(248 days)
Not Found
Unknown
The summary describes automated segmentation and quantification using image processing and statistical models, but does not explicitly mention AI or ML. While these tasks can be performed using AI/ML, the description is not specific enough to confirm its use.
No
The device is intended for automatic labeling, quantification, and segmentation of brain structures from MR images, providing computed biomarkers. It processes images and presents information to medical professionals for assessment, but it does not directly treat or diagnose a disease or condition.
Yes
The device quantifies segmentable brain structures and compares computed biomarkers (volumes of structures and FLAIR white matter hyperintensities) to a healthy reference population using a statistical model. This information is used by medical professionals, suggesting its role in assessing a patient's condition relative to a normal state, which is a diagnostic function.
Yes
The device description explicitly states "cNeuro cMRI is intended for automatic labeling, quantification of segmentable brain structures from a set of MR images. The software is intended to automate the current manual process..." and describes its function as image processing and analysis, taking DICOM files as input and providing computed biomarkers as output. There is no mention of any accompanying hardware component that is part of the device itself.
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.
- Device Function: The cNeuro cMRI device processes medical images (MR images) taken from the body, not samples from the body. It analyzes the structure and volume of the brain based on these images.
- Intended Use: The intended use is for automatic labeling and quantification of brain structures from MR images, automating a manual process performed on imaging data.
- Input: The input is DICOM MR images, which are image files, not biological samples.
Therefore, cNeuro cMRI falls under the category of medical imaging software or a medical image analysis device, not an In Vitro Diagnostic device.
N/A
Intended Use / Indications for Use
cNeuro cMRI is intended for automatic labeling, quantification of segmentable brain structures from a set of MR images. The software is intended to automate the current manual process of identifying, labeling and quantifying the segmentable brain structures identified on MR images.
The intended user profile covers medical professionals who work with medical imaging. The intended operational environment is an office-like environment with a computer.
Product codes
LLZ
Device Description
cNeuro cMRI is intended for automatic labeling, quantification of segmentable brain structures from a set of MR images. The software is intended to automate the current manual process of identifying, labeling and quantifying the segmentable brain structures identified on MR images.
As input, cNeuro cMRI uses T1-weighted (T1) and fluid-attenuated inversion recovery (FLAIR) DICOM MR images from a single time point. The T1 image is mandatory but the FLAIR image is optional. The user selects images through connection with a Picture Archiving and Communication System (PACS) or by selecting DICOM files from a folder. cNeuro cMRI displays the selected images together with information extracted from the DICOM headers.
lmage processing starts with a pre-processing stage with bias-field correction and brain extraction before the actual segmentation and calculation of MRI biomarkers begins. When the processing has completed, the user can review the images with brain segmentations displayed as an overlay. cNeuro cMRI presents computed biomarkers corresponding to volumes of structures and FLAIR white matter hyperintensities. The computed biomarkers are corrected for the subject's head size, gender and age and are compared to corresponding biomarkers from a healthy reference population using a statistical model.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Not Found
Input Imaging Modality
T1-weighted (T1) and fluid-attenuated inversion recovery (FLAIR) DICOM MR images
Anatomical Site
Brain
Indicated Patient Age Range
Not Found
Intended User / Care Setting
medical professionals who work with medical imaging. The intended operational environment is an office-like environment with a computer.
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
Test data included data from healthy subjects, and patients with neurodegenerative diseases such as Alzheimer's disease, mild cognitive impairment, fronto-temporal lobe degeneration, vascular dementia as well as Multiple Sclerosis patients. In the accuracy experiments, cNeuro cMRI fully automated brain segmentation was compared to manually labeled ground truth data. In the reproducibility experiments, the volumes were compared using test-retest data. The experiments included data from 1399 subjects in total.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Support for the substantial equivalence of cNeuro cMRI to the predicate devices was provided as a result of risk management and testing. The design verification activities consist of code review and static code analysis, unit tests, integration tests, system tests (including safety related tests from risk analysis) and regression testing after modifications.
To demonstrate the performance of cNeuro cMRI, the computed volumes of brains structures were validated for accuracy and reproducibility. Test data included data from healthy subjects, and patients with neurodegenerative diseases such as Alzheimer's disease, mild cognitive impairment, fronto-temporal lobe degeneration, vascular dementia as well as Multiple Sclerosis patients. In the accuracy experiments, cNeuro cMRI fully automated brain segmentation was compared to manually labeled ground truth data. In the reproducibility experiments, the volumes were compared using test-retest data. The experiments included data from 1399 subjects in total.
