(123 days)
Not Found
Yes.
The device description explicitly states, "Viz Subdural+ is a software-only device that uses a locked artificial intelligence machine learning (AI/ML) algorithm to process and analyze non-contrast CT (NCCT) scans of the head".
No
This device is intended for automatic labeling, visualization, and quantification of collections in the subdural space from NCCT images. It automates the measurement of subdural collections and midline shift, providing information to assist physicians in diagnosis and monitoring, but it does not directly treat or prevent a medical condition.
Yes
The device is intended for "automatic labeling, visualization and quantification of collections in the subdural space" and to "automate the current manual process of identifying, labeling and quantifying the volume of collections," indicating its role in assisting with a medical diagnosis or condition.
Yes
The device explicitly states in the "Device Description" section that "Viz Subdural+ is a software-only device." While it processes CT images and produces outputs, these are handled by software and do not involve any physical hardware provided or required by the device itself beyond the existing hospital infrastructure (CT scanner, PACS).
No.
The device processes medical images (NCCT scans) to identify and quantify subdural collections. It does not perform tests on biological samples or specimens.
No
The letter does not mention that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
The Viz Subdural+ (Subdural Plus) device is intended for automatic labeling, visualization and quantification of collections in the subdural space from a set of Non-Contrast Head CT (NCCT) images. The software is intended to automate the current manual process of identifying, labeling and quantifying the volume of collections in the subdural space identified on NCCT images. Viz Subdural + provides volumes from NCCT images acquired at a single time point.
The Viz Subdural+ software is intended for labeling subdural collections and reporting the grayscale value of the collection, widest width of the subdural collection, and midline shift. The device output should be reviewed along with the patient's original images by a physician qualified to interpret brain CT images.
Product codes
QIH
Device Description
Viz Subdural+ is a software-only device that uses a locked artificial intelligence machine learning (AI/ML) algorithm to process and analyze non-contrast CT (NCCT) scans of the head to automatically measure the collections in the subdural region in the brain and midline shift.
The device output provides visual overlays of automatically measured subdural collections where the overlay opacity (intensity) corresponds to the grayscale value of the collection within the native NCCT, and reports the total volume and widest width of the subdural collections. The device also automates and reports the measure of midline shift.
The results of the automated measurement are provided in a summary series and segmentation series in DICOM format. The summary series consists of a summary table of subdural collections, snapshot of each collection and a midline shift measurement. The first slice of the Subdural+ summary series summarizes the measurement results of each subdural collection (volume and widest width), total volume and midline shift in tabular format. The summary series also contains a snapshot of each subdural collection and a snapshot of the midline shift measurement. The segmentation series shows an RGB overlay where a subdural collection is identified by a colored overlay with the color intensity corresponding to the HU values of the original image on each slice of the input series of the segmented region. On slices with an overlay representing a measured subdural collection, the volume of the subdural collection is provided. The midline shift is overlaid and provided on the slice where the midline shift is measured.
Images are automatically forwarded from the Healthcare Facility and sent to Viz.ai's Backend Server after acquisition at the CT scanner. Viz Subdural+ is hosted on Viz.ai's Backend Server and automatically analyzes applicable NCCT scans that are acquired on CT scanners and are forwarded to Viz.ai's Backend Server. The results of the analysis are exported in DICOM format and are sent to a DICOM destination (e.g., PACS) where they are available for review by radiologists, neurologists, neuro-surgeons, interventional neuroradiologists, or other appropriately trained professionals to assist in the measurement of subdural collection volume, widest subdural collection width and midline shift.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Non-contrast CT (NCCT)
Anatomical Site
Head
Indicated Patient Age Range
Not Found
Intended User / Care Setting
radiologists, neurologists, neuro-surgeons, interventional neuroradiologists, or other appropriately trained professionals to assist in the measurement of subdural collection volume, widest subdural collection width and midline shift.
