(245 days)
Not Found
Yes
The description mentions "automatic segmentation" and "model performance" evaluated against expert segmentations using metrics like Dice similarity coefficient and R2, which are common in ML-based image analysis. While "AI" or "ML" are not explicitly stated, the methodology described strongly suggests the use of such techniques for automated segmentation.
No.
The device is a post-processing software that analyzes and evaluates neurological MR image data to assist clinicians with diagnosis and monitoring, but it does not directly treat or prevent a disease or condition.
Yes
The device analyzes and evaluates neurological MR image data for brain tumors, provides segmentation, quantification, and reporting of image metrics, assists in interpretation of imaging, and supports comparison of derived image metrics from multiple time points.
Yes
The device description explicitly states "OnQ Neuro is a fully automated post-processing medical device software" and "The OnQ Neuro is a stand-alone medical device software package". It also mentions it does not have any accessories or patient contacting components, further supporting its software-only nature.
Based on the provided information, this device is NOT an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In vitro diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections. They are used to examine specimens outside of the body.
- OnQ Neuro's Function: OnQ Neuro processes and analyzes medical images (specifically neurological MR image data) that are acquired directly from the patient's body. It does not analyze biological samples taken from the patient.
- Intended Use: The intended use clearly states it's for analyzing and evaluating neurological MR image data, providing segmentation, quantification, and reporting of derived image metrics. This is image processing and analysis, not in vitro testing.
- Device Description: The description reinforces that it accepts DICOM images and performs analysis on this image data. There is no mention of handling biological specimens.
Therefore, OnQ Neuro falls under the category of medical image processing software, not an in vitro diagnostic device.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
OnQ Neuro is a fully automated post-processing medical device software intended for analyzing and evaluating neurological MR image data.
- OnQ Neuro is intended to provide automatic segmentation, quantification, and reporting of derived image metrics.
OnQ Neuro is additionally intended to provide automatic fusion of derived parametric maps with anatomical MRI data. OnQ Neuro is intended for use on brain tumors, which are known/confirmed to be pathologically diagnosed cancer.
OnO Neuro is intended for comparison of derived image metrics from multiple time-points.
The physician retains the ultimate responsibility for making the final diagnosis and treatment decision.
Product codes
QIH
Device Description
OnQ Neuro is a fully automated post-processing medical device software that is used by radiologists, oncologists, and other clinicians to assist with analysis and interpretation of neurological MR images. It accepts DICOM images using supported protocols and performs 1) automatic segmentation and volumetric quantification of brain tumors, which are known/confirmed to be pathologically diagnosed cancer, 2) automatic post-acquisition analysis of diffusion-weighted magnetic resonance imaging (DWI) data and optional automated fusion of derived image data with anatomical MR images, and 3) comparison of derived image metrics from multiple time-points.
Output of the software provides values as numerical volumes, and images of derived data as grayscale intensity maps and as graphical color overlays on top of the anatomical image. OnQ Neuro output is provided in standard DICOM format as image series and reports that can be displayed on most third-party commercial DICOM workstations.
The OnQ Neuro is a stand-alone medical device software package that is designed to be installed in the cloud or within a hospital's IT infrastructure on a server or PC-based workstation. Once installed and configured, the OnQ Neuro software automatically processes images sent from the originating system (MRI scanner or PACS). The software is configured at installation to receive input DICOM files from a network location, and output DICOM to a network destination.
The software is designed without the need for a user interface after installation. Any processing errors are reported either in the output series error report, or system log files.
OnQ Neuro software is intended to be used by trained personnel only and is to be installed by trained technical personnel.
Quantitative reports and derived image data sets are intended to be used as complementary information in the review of a case.
The OnQ Neuro software does not have any accessories or patient contacting components.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Not Found
Input Imaging Modality
MR imaging data, diffusion-weighted magnetic resonance imaging (DWI) data, anatomical MRI data, MR scans.
Anatomical Site
neurological MR images, brain tumors.
Indicated Patient Age Range
adult population only.
Intended User / Care Setting
radiologists, oncologists, and other clinicians.
Hospital, Clinic, Imaging Center, Medical Offices
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
Independent test dataset: used for evaluating the accuracy of OnQ Neuro v1.1.0's brain tumor segmentation.
Comparison to expert-labeled segmentations: OnQ Neuro's automatic segmentation performance is evaluated by comparing software-derived segmentations to expert-labeled segmentations of brain tumors.
