VBrain is a software device intended to assist trained medical professionals, during their clinical workflows of radiation therapy treatment planning, by providing initial object contours of known (diagnosed) brain tumors and organs at risk in the brain (i.e., the region of interest, ROI) on axial T1 contrast-enhanced brain MRI images. VBrain is intended to be used on adult patients only.

VBrain uses an artificial intelligence algorithm (i.e., deep learning neural networks) to contour (segment) brain tumor and organs at risk (brain stem, eyes, optic nerves, optic chiasm) in the brain on MRI images for trained medical professionals' attention, which is meant for informational purposes only and not intended for replacing their current standard practice of manual contouring process. VBrain does not alter the original MRI image, nor does it intend to detect tumors for diagnosis. VBrain is intended only for generating Gross Tumor Volume (GTV) contours of brain metastases, meningiomas, and acoustic neuromas, and contours of organs at risk in the brain; it is not intended to be used with images of other brain tumors or other body parts. The user must know the tumor type when they use VBrain.

VBrain also contains the automatic image registration feature to register volumetric medical image data. (e.g., MR, CT). It allows rigid image registration to adjust the spatial position and orientation of two images.

Medical professionals must finalize (confirm or modify) the contours generated by VBrain, as necessary, using an external platform available at the facility that supports DICOM-RT viewing/editing functions, such as image visualization software and treatment planning system.

Device Description

VBrain is a software application system intended for use in the contouring (segmentation) of brain MRI images and in the registration of multi-modality images. The device consists of a workflow management module and 3 algorithm modules, which are the tumor contouring algorithm module, OAR contouring algorithm module, and registration algorithm modules can work independently, and yet can be integrated with each other.

The tumor contouring (segmentation) algorithm module consists of image preprocessing, deep learning neural networks, and postprocessing components, and is intended to contour brain tumor on the axial T1 contrast-enhanced MR images. It utilizes deep learning neural networks to generate contours for the detected/diagnosed brain tumors and export the results as DICOM-RT objects (using the RT Structure Set ROI Contour attribute, RTSTRUCT).

The OAR contouring (segmentation) algorithm module consists of image preprocessing, deep learning neural networks, and postprocessing components, and is intended to contour organs at risk in the brain on the axial T1 contrast-enhanced MR images. It utilizes deep learning neural networks to generate contours for the organs at risk in the brain and export the results as DICOM-RT objects (using the RT Structure Set ROI Contour attribute, RTSTRUCT).

The registration algorithm module registers volumetric medical image data (e.g., MR, CT). It allows rigid image registration to adjust the spatial position and orientation of two images.

The workflow management module is configured to work on a PACS network. Upon user's request, it will pull patient scans from a PACS, and it will trigger a predefined workflow, in which different algorithm modules are executed to generate the DICOM output. The DICOM output of a workflow can be sent back to the PACS.

AI/ML Overview

The provided text describes a 510(k) summary for VBrain and references previous 510(k) submissions (K203235 and K212116) for the predicate devices. However, the current document does not explicitly state the acceptance criteria and the study results for the current device. It only mentions that "The protocol, methods and acceptance criteria of software verification and validation testing used to evaluate the changes were not modified from those used in the predicate submission. The acceptance criteria and a summary of the results were provided for each test. VBrain passed all V&V testing, performance requirements and specifications are met."

To provide a complete answer, I would need access to the predicate submissions (K203235 and K212116) which presumably contain the detailed acceptance criteria and study results.

Based only on the provided text, here’s what can be inferred or stated:

1. A table of acceptance criteria and the reported device performance

The document states that "The acceptance criteria and a summary of the results were provided for each test. VBrain passed all V&V testing, performance requirements and specifications are met." However, the specific criteria and performance values are not detailed in this submission. This document highlights that the protocols, methods, and acceptance criteria were not modified from the predicate submissions, implying that the performance metrics from the predicate devices are applicable and the current device met those established benchmarks.

To present a table, I would need the specific metrics (e.g., Dice Similarity Coefficient, Hausdorff Distance, etc.) and their thresholds from the predicate summaries, which are not in the provided text.

2. Sample size used for the test set and the data provenance

This information is not provided in the current document. It retrospectively refers to the V&V testing from the predicate submissions.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

This information is not provided in the current document. It retrospectively refers to the V&V testing from the predicate submissions.

4. Adjudication method for the test set

This information is not provided in the current document. It retrospectively refers to the V&V testing from the predicate submissions.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

The document describes VBrain as a "software device intended to assist trained medical professionals... by providing initial object contours," and states that "Medical professionals must finalize (confirm or modify) the contours generated by VBrain". This indicates a human-in-the-loop workflow. However, the current document does not report on a direct MRMC comparative effectiveness study or the effect size of human reader improvement with AI assistance. This information, if available, would likely be in the predicate submissions.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

The document implies standalone performance evaluation based on the statement that VBrain "generates contours for the detected/diagnosed brain tumors and exports the results as DICOM-RT objects." The V&V testing would have evaluated the accuracy of these generated contours against ground truth. The acceptance criteria for such standalone performance are referenced to the predicate submissions but are not explicitly listed here.

7. The type of ground truth used

This information is not explicitly provided in the current document. It retrospectively refers to the V&V testing from the predicate submissions. It is common for such devices to use expert consensus contours (often by radiologists or radiation oncologists) as the ground truth for segmentation accuracy, but this is not confirmed in the provided text.

8. The sample size for the training set

This information is not provided in the current document.

9. How the ground truth for the training set was established

This information is not provided in the current document.