A literature review was performed to set relevant acceptance criteria for each type of experiment. All experiments passed the acceptance criteria. Averaged over all experiments, the similarity index (or Dice index) were 0.88 for the hippocampus, 0.91 for the thalamus and 0.88 for the whole cortex. Furthermore, intraclass correlation coefficient for the test-retest reproducibility measurements averaged over all 133 structures was 0.96 and the correlation coefficient between the computed FLAIR white matter hyperintensities and the manually labelled data was 0.97.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Similarity index (or Dice index): 0.88 for the hippocampus, 0.91 for the thalamus and 0.88 for the whole cortex.
Intraclass correlation coefficient for test-retest reproducibility: 0.96 (averaged over all 133 structures).
Correlation coefficient between computed FLAIR white matter hyperintensities and manually labelled data: 0.97.
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).
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January 8, 2018
Combinostics Oy % Lennart Thurfjell CEO Hatanpään valtatie 24 Tampere FI 33100 FINLAND
Re: K171328
Trade/Device Name: cNeuro cMRI Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: II Product Code: LLZ Dated: December 6, 2017 Received: December 8, 2017
Dear Lennart Thurfjell:
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. 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 801); medical device reporting of medical device-related adverse events) (21 CFR 803); good manufacturing practice requirements as set forth in the quality systems (OS) regulation (21 CFR Part 820);
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Page 2 - Lennart Thurfjell
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 http://www.fda.gov/MedicalDevices/Safety/ReportaProblem/default.htm for the CDRH's Office of Surveillance and Biometrics/Division of Postmarket Surveillance.
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/DeviceRegulationandGuidance/) and CDRH Learn (http://www.fda.gov/Training/CDRHLearn). 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 (http://www.fda.gov/DICE) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Michael D. O'Hara For
Robert Ochs. Ph.D Director Division of Radiological Health Office of In Vitro Diagnostics and Radiological Health Center for Devices and Radiological Health
Enclosure
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DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration
Indications for Use
Form Approved: OMB No. 0910-0120 Expiration Date: January 31, 2017 See PRA Statement below.
510(k) Number (if known)
Device Name cNeuro cMRI
Indications for Use (Describe)
cNeuro cMRI is intended for automatic labeling, quantification of segmentable brain structures from a set of MR images. The software is intended to automate the current manual process of identifying, labeling and quantifying the segmentable brain structures identified on MR images.
The intended user profile covers medical professionals who work with medical imaging. The intended operational environment is an office-like environment with a computer.
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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3
Image /page/3/Picture/1 description: The image shows the word "COMBINOSTICS" in all capital letters. To the left of the word is a symbol that looks like a "C" with a blue square on top and a red square on the bottom. The word is written in a simple, sans-serif font and is black.
Combinostics Oy Hatanpään valtatie 24 33 100 Tampere Finland
Section 5. 510(k) Summary
5.1 Submitter
| Name: | Combinostics OY |
|---|---|
| Address: | Hatanpään valtatie 24, FI 33 100 Tampere, |
| Finland | |
| Contact Person: | Lennart Thurfjell |
| Telephone number: | +46 730 699057 |
| Fax Number: | N.A. |
| E-mail: | lennart.thurfjell@combinostics.com |
| Consultant: | Allison Komiyama, PhD, RAC |
| Date prepared: | January 2, 2018 |
5.2 Device
| Trade Name: | cNeuro cMRI |
|---|---|
| Common Name | Medical Image Processing Software |
| Classification Name | System, Image processing, Radiological |
| Regulation Number | 892.2050 |
| Product Code | LLZ |
| Classification Panel | Radiology |
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5.3 Predicate Device
| Primary predicate device | |
|---|---|
| Device | NeuroQuant |
| 510(k) # | K061855 |
| Manufacturer | CorTechs Labs, Inc. 4690 Executive Drive, Suite |
| 250 San Diego, CA 92121 USA | |
| Secondary predicate device | |
| Device | icobrain |
| 510(k) # | K161148 |
| Manufacturer | Icometrix, Kolonel Begaultlaan 1b / 12 |
| 3012 Leuven, Belgium |
5.4 Device Description
cNeuro cMRI is intended for automatic labeling, quantification of segmentable brain structures from a set of MR images. The software is intended to automate the current manual process of identifying, labeling and quantifying the segmentable brain structures identified on MR images.
The flowchart below outlines the workflow and main steps in the usage of cNeuro cMRI.
