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510(k) Data Aggregation
(29 days)
Vysioneer Inc.
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.
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.
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.
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(97 days)
Vysioneer Inc.
VBrain-OAR is a software device intended to assist trained radiotherapy personnel including, but not limited to, radiologists, radiation oncologists, neurosurgeons, radiation therapists, and medical physicists, during their clinical workflows of brain tumor radiation therapy treatment planning, by providing initial object contours of organs at risk in the brain (i.e., the region of interest, ROI) on axial T1 contrast-enhanced brain MRI images. VBrain-OAR is intended to be used on adult patients only.
VBrain-OAR uses an artificial intelligence algorithm (i.e., deep learning neural networks) to contour (segment) organs at risk (brain stem, eyes, optic nerves, optic chiasm) in the brain on MRI images for trained radiotherapy personnel's attention, which is meant for informational purposes only and not intended for replacing their current standard practice of manual contouring process. VBrain-OAR does not alter the original MRI image, nor does it intend to detect tumors for diagnosis. VBrain-OAR is intended only for contouring and generating contours of organs at risk in the brain; it is not intended to be used with images of other body parts.
VBrain-OAR also contains the automatic image register volumetric medical image data. (e.g., MR, CT). It allows rigid image registration to adjust the spatial position and orientation of two images. Radiation therapy treatment personnel must finalize (confirm or modify) the contours generated by VBrain-OAR, as necessary, using an external platform available at the facility that supports DICOM-RT viewinglediting functions, such as image visualization software and treatment planning system.
VBrain-OAR is a software application system indicated for use in the contouring (segmentation) of brain MRI images for the organs at risk (OAR) in the brain during radiation treatment planning and in the registration of multi-modality images. The device consists of 2 algorithm modules, which are contouring algorithm module and registration algorithm module, and a workflow management module. The modules can work independently, and yet can be integrated with each other.
The 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 or users can send corresponding DICOM images, 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 are sent back to the PACS.
This document, a 510(k) premarket notification for Vysioneer Inc.'s VBrain-OAR, focuses on the device's technical characteristics and claims of substantial equivalence to predicate devices, but does not provide the specific acceptance criteria and detailed study results (including performance metrics like Dice Similarity Coefficient, Hausdorff Distance, or expert review scores) that would typically be required to fully describe how the device met these criteria.
The document states that "performance testing was conducted to evaluate the contouring (segmentation) performance and registration performance of VBrain-OAR" and that "the auto-segmentation algorithm of the VBrain-OAR algorithm module provides clinically acceptable contours for organs at risk in the brain structures on an image of a patient." However, it does not explicitly define what constitutes "clinically acceptable" or provide the quantitative results from these tests.
Therefore, many of the requested details cannot be extracted directly from the provided text. I will provide information based on what is available and indicate where details are missing.
Acceptance Criteria and Device Performance Study (Based on Provided Text)
The document generally states that the device's performance was evaluated, and it met "clinically acceptable" standards. However, the specific quantitative acceptance criteria (e.g., minimum Dice Similarity Coefficient, maximum Hausdorff Distance) are not detailed in the provided text. Similarly, the reported device performance (quantitative results) against these criteria is also not included in this summary.
In the absence of specific acceptance criteria and performance results, the table below is illustrative of what would typically be included in such a section, but the "Acceptance Criteria" and "Reported Device Performance" columns cannot be filled with concrete numbers from the provided document.
1. Table of Acceptance Criteria and Reported Device Performance
| Feature/Metric | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Contouring (Segmentation) Performance | Not explicitly stated in document (e.g., Min. Dice Similarity Coefficient, Max. Hausdorff Distance, Expert Review Score) | Not explicitly stated in document (e.g., Achieved Dice scores, Hausdorff distances, Qualitative assessment) |
| Brain Stem Contouring | Clinically acceptable* | Clinically acceptable* |
| Eyes Contouring | Clinically acceptable* | Clinically acceptable* |
| Optic Nerves Contouring | Clinically acceptable* | Clinically acceptable* |
| Optic Chiasm Contouring | Clinically acceptable* | Clinically acceptable* |
| Registration Performance | Not explicitly stated in document (e.g., Registration accuracy in mm) | Not explicitly stated in document (e.g., Achieved registration accuracy) |
| Rigid image registration | Substantially equivalent to predicate device | Substantially equivalent to predicate device |
Note: The term "clinically acceptable" is used in the document but is not defined with quantitative metrics.
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: The document states "VBrain-OAR was tested on datasets from multiple institutions" for standalone performance testing. However, the exact number of cases or scans in the test set is not specified.
- Data Provenance: The document mentions "datasets from multiple institutions" and "data across patient sex, multiple imaging hardware and protocols" was used for testing. However, the country of origin of the data is not specified. The document also does not explicitly state whether the data was retrospective or prospective, though typical 510(k) submissions for AI devices often rely on retrospective datasets for performance testing.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
The provided document does not specify the number of experts used to establish the ground truth for the test set, nor does it detail their qualifications (e.g., radiologist with X years of experience). It's implied that "trained radiotherapy personnel" are involved in the standard practice of manual contouring which the device aims to assist, but this does not directly describe the ground truth establishment process for the test data.