Quantification methods: Dice similarity coefficient (extent of software-derived vs. ground truth overlap), and squared correlation coefficient (R2) of segmented region of interest volumes.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Performance Testing:
- Study Type: Accuracy of automated segmentation compared to manual radiologist segmentations.
- Standalone Performance: OnQ Neuro v1.1 model performance is consistent (95% percent performance) with expert rater manual segmentation performance and meets minimum clinically acceptable levels.
- Key Results: The results of the segmentation performance testing on an independent test dataset demonstrate that OnQ Neuro v1.1.0 segments brain tumor ROIs with an accuracy that passed the product's acceptance criteria. Further, segmentation performance is consistent across scanner manufacturers, field strengths, tumor types, and patient sexes. Conclusion: OnQ Neuro v1.1.0 brain tumor segmentation is sufficiently accurate to be used in clinical practice.
V&V Testing:
-
OnQ Neuro tumor segmentation performance tested using three methods:
- Objective unit testing: comparing software-derived values to known ground truth values.
- System testing: verifying correct generation of Tumor Segmentation RGB Overlay and Tumor Segmentation Report (when compatible anatomical images are input).
- Clinical validation testing: confirming Tumor Segmentation RGB Overlay and Tumor Segmentation Report are correct, meet clinical expectations, and are safe and effective.
-
OnQ Neuro diffusion modeling performance evaluated using three methods:
- Objective unit testing: comparing software-derived values using synthetic input data with multiple levels of Rician noise to known ground truth values.
- System testing: verifying correct generation of Restricted Signal Map and ADC map (when compatible diffusion images are input).
- Clinical validation testing: confirming Restricted Signal Map and ADC map are correct, meet clinical expectations, and are safe and effective.
-
Overall Results: The test results demonstrate that OnQ Neuro: 1) complies with international and FDA-recognized consensus standards and FDA guidance documents, and 2) meets acceptance criteria and is adequate for its intended use and specifications. Testing performed demonstrated that OnQ Neuro meets all defined functionality requirements and performance claims.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Dice similarity coefficient (extent of software-derived vs. ground truth overlap), and squared correlation coefficient (R2)
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
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).
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November 19, 2021
CorTechs Labs, Inc. % Kora Marinkovic Director of Quality and Regulatory Affairs 5060 Shoreham Place. Suite 240 SAN DIEGO CA 92122
Re: K210831
Trade/Device Name: OnQ Neuro Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: October 11, 2021 Received: October 15, 2021
Dear Kora Marinkovic:
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; medical device reporting (reporting of medical device-related adverse events) (21 CFR 803) for devices
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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) K210831
Device Name OnQ Neuro
Indications for Use (Describe)
OnQ Neuro is a fully automated post-processing medical device software intended for analyzing and evaluating neurological MR image data.
- OnQ Neuro is intended to provide automatic segmentation, quantification, and reporting of derived image metrics.
OnQ Neuro is additionally intended to provide automatic fusion of derived parametric maps with anatomical MRI data. OnQ Neuro is intended for use on brain tumors, which are known/confirmed to be pathologically diagnosed cancer.
OnO Neuro is intended for comparison of derived image metrics from multiple time-points.
The physician retains the ultimate responsibility for making the final diagnosis and treatment decision.
Type of Use (Select one or both, as applicable)
X Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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Image /page/3/Picture/0 description: The image shows a logo with a stylized brain graphic above the word "cortechs.ai" in a small, sans-serif font. The brain graphic is rendered in a light teal color and appears to be composed of interconnected lines, giving it a modern, tech-oriented feel. The logo is simple and clean, suggesting a focus on technology related to the brain or artificial intelligence.