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
A retrospective study was conducted to assess the standalone performance of the image analysis algorithm for Viz Subdural+ as compared to a ground truth established by trained neuroradiologists in segmenting, labeling and quantifying subdural collections, maximal subdural collection width (thickness) and midline shift. Subdural collection measurement performance (volume and thickness) and midline shift were assessed on datasets with 203 and 151 cases, respectively. Each dataset was obtained from two clinical sites. Imaging within each dataset were from patients that received an NCCT imaging assessment after presenting to one of the participating sites.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
A retrospective study was conducted to assess the standalone performance of the image analysis algorithm for Viz Subdural+ as compared to a ground truth established by trained neuroradiologists in segmenting, labeling and quantifying subdural collections, maximal subdural collection width (thickness) and midline shift. Subdural collection measurement performance (volume and thickness) and midline shift were assessed on datasets with 203 and 151 cases, respectively. Each dataset was obtained from two clinical sites. Imaging within each dataset were from patients that received an NCCT imaging assessment after presenting to one of the participating sites.
The results of the retrospective study demonstrated the device passed the primary endpoints for the study in terms of mean absolute error (MAE).
Additional stratification of the device performance as measured by MAE was provided by different patient demographic, technical and radiographic findings to demonstrate generalizability of the device within the intended population. Device results for subdural collection volume, widest width and midline shift were also compared to the truther consensus by Bland-Altman plots and linear regression analysis.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Subdural Collection Volume (N=203): Mean Absolute Error (MAE) 7.53 (5.60, 9.45), Standard Deviation 13.91, Median (10th - 90th Percentile) 2.70 (0.0 - 22.22), DICE Score 73% (68% - 77%)
Subdural Collection Maximum (Widest) Thickness (N=203): Mean Absolute Error (MAE) 1.77 (1.24, 2.30), Standard Deviation 3.84, Median (10th - 90th Percentile) 0.43 (0.0 - 5.37)
Midline Shift (N=151): Mean Absolute Error (MAE) 1.1 (0.94,1.27), Standard Deviation 1.03, Median (10th - 90th Percentile) 0.8 (0.17 - 2.37)
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
FDA 510(k) Clearance Letter - Viz Subdural+
Page 1
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
Doc ID # 04017.07.05
June 10, 2025
Viz.ai, Inc.
Gregory Ramina
Regulatory Affairs Director
5000 Center Green Way
Cary, North Carolina 27513
Re: K250354
Trade/Device Name: Viz Subdural+, Viz SUBDURAL PLUS
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical Image Management And Processing System
Regulatory Class: Class II
Product Code: QIH
Dated: May 5, 2025
Received: May 5, 2025
Dear Gregory 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 (the 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 available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/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.
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K250354 - Gregory Ramina Page 2
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
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 (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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-devices/medical-device-safety/medical-device-reporting-mdr-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/medical-devices/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-devices/device-advice-comprehensive-regulatory-
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K250354 - Gregory Ramina Page 3
assistance/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,
Jessica Lamb, Ph.D.
Assistant Director
DHT8B: Division of Radiological Imaging
Devices and Electronic Products
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
Page 4
DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration
Indications for Use
Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.
Submission Number (if known): K250354
Device Name: Viz Subdural+, Viz SUBDURAL PLUS
Indications for Use (Describe)
The Viz Subdural+ (Subdural Plus) device is intended for automatic labeling, visualization and quantification of collections in the subdural space from a set of Non-Contrast Head CT (NCCT) images. The software is intended to automate the current manual process of identifying, labeling and quantifying the volume of collections in the subdural space identified on NCCT images. Viz Subdural + provides volumes from NCCT images acquired at a single time point.
The Viz Subdural+ software is intended for labeling subdural collections and reporting the grayscale value of the collection, widest width of the subdural collection, and midline shift. The device output should be reviewed along with the patient's original images by a physician qualified to interpret brain CT images.
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)
CONTINUE ON A SEPARATE PAGE IF NEEDED.
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DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.
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Page 5
510(k) Summary
Viz Subdural+
Applicant Name: Viz.ai, Inc.
5000 Center Green Way
Cary NC, 27513
Contact Person: Gregory Ramina
Regulatory Affairs Director
5000 Center Green Way
Cary NC, 27513
Tel. (415) 663-6130
Greg@viz.ai
Date Prepared: May 1, 2025
Device Name and Classification
Name of Device: Viz Subdural+, Viz SUBDURAL PLUS
Common or Usual Name: Automated Radiological Image Processing Software
Classification Panel: Radiology
Regulation No: 21 C.F.R. § 892.2050
Regulatory Class: Class II
Product Code: QIH
Predicate Device(s)
| Manufacturer | Device Name | Application No. |
|---|---|---|
| Viz.ai, Inc. | Viz HDS | K232363 |
Device Description
Viz Subdural+ is a software-only device that uses a locked artificial intelligence machine learning (AI/ML) algorithm to process and analyze non-contrast CT (NCCT) scans of the head to automatically measure the collections in the subdural region in the brain and midline shift.