Image /page/4/Figure/6 description: The image shows a flowchart of a process. The process starts with selecting DICOM images, followed by pre-processing. The next step is atlas-based segmentation, then QC of segmentation results. The process ends with reviewing biomarkers and creating a report in PDF format.
As input, cNeuro cMRI uses T1-weighted (T1) and fluid-attenuated inversion recovery (FLAIR) DICOM MR images from a single time point. The T1 image is mandatory but the FLAIR image is optional. The user selects images through connection with a Picture Archiving and Communication System (PACS) or by selecting DICOM files from a folder. cNeuro cMRI displays the selected images together with information extracted from the DICOM headers.
lmage processing starts with a pre-processing stage with bias-field correction and brain extraction before the actual segmentation and calculation of MRI biomarkers begins. When the processing has completed, the user can review the images with brain segmentations displayed as an overlay. cNeuro cMRI presents computed biomarkers corresponding to volumes of structures and FLAIR white matter hyperintensities. The computed biomarkers are corrected for the subject's head size, gender and age and are compared to corresponding biomarkers from a healthy reference population using a statistical model.
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5.5 Indications for Use
cNeuro cMRI is intended for automatic labeling, quantification of segmentable brain structures from a set of MR images. The software is intended to automate the current manual process of identifying, labeling and quantifying the segmentable brain structures identified on MR images.
The intended user profile covers medical professionals who work with medical imaging. The intended operational environment is an office-like environment with a computer.
5.6 Comparison and substantial equivalence statement
cNeuro cMRI is substantially equivalent to the NeuroQuant device by Cortechs Labs, cleared in K061855 with regards to processing of T1 images and it is substantially equivalent to the icobrain device by icometrix, cleared in K161148 with regards to processing of FLAIR images. K061855 is the primary predicate and K161148 is the secondary predicate.
| | SUBJECT DEVICE | Primary Predicate
Device | Secondary Predicate
Device | Conclusion/
Comparison |
|----------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------------------------------------------------------------------|
| Device | cNeuro cMRI | NeuroQuant | icobrain | --- |
| 510(k) Number | K171328 | K061855 | K161148 | |
| Manufacturer | Combinostics OY | CorTechs Labs, Inc | icometrix | --- |
| Device
Classification
Name | Picture archiving and
communication system | Picture archiving and
communication system | Picture archiving and
communication system | Identical |
| Deployment | Cloud based | Cloud based or installed | Cloud based | cNeuro cMRI and icobrain are
identical. NeuroQuant is
available either cloud based
or installed. |
A comparison of the subject device and predicate devices (K061855 and K161148) is provided below.
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| | SUBJECT DEVICE | Primary Predicate
Device | Secondary Predicate
Device | Conclusion/
Comparison |
|------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Device | cNeuro cMRI | NeuroQuant | icobrain | -- |
| 510(k) Number | K171328 | K061855 | K161148 | -- |
| Manufacturer | Combinostics OY | CorTechs Labs, Inc | icometrix | -- |
| Indications for
Use | cNeuro cMRI is
intended for automatic
labeling, quantification
and visualization of
segmentable brain
structures from a set of
MR images. The
software is intended to
automate the current
manual process of
identifying, labeling and
quantifying
segmentable brain
structures identified on
MR images.
The users are trained
healthcare
professionals who work
with medical imaging.
The product is used in
an office-like
environment. | NeuroQuantTM is
intended for automatic
labeling, visualization and
volumetric quantification
of segmentable brain
structures from a set of
MR images. This software
is intended to automate
the current manual
process of identifying,
labeling and quantifying
the volume of
segmentable brain
structures identified on
MR images. | icobrain is intended for
automatic labeling,
visualization and volumetric
quantification of segmentable
brain structures from a set of
MR images. This software is
intended to automate the
current manual process of
identifying, labeling and
quantifying the volume of
segmentable brain structures
identified on MR images.
icobrain consists of two
distinct image processing
pipelines: icobrain cross and
icobrain long. icobrain cross
is intended to provide
volumes from images
acquired at a single timepoint
icobrain long is intended to
provide changes in volumes
between two images that
were acquired on the same
scanner, with the same image
acquisition protocol and with
same contrast at two different
timepoints The results of
icobrain cross cannot be
compared with the results of
icobrain long. | Functionally identical.
cNeuro cMRI lists
"quantification" while
NeuroQuant lists "volumetric
quantification". Since cNeuro
cMRI makes a volumetric
quantification, these
expressions can be considered
identical.