In summary of what is available:

The current 510(k) submission for VBrain (K213628) primarily focuses on the substantial equivalence of the modified device to its two predicate devices (K203235 and K212116). It states that the "protocol, methods and acceptance criteria of software verification and validation testing used to evaluate the changes were not modified from those used in the predicate submission" and that "VBrain passed all V&V testing, performance requirements and specifications are met." Therefore, the detailed acceptance criteria and study particulars are implicitly relying on the documentation provided in the predicate submissions, which are not included in the provided text.

Summary

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Vysioneer Inc. % Vicki Lin Regulatory Specialist 33 Rogers. St., # 308 Cambridge. MA 02142

Re: K213628

Trade/Device Name: VBrain Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QKB Dated: November 12, 2021 Received: November 17, 2021

Dear Vicki Lin:

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/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.

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 and Part 809); medical device reporting of medical device-related adverse events) (21 CFR

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for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see 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,

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

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Indications for Use

510(k) Number (if known) K213628

Device Name V Brain

Indications for Use (Describe)

V Brain is a software device intended to assist trained medical professionals, during their clinical workflows of radiation therapy treatment planning, by providing initial object contours of known (diagnosed) brain tumors and organs at risk in the brain (i.e., the region of interest, ROI) on axial T1 contrast-enhanced brain is intended to be used on adult patients only.

V Brain uses an artificial intelligence algorithm (i.e., deep learning neural networks) to contour (segment) brain tumor and organs at risk (brain stem, eyes, optic chiasm) in the brain on MRI images for trained medical professionals' attention, which is meant for informational purposes only and not intended for replacing their current standard practice of manual contouring process. VBrain does not alter the original MRI image, nor does it intend to detect tumors for diagnosis. VBrain is intended only for generating Gross Tumor Volume (GTV) contours of brain metastases,

meningiomas, and acoustic neuromas, and contours of organs at risk in the brain; it is not intended to be used with images of other brain tumors or other body parts. The user must know the tumor type when they use VBrain.

V Brain also contains the automatic image registration feature to register volumetric medical image data. (e.g., MR, CT). It allows rigid image registration to adjust the spatial position and orientation of two images.

Medical professionals must finalize (confirm or modify) the contours generated by V Brain, as necessary, using an external platform available at the facility that supports DICOM-RT viewing/editing functions, such as image visualization software and treatment planning system.

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

This 510(k) Summary is submitted in accordance with 21 CFR 807.92

Submitter

Vysioneer Inc.

33 Rogers St. #308, Cambridge, MA 02142

Contact Person:	Vicki Lin
Phone:	609-865-8659
Email:	vicki.lin@vysioneer.com
Date Summary Prepared:	November 12, 2021

Device Name

Trade Name:	VBrain
Common Name:	Radiological Image Processing Software forRadiation Therapy
Classification Name:	Medical image management and processingsystem (21 CFR 892.2050)
Regulatory Class:	II
Product Code:	QKB

Predicate Devices

Predicate Device: VBrain

510(k) Holder/Submitter: Vysioneer Inc.

510(k) Number: K203235 (Cleared on 03/19/2021)

Predicate Device: VBrain-OAR

510(k) Holder/Submitter: Vysioneer Inc.

510(k) Number: K212116 (Cleared on 10/12/2021)

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Intended Use / Indications for Use

Device Description

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Image /page/5/Picture/1 description: The image contains the logo for Vysioneer. The logo consists of the word "VYSIONEER" in a sans-serif font, with a stylized "V" shape above it. The color of the text and the "V" shape is a dark purple.

The registration algorithm module registers volumetric medical image data (e.g., MR, CT). It allows rigid image registration to adjust the spatial position and orientation of two images.

Comparison of Technological Characteristics with the Predicate Devices

The intended use and features of the modified device VBrain are a combination of those of the two predicate devices. The modified device uses VBrain's (K203235) tumor contouring algorithm, VBrain-OAR's (K212116) organs at risk contouring algorithm, registration algorithm and application. Each algorithm works independently and does not interfere with the others, yet they could be integrated in a predefined pipeline to work consecutively.

Same as the predicate devices, the modified device, VBrain is an AI-based (deep learning) software devices intended to be used in the workflow of radiation therapy treatment planning by providing tools to automatically segment/contour tumors and organs at risk on images as well as perform image registration (image fusion). VBrain uses the identical Deep Neural Networks and registration method as the predicates.

Minor modifications are made to increase convenience of use and improve functionality. Risk analysis and V&V testing were performed, and the results confirm that the modifications do not change the device's safety and effectiveness.

The modified device is substantially equivalent to the predicate devices because it has identical intended use, technological characteristics, and principles of operation as the predicates.

Performance Data

Regression testing was performed to verify and validate the changes of V Brain in accordance with FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" for software devices identified as Major Level of Concern related to radiation therapy treatment planning.

The protocol, methods and acceptance criteria of software verification and validation testing used to evaluate the changes were not modified from those used in the predicate submission. The acceptance criteria and a summary of the results were provided for each test. VBrain passed all V&V testing, performance requirements and specifications are met.

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Substantially Equivalent (SE) Conclusion

VBrain is considered substantially equivalent to the predicate devices.

Verification and validation testing and hazard analysis demonstrate that VBrain performs within its design specifications and is as safe and effective as the predicate. The minor modifications of the device do not introduce any new potential risks. Based on the information presented in these 510(k) premarket notifications, it could be concluded that the subject device VBrain is as safe and effective as the predicate devices, with the same intended use, technological characteristics, and principles of operation.

Regulation Number and Section

§ 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).