The device output provides visual overlays of automatically measured subdural collections where the overlay opacity (intensity) corresponds to the grayscale value of the collection within the native
Page 6
NCCT, and reports the total volume and widest width of the subdural collections. The device also automates and reports the measure of midline shift.
The results of the automated measurement are provided in a summary series and segmentation series in DICOM format. The summary series consists of a summary table of subdural collections, snapshot of each collection and a midline shift measurement. The first slice of the Subdural+ summary series summarizes the measurement results of each subdural collection (volume and widest width), total volume and midline shift in tabular format. The summary series also contains a snapshot of each subdural collection and a snapshot of the midline shift measurement. The segmentation series shows an RGB overlay where a subdural collection is identified by a colored overlay with the color intensity corresponding to the HU values of the original image on each slice of the input series of the segmented region. On slices with an overlay representing a measured subdural collection, the volume of the subdural collection is provided. The midline shift is overlaid and provided on the slice where the midline shift is measured.
Figure 1. Examples of the summary table from the summary series output (left) and a slice from the segmentation series output (right). The summary series would include an additional summary image (snapshot) of each collection in the table and a summary image of the midline shift. The color overlay gradient in the slice from the segmentation series output corresponds to the Hounsfield Unit (HU) of each corresponding pixel in the NCCT.
Images are automatically forwarded from the Healthcare Facility and sent to Viz.ai's Backend Server after acquisition at the CT scanner. Viz Subdural+ is hosted on Viz.ai's Backend Server and automatically analyzes applicable NCCT scans that are acquired on CT scanners and are forwarded to Viz.ai's Backend Server. The results of the analysis are exported in DICOM format and are sent to a DICOM destination (e.g., PACS) where they are available for review by radiologists, neurologists, neuro-surgeons, interventional neuroradiologists, or other appropriately trained professionals to
Page 7
assist in the measurement of subdural collection volume, widest subdural collection width and midline shift.
Figure 2. Data flow diagram for Viz Subdural+.
Intended Use and Indications for Use
The Viz Subdural+ (Subdural Plus) device is intended for automatic labeling, visualization and quantification of collections in the subdural space from a set of Non-Contrast Head CT (NCCT) images. The software is intended to automate the current manual process of identifying, labeling and quantifying the volume of collections in the subdural space identified on NCCT images. Viz Subdural+ provides volumes from NCCT images acquired at a single time point.
The Viz Subdural+ software is intended for labeling subdural collections and reporting the grayscale value of the collection, widest width of the subdural collection, and midline shift. The device output should be reviewed along with the patient's original images by a physician qualified to interpret brain CT images.
Summary of Technological Characteristics
The subject device, Viz Subdural+, is substantially equivalent to the predicate device, Viz HDS (K232363). In comparing the technological characteristics, both the subject and predicate devices use an artificial intelligence algorithm to identify, label and quantify measured quantities in NCCT imaging of the head from images acquired at a single time point. Both the subject and predicate devices use software algorithms that incorporate artificial-intelligence to perform as intended. Both devices' algorithms automatically receive, assess the applicability of received input imaging, and automatically process and measure supported imaging. Both devices' algorithms use similar pipelines with similar steps to measure their indicated structures and both devices' algorithms use deep-learning convolutional neural networks with similar architectures. Both devices provide their outputs in DICOM format and return the results to a pre-configured destination (e.g., a PACS server) for the user to view the device outputs.
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While both devices provide an automated measurement of midline shift, the subject device's algorithm is different from the predicate device's algorithm and is designed and indicated for automatically labeling and quantifying subdural collections (subdural collection volume and widest width) whereas the predicate device's algorithm is indicated and designed for automatically labeling and measuring the volume of intracranial hyperdensities and lateral ventricles. Performance testing demonstrated that Viz Subdural+ has acceptable subdural collection volume and collection width measurement performance. Additionally, the Viz Subdural+ algorithm can measure midline shift within the same performance limits (MAE