4. Adjudication Method for the Test Set
The document does not describe any specific adjudication method (e.g., 2+1, 3+1, none) used for establishing the ground truth for the test set.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
The provided summary does not indicate that an MRMC comparative effectiveness study was performed to assess how human readers improve with AI vs. without AI assistance. The device is described as an "assistance" tool, but an MRMC study demonstrating improvement in human performance is not mentioned. The focus is on the standalone performance of the AI algorithm.
6. If a Standalone (Algorithm Only) Performance Study Was Done
Yes, a standalone performance study was done. The document explicitly states under "5.7 Non-Clinical Test (Standalone Performance Data)":
"Standalone performance testing was conducted to evaluate the contouring (segmentation) performance and registration performance of VBrain-OAR."
7. The Type of Ground Truth Used
The type of ground truth used is implied to be expert consensus or expert-derived manual contours. The device aims to "provide initial object contours... on axial T1 contrast-enhanced brain MRI images" and states it's "not intended for replacing their current standard practice of manual contouring process." This suggests that human expert manual contours would serve as the ground truth against which the AI's generated contours are compared. However, the exact methodology for establishing this ground truth (e.g., single expert, multi-expert consensus) for the test set is not detailed.
8. The Sample Size for the Training Set
The document does not specify the sample size used for the training set. It mentions the use of "deep learning neural networks" implying a training phase, but provides no details on the data used.
9. How the Ground Truth for the Training Set Was Established
The document does not specify how the ground truth for the training set was established. While it is implied that expert manual contours would be used given the device's function, the details of this process for the training data are not provided.
Ask a specific question about this device
(136 days)
Vysioneer Inc.
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 (i.e., region of interest, ROI) on axial T1 contrast-enhanced brain MRI images.
VBrain uses an artificial intelligence algorithm (i.e., deep learning neural networks) to contour (segment) brain tumor 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 be used to detect tumors for diagnosis. VBrain is intended only for generating Gross Tumor Volume (GTV) contours of brain metastases, meningiomas, and acoustic neuromas on axial T1 contrast-enhanced MRI images; It is not intended to be used with images of other brain tumors. The user must know the tumor type when they use VBrain. VBrain is intended to be used on adult patients only.
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.
VBrain is a software device indicated for use in the analysis of brain MRI images. The device consists of image preprocessing, deep learning neural networks, and postprocessing components, and is intended to assist trained medical professionals, during clinical workflows of radiation therapy treatment planning, by highlighting and contouring known (diagnosed) brain tumors on the axial T1 contrast-enhanced MRI images. The software is configured to work on a PACS network. Upon user's request, it will patient scans or users can send corresponding MR images, and the device will utilize 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) back to the network. The medical professionals must finalize (confirm and modify) the contours produced by VBrain as necessary using an external platform that supports RT DICOM viewing/editing, such as a treatment planning system.
The provided text describes the performance data for Vysioneer's VBrain device. Here's a breakdown of the acceptance criteria and the study that proves the device meets them:
1. Table of Acceptance Criteria and Reported Device Performance
| Performance Metric | Acceptance Criteria/Performance Goal (Implicitly "As Demonstrated") | Reported Device Performance (95% Confidence Interval) |
|---|---|---|
| Lesion-wise Sensitivity | Meets performance goals | 90.3% (86.1-93.7%) |
| False-Positive Rate (tumors/case) | Meets performance goals | 0.681 (0.500-0.879) |
| Lesion-wise Dice Similarity Coefficient (DSC) | Meets performance goals | 0.793 (0.775-0.811) |
| Average Hausdorff Distance (in terms of lesion size) | Meets performance goals | 5.0% (4.4-5.6%) |
| Centroid Distance (in terms of lesion size) | Meets performance goals | 5.6% (5.0-6.2%) |
Note: The document explicitly states "VBrain meets all performance goals" and "All the metrics were demonstrated to pass the performance goals," implying that the reported performance values themselves serve as the acceptance criteria being met.
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: 116 cases with 238 tumors.
- Data Provenance: Retrospective, multicenter, multinational. The data was acquired from 4 different institutions: 3 from the U.S. and 1 non-U.S.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
- Number of Experts: Three.
- Qualifications of Experts: Board-certified radiation oncologists.
4. Adjudication Method for the Test Set
- Method: Consensus. The ground truth of each tumor contour was generated from the consensus of the three board-certified radiation oncologists.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study to evaluate how human readers improve with AI vs. without AI assistance. The study focuses on the standalone performance of the VBrain algorithm relative to ground truth established by expert consensus.
6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study
- Yes, a standalone performance study was conducted. The reported metrics (Sensitivity, False-Positive Rate, DSC, Hausdorff Distance, Centroid Distance) directly evaluate the VBrain algorithm's performance in segmenting tumors against an expert-defined ground truth, without measuring human-in-the-loop performance improvement.
7. Type of Ground Truth Used
- Type: Expert Consensus. The ground truth for tumor contours was established by the consensus of three board-certified radiation oncologists.
8. Sample Size for the Training Set
- The document does not explicitly state the sample size used for the training set. It mentions that VBrain uses an "artificial intelligence algorithm (i.e., deep learning neural networks)" which implies a training phase, but the details of the training data are not provided in this specific excerpt.
9. How the Ground Truth for the Training Set Was Established
- The document does not explicitly state how the ground truth for the training set was established. While it describes the ground truth process for the test set, it does not detail the methodology for the training data used to develop the deep learning model.
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