510(k) Summary: OnQ Neuro
1. Submitter
| Name | CorTechs Labs, Inc. |
|---|---|
| Address | 5060 Shoreham Place, Suite 240 San Diego, CA 92122 |
| Contact Person | Kora Marinkovic |
| Telephone Number | (858) 459-9703 |
| Fax Number | (858) 459-9705 |
| koram@cortechslabs.com | |
| Date Prepared | 11/19/2021 |
2. Device
| Device Trade Name | OnQ Neuro |
|---|---|
| Common Name | Medical Image Processing Software |
| Classification Name | System, Image Processing, Radiological |
| Regulation Number | 21 CFR 892.2050 |
| Regulation Description | Medical image management and processing system |
| Product Code | QIH |
| Classification Panel | Radiology |
3. Predicate Devices
Primary Predicate Device
| Device | Multi-Modality Tumor Tracking (MMTT) |
|---|---|
| 510(k) Number | K162955 |
| Manufacturer | Philips Medical Systems |
| Product Code | LLZ |
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| Image: cortechsol logo | 510(k) Section/Document Number | 5 |
|---|---|---|
| Title | OnQ Neuro: 510(k) Summary | |
| Revision: 03 | Pages 2 of 6 | Date: 11/19/2021 |
4. Device Description
OnQ Neuro is a fully automated post-processing medical device software that is used by radiologists, oncologists, and other clinicians to assist with analysis and interpretation of neurological MR images. It accepts DICOM images using supported protocols and performs 1) automatic segmentation and volumetric quantification of brain tumors, which are known/confirmed to be pathologically diagnosed cancer, 2) automatic post-acquisition analysis of diffusion-weighted magnetic resonance imaging (DWI) data and optional automated fusion of derived image data with anatomical MR images, and 3) comparison of derived image metrics from multiple time-points.
Output of the software provides values as numerical volumes, and images of derived data as grayscale intensity maps and as graphical color overlays on top of the anatomical image. OnQ Neuro output is provided in standard DICOM format as image series and reports that can be displayed on most third-party commercial DICOM workstations.
The OnQ Neuro is a stand-alone medical device software package that is designed to be installed in the cloud or within a hospital's IT infrastructure on a server or PC-based workstation. Once installed and configured, the OnQ Neuro software automatically processes images sent from the originating system (MRI scanner or PACS). The software is configured at installation to receive input DICOM files from a network location, and output DICOM to a network destination.
The software is designed without the need for a user interface after installation. Any processing errors are reported either in the output series error report, or system log files.
OnQ Neuro software is intended to be used by trained personnel only and is to be installed by trained technical personnel.
Quantitative reports and derived image data sets are intended to be used as complementary information in the review of a case.
The OnQ Neuro software does not have any accessories or patient contacting components.
The OnQ application is intended to be used for the adult population only.
5. Indications for Use
OnQ Neuro is a fully automated post-processing medical device software intended for analyzing and evaluating neurological MR image data.
OnQ Neuro is intended to provide automatic segmentation, quantification, and reporting of derived image metrics.
OnQ Neuro is additionally intended to provide automatic fusion of derived parametric maps with anatomical MRI data.
OnQ Neuro is intended for use on brain tumors, which are known/confirmed to be pathologically diagnosed cancer.
OnQ Neuro is intended for comparison of derived image metrics from multiple time-points.
The physician retains the ultimate responsibility for making the final diagnosis and treatment decision.
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Image /page/5/Picture/9 description: The image shows a logo for a company called "cortechs.ai". The logo features a stylized image of a brain above the company name. The brain is drawn with thin, interconnected lines, giving it a modern and technological feel. The company name is written in a lowercase, sans-serif font.
| 510(k) Section/Document Number | 5 | |
|---|---|---|
| Title | OnQ Neuro: 510(k) Summary | |
| Revision: 03 | Pages 3 of 6 | Date: 11/19/2021 |
6. Predicate Device
Philips Medical Systems' Multi-Modality Tumor Tracking (MMTT, K162955) market-cleared device is identified as the primary predicate device for the OnQ Neuro application.
| 510(k) Number | Product Name | Submitter | |
|---|---|---|---|
| Primary Predicate | K162955 | Multi-Modality Tumor Tracking (MMTT) | Philips Medical Systems |
The proposed OnQ Neuro application and its predicate device, MMTT (K162955), are substantially equivalent in regard to their general intended uses, intended users, clinical indications, and principle of operation. They have similar basic design and features.