Furthermore, icobrain has the
added description of two
distinct processing pipelines:
icobrain cross and icobrain
long. Results of icobrain cross
cannot be compared with the
results of icobrain long.
cNeuro cMRI and NeuroQuant
provides only one processing
pipeline.
Finally, cNeuro cMRI lists
intended users and intended
use environment whereas
NeuroQuant and icobrain
does not. |
Subject device and predicate devices are software for automatically identifying and quantifying volumes of brain structures, labeling and visualization. Both subject and predicate devices take 3D MR images of the brain as input and generate an electronic report with similar quantitative information. The output values are for all devices compared to a normative data based on MRI data from healthy control subjects.
Subject device and the primary predicate device segments cortical structures from MRI T1 images based on a similar principle, where the quantification relies on pre-processing with skull stripping (brain extraction) followed by multi-atlas segmentation. The main difference is that different atlases are used. The subject device uses atlases with 133 brain structures, while the primary predicate uses atlases with 34 brain structures. Similarly, the subject device and the secondary predicate device segments white matter hyperintensities from FLAIR MR images based on a similar principle. Furthermore, for volumes derived from T1 images, the subject device and the predicate devices provide statistical comparison of normalized values with a normative dataset from a healthy reference population. In addition, subject device compares normalized volumes of FLAR white matter hyperintensities to a normative dataset from a reference population. The secondary predicate device does not provide such a comparison.
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The primary predicate device uses an index computed based on the image volumes from normal atlas space as a quality control measure. The subject device does not employ such an index, but provides functionality where the user interactively can review the quality of the segmentations by checking color coded overlays on the original MR slices.
Subject device provides a gray matter concentration map, i.e., an overlay on MR T1 images highlighting regions where the local gray matter concentration of the patient is smaller than the gray matter concentration in the reference population normalized for age, sex and head size. This overlay, which can be toggled on and off, provides a means for the user to locate regions that are atypical compared to the reference population. The predicate devices do not provide such a gray matter concentration map. Subject device's gray matter concentration map provides a visual complement to the quantitative volume measurements and it does not affect safety and effectiveness of the device.
5.7 Performance testing
Support for the substantial equivalence of cNeuro cMRI to the predicate devices was provided as a result of risk management and testing. The design verification activities consist of code review and static code analysis, unit tests, integration tests, system tests (including safety related tests from risk analysis) and regression testing after modifications
To demonstrate the performance of cNeuro cMRI, the computed volumes of brains structures were validated for accuracy and reproducibility. Test data included data from healthy subjects, and patients with neurodegenerative diseases such as Alzheimer's disease, mild cognitive impairment, fronto-temporal lobe degeneration, vascular dementia as well as Multiple Sclerosis patients. In the accuracy experiments, cNeuro cMRI fully automated brain segmentation was compared to manually labeled ground truth data. In the reproducibility experiments, the volumes were compared using test-retest data. The experiments included data from 1399 subjects in total.
A literature review was performed to set relevant acceptance criteria for each type of experiment. All experiments passed the acceptance criteria. Averaged over all experiments, the similarity index (or Dice index) were 0.88 for the hippocampus, 0.91 for the thalamus and 0.88 for the whole cortex. Furthermore, intraclass correlation coefficient for the test-retest reproducibility measurements averaged over all 133 structures was 0.96 and the correlation coefficient between the computed FLAIR white matter hyperintensities and the manually labelled data was 0.97.
The verification and performance testing demonstrate that cNeuro cMRI is safe and effective to use.
5.8 Conclusion
Combinostics OY believes that cNeuro cMRI has the identical indication for use and that there are no new types of questions regarding safety and effectiveness for cNeuro cMRI as compared to the cleared predicate devices. Combinostics OY has conducted the risk analysis and performed the necessary verification and validation activities to demonstrate that the design outputs meet the design inputs and the applicable process standards. Combinostics OY has concluded that the performance data for the cNeuro cMRI shows that it is substantially equivalent to the primary predicate device, NeuroQuant (K061855), for processing of MRI T1 images and to the secondary predicate device, icobrain (K161148), for processing of MRI FLAIR images.
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This document is reviewed and approved by Lennart Thurfjell, CEO of Combinostics.
Image /page/8/Picture/2 description: The image shows a DocuSign signature block. The block includes the phrase "DocuSigned by" followed by a signature. The signer's name is Lennart Thurfjell, and the signing reason is "I approve this document." The signing time is 2018-01-02 at 21:10 CET, and the document ID is D90D9E2D88AF4E038888D92E1C8BDDFA.