.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- MRMC Study: No, an MRMC comparative effectiveness study was not explicitly mentioned as being performed. The study described compares the device's output to "ground truth as established by trained radiologists" and does not describe a scenario where human readers' performance with and without AI assistance was measured.
- Effect Size of Human Readers Improvement: Not applicable, as an MRMC study was not performed.
6. Standalone Performance (Algorithm Only)
- Standalone Performance: Yes, a standalone performance study was done. The study compares the Viz HDS's output directly to the established ground truth. This is a measure of the algorithm's performance without direct human intervention in the measurement process itself, although the output is intended for physician review.
7. Type of Ground Truth Used
- Type of Ground Truth: The ground truth was established by "expert consensus" from trained radiologists. The text states, "ground truth as established by trained radiologists."
8. Sample Size for Training Set
- Sample Size for Training Set: Not explicitly stated in the provided text.
9. How Ground Truth for Training Set Was Established
- Ground Truth for Training Set: Not explicitly stated in the provided text.
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(56 days)
Viz ICH
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 standard 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 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 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 is a combination of software modules that consists of an image analysis software algorithm and mobile application software module. The Viz ICH image analysis software algorithm is an artificial intelligence machine (AI/ML) software algorithm that analyzes non-contract CT images of the head for an intracranial hemorrhage. The Viz ICH Image Analysis Algorithm is hosted on Viz.ai's servers and analyzes applicable stroke-protocoled NCCT images of the head that are acquired on CT scanners and are forwarded to Viz.ai servers. Upon detection of a suspected intracranial hemorrhage, the Viz ICH Image Analysis Algorithm sends a notification of the suspected finding.
Viz ICH includes a mobile software module that enables the end user to receive and toggle notifications for suspected intracranial hemorrhages identified by the Viz ICH Image Analysis Algorithm. The Viz ICH mobile notification software module is implemented into Viz.ai's non-diagnostic DICOM image viewer, Viz VIEW, which displays CT scans that are sent to Viz.ai's servers. When the Viz ICH mobile notification software module is enabled for a user, the user can receive and toggle the notifications for patients with a suspected intracranial hemorrhage, view a unique patient list of patients with a suspected intracranial hemorrhage, and view the non-diagnostic CT scan of the patient through the Viz VIEW mobile application. Image viewing through the mobile application interface is for nondiagnostic purposes only.
Viz ICH Acceptance Criteria and Performance Study
This document describes the acceptance criteria for the Viz ICH device and the study conducted to demonstrate its performance.
1. Table of Acceptance Criteria and Reported Device Performance
| Metric | Acceptance Criteria (Lower Bound 95% CI) | Reported Device Performance [95% CI] |
|---|---|---|
| Sensitivity | ≥ 80% | 95% [91% - 98%] |
| Specificity | ≥ 80% | 96% [92% - 98%] |
| AUC | Not explicitly stated (but 0.97 indicates strong performance) | 0.97 |
| Time to Alert | Not explicitly stated (but improvement over standard of care is implied) | 0.49 ± 0.08 minutes |
2. Sample Size and Data Provenance
- Test Set Sample Size: 387 Non-contrast Computed Tomography (NCCT) scans (studies).
- Data Provenance: Two clinical sites in the U.S. (Retrospective, as the study was to evaluate the performance of an already developed algorithm on existing data).
3. Number, Qualifications, and Adjudication of Experts for Ground Truth
- Number of Experts: Not explicitly stated, but referred to as "trained neuro-radiologists."
- Qualifications of Experts: "Trained neuro-radiologists." Specific years of experience are not mentioned.
- Adjudication Method: Not explicitly stated, but "ground truth as established by trained neuro-radiologists" implies a consensus or majority vote among multiple experts.
4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- Was an MRMC study done? No. The study focuses on the standalone performance of the AI algorithm.
- Effect size of human readers with AI vs. without AI assistance: Not applicable as no MRMC study was conducted. However, the study does mention the average time to alert:
- Viz ICH: 0.49 ± 0.08 minutes
- Standard of Care: 18.3 ± 14.2 minutes
This reduction in alert time implies a significant improvement in the speed of notification for human specialists.
5. Standalone Performance
- Was a standalone (algorithm only) performance study done? Yes. The provided performance data (sensitivity, specificity, AUC) directly reflects the algorithm's performance compared to ground truth.
6. Type of Ground Truth Used
- Type of Ground Truth: Expert consensus, specifically "ground truth as established by trained neuro-radiologists" for the presence or absence of intracranial hemorrhage.
7. Sample Size for Training Set
- Sample Size for Training Set: Not explicitly stated in the provided document.
8. How Ground Truth for Training Set Was Established
- How Ground Truth for Training Set Was Established: Not explicitly stated in the provided document. It is generally assumed that the training data ground truth would also be established by clinical experts, similar to the test set.
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(79 days)
Viz ICH
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.
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