7. Comparison to Predicate Device
Summary Comparison Table for the device (OnQ Neuro) and the predicate device (MMTT)
| Feature | OnQ Neuro | MMTT: K162955 (Predicate) |
|---|---|---|
| Device Classification Name | System, Image processing, | |
| Radiological | System, Image processing, | |
| Radiological | ||
| Device Class | Class II | Class II |
| Classification Panel | Radiology | Radiology |
| Product Code | LLZ | LLZ |
| Regulation Description | Picture archiving and | |
| communication system | Picture archiving and | |
| communication system | ||
| Regulation number | 21 CFR 892.2050 | 21 CFR 892.2050 |
| Intended Use | MR imaging data post processing | |
| software | MR imaging data post processing | |
| software | ||
| Intended users | Radiologists, oncologists | Radiologists, oncologists |
| Type of Imaging Scan | MRI | CT, MR PET/CT and SPECT/CT |
| Intended Body part | Neurological images | All body images |
| Support Longitudinal Analysis | Yes - performs comparison of | |
| derived image metrics from | ||
| multiple time points | Yes | |
| Support 2D and 3D anatomical | ||
| sequences | Yes | Yes |
| Automatic image registration | ||
| between series within a study | Yes | Yes |
| Segmentation editing tools | No | Yes |
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| Image: cortechsol logo | 510(k) Section/Document Number
5 |
|------------------------|-------------------------------------|
| Title | OnQ Neuro: 510(k) Summary |
| Revision: 03 | Pages 4 of 6 Date: 11/19/2021 |
| Tumor Segmentation Type | Fully-automated | Semi-Automated and manual |
|---|---|---|
| Findings management of | ||
| identified tumors | Yes. Prior segmentation results | |
| are provided numerically. | Yes. | |
| Quantitative Analysis of | ||
| Regions-of-Interest | Yes. Including ROI Volumes, and | |
| histogram statistics of optional | ||
| quantitative maps. | Yes. Including Volume, | |
| Min/Max/Mean | ||
| Reporting | Results displayed in tabular and | |
| graphical formats. | Results displayed in tabular and | |
| graphical formats. | ||
| DICOM Communication | Yes | Yes |
| Diffusion Analysis | Yes, including single and multi- | |
| compartment diffusion models. | No | |
| Image Fusion | Automated fusion of segmentation | |
| results and parametric maps. | Automated fusion of | |
| segmentation results. | ||
| Safety | Display/measurement data can be | |
| viewed, accepted, or rejected by a | ||
| physician. | Display/measurement data can | |
| be viewed, accepted, or rejected | ||
| by a physician. | ||
| Environment for use | Hospital, Clinic, Imaging Center, | |
| Medical Offices | Hospital, Clinic, Medical Offices |
Description of Similarities and Differences: OnQ Neuro and MMTT (Predicate)
Both applications, the OnQ Neuro and the MMTT (Predicate), 1) are post-processing software applications for analysis of MR imaging data; 2) have the ability to perform volumetric quantification of MR imaging data; 3) offer the ability to compare medical images and/or multiple time-points; 4) enable visualization of information that would otherwise have to be visually compared disjointedly; 5) have the ability to report derived imaging metrics; and 6) are intended for use on brain tumors, which are known/confirmed to be pathologically diagnosed cancer.
They differ in that: 1) The Intended Use of the MMTT application includes language regarding viewing and manipulation of images. Specifically, MMTT provides editing tools for manual and semi-manual tumor annotation and allows loading of multiple concurrent studies for temporal measurements. OnQ Neuro does not include a GUI, so it does not include the ability to manually annotate and manipulate images; 2) While MMTT utilizes a manual, user-dependent feature to outline regions of interest, the OnQ Neuro software provides similar segmentation functionality automatically (i.e., without user intervention); 3) The OnQ Neuro software performs automated analysis of diffusion-weighted images and returns an Apparent Diffusion Coefficient (ADC) map and an enhanced DWI map (the Restricted Signal Map) showing the restricted component of the water diffusion signal, which the MMTT application does not include; 4) The MMTT application provides multi-modality support for CT, MR PET/CT and SPECT/CT scans, which includes support for SUV calculation of PET scans. OnQ Neuro supports only MR scans; and 5) While both applications perform measurements of the ROIs, the MMTT application allows for measurements that support oncology response criteria; OnQ Neuro does not specifically support
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| Image: cortechs logo | 510(k) Section/Document Number | 5 |
|---|---|---|
| Title | OnQ Neuro: 510(k) Summary | |
| Revision: 03 | Pages 5 of 6 | Date: 11/19/2021 |
oncology response criteria.
The CorTechs' OnQ Neuro and the identified Philips Medical Systems Multi-Modality Tumor Tracking (MMTT, K162955) are substantially equivalent in terms of indication for use, intended users, principle of operation and safety and/or effectiveness.
