Who is the manufacturer of cNeuro cMRI?

Combinostics OY filed the 510(k) submission for cNeuro cMRI (K171328).

What is the FDA product code for cNeuro cMRI?

LLZ. It is classified under 21 CFR 892.2050 as a Class 2 device in the Radiology panel.

What is the regulatory pathway for cNeuro cMRI?

510(k) clearance (submission type: Traditional).

What are the predicate devices for cNeuro cMRI?

NeuroQuant (K061855); icobrain (K161148).

Who can help prepare an FDA 510(k) submission like cNeuro cMRI?

Innolitics — a medical-device software consultancy — provides regulatory and engineering support for FDA 510(k) submissions. See https://innolitics.com/services/510ks/ or contact https://innolitics.com/contact.

cNeuro cMRI

K171328 · Combinostics OY · LLZ · Jan 8, 2018 · Radiology

Device Facts

Record ID	K171328
Device Name	cNeuro cMRI
Applicant	Combinostics OY
Product Code	LLZ · Radiology
Decision Date	Jan 8, 2018
Decision	SESE
Submission Type	Traditional
Regulation	21 CFR 892.2050
Device Class	Class 2
Attributes	Software as a Medical Device

Intended 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.

Device Story

cNeuro cMRI processes T1-weighted and optional FLAIR DICOM MR images; performs bias-field correction and brain extraction; utilizes multi-atlas segmentation to identify 133 brain structures; calculates volumetric biomarkers corrected for age, gender, and head size; compares results against a healthy reference population; generates gray matter concentration maps highlighting atypical regions; provides PDF reports for clinician review. Used in office settings by medical professionals to assist in clinical decision-making by providing quantitative volumetric data and visual overlays of brain structures. Benefits include automated, reproducible quantification of brain atrophy and white matter hyperintensities compared to manual methods.

Clinical Evidence

Bench testing only. Validation performed on 1,399 subjects including healthy controls and patients with Alzheimer's, mild cognitive impairment, fronto-temporal lobe degeneration, vascular dementia, and Multiple Sclerosis. Accuracy validated against manual ground truth; reproducibility validated via test-retest. Mean Dice index: 0.88 (hippocampus), 0.91 (thalamus), 0.88 (whole cortex). Mean intraclass correlation coefficient (ICC) for reproducibility: 0.96 across 133 structures. Correlation coefficient for FLAIR white matter hyperintensities: 0.97.

Technological Characteristics

Software-based medical image processing. Inputs: T1-weighted and FLAIR DICOM MR images. Processing: Bias-field correction, brain extraction, multi-atlas segmentation (133 structures). Output: Quantitative volumetric biomarkers, gray matter concentration maps, PDF reports. Deployment: Cloud-based. Statistical model compares normalized volumes to healthy reference population.

Indications for Use

Indicated for automatic labeling, quantification, and visualization of segmentable brain structures from MR images in patients, including those with neurodegenerative diseases (e.g., Alzheimer's, mild cognitive impairment, fronto-temporal lobe degeneration, vascular dementia) and Multiple Sclerosis. Intended for use by trained medical professionals in office environments.

Regulatory Classification

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.

Special Controls

*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).

Predicate Devices

NeuroQuant (K061855)
icobrain (K161148)

Related Devices

K170981 — NeuroQuant · Cortechs Labs, Inc. · Sep 7, 2017
K193287 — CorInsights MRI · Adm Diagnostics, Inc. · Nov 20, 2020
K161148 — icobrain · Icometrix · Aug 9, 2016
K232305 — AI-Rad Companion Brain MR · Siemens Medical Solutions U.S.A. · Oct 23, 2023
K220437 — Neurophet AQUA · Neurophet., Inc. · May 10, 2023

Submission Summary (Full Text)

{0}------------------------------------------------ Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health and Human Services logo. To the right of that is the FDA logo, with the letters "FDA" in a blue box, followed by the words "U.S. FOOD & DRUG" in a larger, bold blue font. Below that is the word "ADMINISTRATION" in a smaller, blue font. 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); {1}------------------------------------------------ # 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 {2}------------------------------------------------ #### 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) K171328 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) ### CONTINUE ON A SEPARATE PAGE IF NEEDED. This section applies only to requirements of the Paperwork Reduction Act of 1995. #### *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW!" The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to: > Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov "An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number." {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 | {4}------------------------------------------------ ## 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. {5}------------------------------------------------ ### 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. {6}------------------------------------------------ | | 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. {7}------------------------------------------------ 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. {8}------------------------------------------------ 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.