(56 days)
Not Found
Yes
The device description explicitly states that Viz ICH uses an "artificial intelligence machine (AI/ML) software algorithm" to analyze images.
No.
The device is a notification tool that uses AI to analyze images for findings suggestive of a prespecified clinical condition and notify a medical specialist; it is not intended for diagnostic use beyond notification and is not used to treat or prevent a disease.
No
The text explicitly states multiple times that the device is "not for diagnostic use beyond notification" and "not intended for diagnostic use beyond notification," and that "Images that are previewed through the mobile application...for informational purposes only and not intended for diagnostic use beyond notification." It also specifies that "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."
Yes
The device description explicitly states "Viz ICH is a software-only, parallel workflow tool". It further details that the system is a combination of software modules (image analysis algorithm and mobile application) and is hosted on servers. There is no mention of accompanying hardware components that are part of the regulated device.
Based on the provided information, no, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are medical devices used to examine specimens taken from the human body (like blood, urine, tissue) to provide information about a person's health. They are used outside of the body.
- Viz ICH Function: Viz ICH analyzes medical images (non-contrast CT scans of the brain) that are acquired from the patient's body. It does not analyze biological specimens taken from the body.
- Intended Use: The intended use clearly states it's a "notification-only, parallel workflow tool" to identify and communicate images of specific patients to a specialist. It explicitly states that the "Identification of suspected findings is not for diagnostic use beyond notification." While it uses an AI algorithm to analyze images for findings suggestive of a condition, its purpose is to alert a specialist to review the images, not to perform a diagnostic test on a biological sample.
Therefore, Viz ICH falls under the category of a medical device that processes and analyzes medical images, not an In Vitro Diagnostic device.
No
The letter does not state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
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.
Product codes
QAS
Device Description
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.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Computed Tomography, non-contrast (NCCT)
Anatomical Site
Head
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Neurovascular or Neurosurgical Specialist
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Not Found
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
387 Non-contrast Computed Tomography (NCCT) scans (studies) were obtained from two clinical sites in the U.S. There were approximately equal numbers of positive and negative cases (50.6% images with ICH and 49.4% without ICH, respectively) included in the analysis.
Sensitivity and specificity were calculated in the image database, comparing the Viz ICH's output to ground truth as established by trained neuro-radiologists. Sensitivity and specificity were 95% (91% - 98%) and 96% (92% - 98%), respectively. Because the lower bound of each confidence interval exceeded 80%, the study met the pre-specified performance goals of 80% for sensitivity and specificity.
In addition, the area under the receiver operating characteristic curve (AUC) was 0.97, demonstrating the clinical utility and potential benefits of the classifier based on the imaging study results.
In the study, the average time to alerting a specialist was 0.49±0.08 minutes, which is lower than the average time to notification seen in the Standard of Care of 18.3±14.2 minutes.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Sensitivity and specificity were 95% (91% - 98%) and 96% (92% - 98%), respectively.
Area under the receiver operating characteristic curve (AUC) was 0.97.
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 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.
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March 23, 2021
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Viz.ai, Inc. % Mr. Gregory Ramina Director of Regulatory Affairs 350 Rhode Island Street, Suite 240 SAN FRANCISCO CA 94103
Re: K210209
Trade/Device Name: Viz ICH Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QAS Dated: January 26, 2021 Received: January 26, 2021
Dear Mr. Ramina:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmp/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or post-marketing safety reporting (21 CFR 4, Subpart B) for combination products (see
1
https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
510(k) Number (if known)
Device Name
Viz ICH
Indications for Use (Describe)
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 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.
Type of Use (Select one or both, as applicable)
🇿 Prescription Use (Part 21 CFR 801 Subpart D)
□ Over-The-Counter Use (21 CFR 801 Subpart C)
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510(k) SUMMARY of K210209
Viz.ai, Inc.'s Viz ICH
| Applicant Name: | Viz.ai, Inc. |
|---|---|
| 555 De Haro St Suite 400 | |
| San Francisco, CA 94107 |
- Contact Person: Gregory Ramina Director of Regulatory Affairs 350 Rhode Island Street Suite 240 San Francisco, CA 94103 Tel. (415) 663-6130 Greg@viz.ai
Date Prepared: January 26, 2021
Device Name and Classification
| Name of Device: | Viz ICH |
|---|---|
| Common or Usual Name: | Radiological Computer-Assisted Triage and Notification Software |
| Classification Panel: | Radiology |
| Regulation No: | 21 C.F.R. § 892.2080 |
| Regulatory Class: | Class II |
| Product Code: | QAS |
Predicate Device
| Manufacturer | Device Name | Application No. |
|---|---|---|
| Viz.ai, Inc. | Viz ICH | K193658 |
Device Description
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.
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Image /page/4/Picture/0 description: The image shows a blue logo that is shaped like a stylized letter V. The logo is made up of several curved and angled segments that are arranged to create the overall V shape. The segments are all the same shade of blue, and there are small gaps between them.
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.