In conclusion, CorTechs believes that the proposed OnQ Neuro does not introduce any new potential safety and/or effectiveness issues and is substantially equivalent to the identified predicate device Multi-Modality Tumor Tracking (MMTT, K162955).
8. Performance and V&V Testing Summary
OnQ Neuro software application was tested in accordance with CorTechs verification and validation (V&V) processes. All product and engineering specifications were verified and validated. Software verification and validation testing were conducted, and documentation was provided as recommended by FDA's "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices".
Verification and Validation tests have been performed to address intended use, the technological characteristics claims, requirement specifications and the risk management results.
The V&V and performance data were provided in support of safety and effectiveness for the substantial equivalence determination.
Performance Testing Summary
Performance testing included protocols demonstrating accuracy of automated segmentation compared to manual radiologist segmentations. OnQ Neuro software performs automatic postacquisition analysis of anatomical and diffusion-weighted MRI and coregistration, fusion, and quantification of supported regions. OnQ Neuro automatic segmentation performance is evaluated by comparing the software-derived segmentations to expert-labeled segmentations of brain tumors, which are known/confirmed to be pathologically diagnosed cancer. Comparisons to expert segmentations are quantified using the Dice similarity coefficient (extent of softwarederived vs. ground truth overlap), and squared correlation coefficient (R2) of segmented region of interest volumes. Acceptance criteria are set such that OnQ Neuro v1.1 model performance is consistent (95% percent performance) with expert rater manual segmentation performance and meets minimum clinically acceptable levels. The results of the segmentation performance testing on an independent test dataset demonstrate that OnQ Neuro v1.1.0 segments brain tumor ROIs with an accuracy that passed the product's acceptance criteria. Further, segmentation performance is consistent across scanner manufacturers, field strengths, tumor types, and patient sexes. We conclude that OnQ Neuro v1.1.0 brain tumor segmentation is sufficiently accurate to be used in clinical practice in accordance with the OnQ Neuro v1.1.0 Indications for Use.
V&V Testing Summary
OnQ Neuro tumor segmentation performance is additionally tested using three separate testing methods: 1) objective unit testing comparing the software-derived values to the known ground truth values, 2) system testing to verify that the Tumor Segmentation RGB Overlay and Tumor
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| Image: cortechsol logo | 510(k) Section/Document Number | 5 |
|---|---|---|
| Title | OnQ Neuro: 510(k) Summary | |
| Revision: 03 | Pages 6 of 6 Date: 11/19/2021 |
Segmentation Report are correctly generated when compatible anatomical images are input to OnQ Neuro, and 3) clinical validation testing that the Tumor Segmentation RGB Overlay and Tumor Segmentation Report are correct, meet clinical expectations, and are safe and effective.
OnQ Neuro diffusion modeling performance is evaluated using three separate testing methods: 1) objective unit testing comparing the software-derived values using synthetic input data, with multiple levels of Rician noise, to the known ground truth values, 2) system testing to verify that the Restricted Signal Map and ADC map are correctly generated when compatible diffusion images are input to OnQ Neuro, and 3) clinical validation testing that the Restricted Signal Map and ADC map are correct, meet clinical expectations, and are safe and effective.
The test results in this 510(k) premarket notification demonstrate that the OnQ Neuro: 1) complies with the international and FDA-recognized consensus standards and FDA guidance documents, as listed on the CDRH Premarket Review Submission Cover Sheet Form, and 2) meets the acceptance criteria and is adequate for its intended use and specifications.
V&V activities required to establish performance and functionality of OnQ Neuro were performed. Testing performed demonstrated the OnQ Neuro meets all defined functionality requirements and performance claims.
9. Conclusions
The comparison between the two devices demonstrates that OnQ Neuro is as safe and as effective as its predicate, the Multi-Modality Tumor Tracking application (MMTT). Both OnQ Neuro and its predicate are substantially equivalent in regard to their general intended uses, intended users, and principle of operation. They have similar basic design, functions and technological characteristics. To address the technological differences the appropriate V&V and performance testing has been performed. The V&V testing demonstrates the safety and effectiveness of the device to meet its intended use and specifications. The performance testing shows that the device performs as well as gold standard - computer-aided expert manual segmentation.
By virtue of the physical characteristics and intended use, OnQ Neuro is substantially equivalent to its identified predicate device and is as safe and effective as its predicate device without raising any new safety and/or effectiveness concerns.