Intended Use / Indications for Use
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 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.
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Image /page/5/Picture/0 description: The image shows a stylized blue logo. The logo is composed of several curved and angular shapes arranged vertically. The overall shape resembles an abstract letter 'V' or a stylized funnel. The logo has a modern and clean design.
Summary of Technological Characteristics
The subject device, Viz ICH, is substantially equivalent to the predicate device, the previously cleared version of the Viz ICH device (K193658). In comparing the technological characteristics, both the subject and predicate devices use an artificial intelligence algorithm and mobile notification software to identify and notify specialists of patients with a suspected intracranial hemorrhage. Where the subject and predicate differ is that software algorithm for the subject device is not restricted to processing NCCT scans of the head acquired on General Electric (GE) scanners.
Both the subject and the predicate devices include mobile application software that allows a user to receive push notifications for patients identified with a suspected ICH by their respective software algorithms. Both devices interface with a non-diagnostic mobile DICOM image viewer to allow the specialist user to preview non-diagnostic images and view patient details associated with a series.
When used with the Viz VIEW mobile application software, the Viz ICH mobile notification software module is subject to the same non-diagnostic viewing limitations as the predicate and has the same non-diagnostic warning on the image viewing screen as the predicate.
| Subject Device | Predicate Device | |
|---|---|---|
| Viz ICH | Viz ICH | |
| Application No. | K210209 | K193658 |
| Product Code | QAS | QAS |
| Regulation No. | 21 C.F.R. § 892.2080 | 21 C.F.R. § 892.2080 |
| Anatomical Region | Head | Head |
| Diagnostic | ||
| Application | Notification-only | Notification-only |
| Notification/ | ||
| Prioritization | Yes | Yes |
| Intended User | Neurovascular or Neurosurgical | |
| Specialist | Neurovascular or Neurosurgical Specialist | |
| DICOM Compatible | Yes | Yes |
| Data Acquisition | Acquires medical image data from | |
| DICOM compliant imaging devices | ||
| and modalities. | Acquires medical image data from DICOM | |
| compliant imaging devices and modalities. | ||
| Supported Imaging | ||
| Modality | Computed Tomography, non- | |
| contrast (NCCT) | Computed Tomography, non-contrast | |
| (NCCT) | ||
| Alteration of Original | ||
| Image | No | No |
| Results of Image | ||
| Analysis | Internal, no image marking | Internal, no image marking |
| Preview Images | Initial assessment; non-diagnostic | |
| purposes | Initial assessment; non-diagnostic | |
| purposes | ||
| View DICOM Data | DICOM Information about the | |
| patient, study and current image. | DICOM Information about the patient, study | |
| and current image. |
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Image /page/6/Picture/0 description: The image shows a blue logo that is composed of several curved and angular shapes. The logo is vertically oriented and has a symmetrical design. The shapes are arranged in a way that suggests a stylized letter 'V' or an abstract representation of wings or feathers.
Performance Data
387 Non-contrast Computed Tomography (NCCT) scans (studies) were obtained from two clinical sites in the U.S. There were approximately equal numbers of positive and negative cases (50.6% images with ICH and 49.4% without ICH, respectively) included in the analysis.
Sensitivity and specificity were calculated in the image database, comparing the Viz ICH's output to ground truth as established by trained neuro-radiologists. Sensitivity and specificity were 95% (91% - 98%) and 96% (92% - 98%), respectively. Because the lower bound of each confidence interval exceeded 80%, the study met the pre-specified performance goals of 80% for sensitivity and specificity.
In addition, the area under the receiver operating characteristic curve (AUC) was 0.97, demonstrating the clinical utility and potential benefits of the classifier based on the imaging study results.
Image /page/6/Figure/5 description: The image is a Receiver Operating Characteristic (ROC) curve for Viz ICH. The x-axis represents the false positive rate, and the y-axis represents the true positive rate. The ROC curve has an area of 0.97, indicating good performance. A dotted line indicates a threshold of 47 voxels.
In the study, the average time to alerting a specialist was 0.49±0.08 minutes, which is lower than the average time to notification seen in the Standard of Care of 18.3±14.2 minutes. This data generally demonstrates that specialists have the opportunity to become involved in the clinical workflow early with notifications from the Viz ICH software.
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Image /page/7/Picture/0 description: The image shows a blue logo. The logo is composed of several curved lines that are stacked on top of each other. The lines are arranged in a way that creates a stylized letter V shape. The logo is simple and modern in design.
Stratification of Device Performance
| Device Performance by Clinical Site | ||||
|---|---|---|---|---|
| Clinical Site | Sensitivity [95% CI] | Specificity [95% CI] | ||
| Site 001 | 0.94 [0.86, 0.98] | 0.97 [0.89, 1.0] | ||
| Site 002 | 0.96 [0.91, 0.99] | 0.95 [0.90, 0.98] |
| Device Performance by Age | ||
|---|---|---|
| Age Range (Years) | Sensitivity [95% CI] | Specificity [95% CI] |