Validation study encompassing 349 subject datasets including healthy subjects, Alzheimer's disease patients, multiple sclerosis patients, traumatic brain injury patients, and depression patients.
—
Indications for Use
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.
Device Story
Software processes T1-weighted and FLAIR DICOM MR images; performs skull stripping, brain segmentation via probabilistic atlas and image intensity; calculates volumes of brain structures. Two pipelines: 'icobrain cross' (single timepoint volumes) and 'icobrain long' (longitudinal volume changes). Used by trained professionals in hospitals, imaging centers, or labs. Outputs electronic PDF/DICOM reports with normalized volumes (whole brain, grey matter) and unnormalized FLAIR white matter hyperintensities; segmentations overlaid on input images. Normalized volumes compared to healthy population statistical model. Assists clinicians in quantifying brain structure changes; aids in monitoring disease progression or treatment effects.
Clinical Evidence
Bench testing and validation study using 349 subject datasets (healthy, Alzheimer's, MS, TBI, depression). Accuracy validated against simulated/manual ground truth; reproducibility validated via test-retest datasets. Performance metrics: average Pearson correlation coefficient 0.90; average intraclass correlation coefficient 0.89. Verification testing confirmed system capabilities.
Technological Characteristics
Software-based image processing; DICOM compatible; operates on off-the-shelf hardware. Uses probabilistic atlas and image intensity for segmentation. Normalizes volumes for statistical comparison. No specific materials or energy sources; software-only device.
Indications for Use
Indicated for automatic labeling, visualization, and volumetric quantification of brain structures from MR images in patients including healthy subjects, Alzheimer's disease, multiple sclerosis, traumatic brain injury, and depression patients.
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).
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Image /page/0/Picture/1 description: The image shows the logo for the U.S. Department of Health & Human Services. The logo features a stylized representation of a human figure with three heads, symbolizing health, services, and people. The text "DEPARTMENT OF HEALTH & HUMAN SERVICES - USA" is arranged in a circular pattern around the figure.
Food and Drug Administration 10903 New Hampshire Avenue Document Control Center - WO66-G609 Silver Spring, MD 20993-0002
August 9, 2016
icometrix NV % Mr. Dirk Loeckx CEO Tervuursesteenweg 244 Leuven, B-3001 BELGIUM
Re: K161148
Trade/Device Name: icobrain Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: II Product Code: LLZ Dated: June 30, 2016 Received: June 30, 2016
Dear Mr. Loeckx:
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 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR 803); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820); and if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
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If you desire specific advice for your device on our labeling regulation (21 CFR Part 801), please contact the Division of Industry and Consumer Education at its toll-free number (800) 638 2041 or (301) 796-7100 or at its Internet address
http://www.fda.gov/MedicalDevices/Resourcesfor You/Industry/default.htm. 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.
You may obtain other general information on your responsibilities under the Act from the Division of Industry and Consumer Education at its toll-free number (800) 638-2041 or (301) 796-7100 or at its Internet address
http://www.fda.gov/MedicalDevices/ResourcesforYou/Industry/default.htm.
Sincerely yours.
Michael D'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) K161148
Device Name icobrain
Indications for Use (Describe)
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.
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."
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"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."
FORM FDA 3881 (8/14)
PSC Publishing Services (301) 443-6740 EF
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Image /page/3/Picture/0 description: The image shows the logo for Icometrix, a company specializing in imaging biomarker expertise. The logo features a stylized brain-shaped graphic in shades of blue, green, and red on the left. To the right of the graphic is the company name "icometrix" in a clean, sans-serif font, with the tagline "IMAGING BIOMARKER EXPERTS" in smaller letters underneath.
# Section 5. 510(k) Summary
icometrix NV - Kolonel Begauitlaan |b / 12 - 3012 Leuven - Belgium
info@icometrix.com - www.icometrix.com - T +32 16 369 000
IBAN: BE02 7360 2208 4540 - BlC KRED BE BB - BTV/
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Doc. III5605663.77 (/display/MSMET/Section+5.+510%28k%29+Summary), 2016-09-08 13:34 UTC
- 5.1 Submitter
- 5.2 Device
- • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Predicate Device
- Device Description
- Intended use
- Comparison of technological characteristics with the predicate device
- 5.7 Performance testing
#### 5. I Submitter
| Name: | icometrix NV |
|-------------------|---------------------------------------------------|
| Address: | Tervuursesteenweg 244<br>B-3001 Leuven<br>Belgium |
| Contact Person: | Dirk Loeckx |
| Telephone number: | +32 16 369 000 |
| Fax Number: | N.A. |
| E-mail: | dirk.loeckx@icometrix.com |
| Date Prepared: | 21 Jun 2016 |
#### 5.2 Device
| Device Trade Name: | <b>icobrain</b> |
|-----------------------|----------------------------------------|
| Common Name | Medical Image Processing Software |
| Classification Name | System, Image processing, Radiological |
| Number | 892.2050 |
| Product Code: | LLZ |
| Classification Panel: | Radiology |
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#### 5.3 Predicate Device
| Device | NeuroQuantTM |
|---------------|--------------------------------------------------------------------------------------|
| 510(k) Number | K061855 |
| Manufacturer | CorTechs Labs, Inc.<br>4690 Executive Drive, Suite 250<br>San Diego, CA 92121<br>USA |
#### 5.4 Device Description
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.
The following flowchart illustrates the overall architecture of icobrain.
Image /page/5/Figure/9 description: This image is a flowchart that shows the steps involved in image processing. The first step is to input images in DICOM format. The next steps are preprocessing, image processing, and output generation (report/images). The final step is to generate a final report in PDF or DICOM format, as well as output images in DICOM format.
As input, icobrain uses TI-weighted and fluid-attenuated inversion recovery (FLAIR) DICOM MR images from a single or from multiple time points. In case of multiple time points, i.e. multiple MRI scans from the same subject, for each time point one FLAIR and one TI image are used as input. During the pre-processing, the scan type (TI, FLAIR) is detected for every input image before it is converted from DICOM format to NIFTI format. The image processing then performs the actual segmentation and calculates the volumes of the brain structures. In case MRI scans subject on multiple time points are available, the changes in volume of the brain structures are calculated as well. Finally, the computed volumes and volume changes (in case of multiple time points) are summarized into an electronic report and (some) segmentations are overlaid on the input images.
The software displays the following volumetric measures:
- normalized volume and volume changes of the whole brain (sum of white and grey matter),
- normalized volume and volume changes of grey matter,
- unnormalized volume and volume changes of FLAIR white matter hyperintensities.
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Normalized whole brain and grey matter volumes are corrected for head size and are compared to a healthy population using a statistical model. The reported FLAIR white matter hyperintensities volumes are not normalized since they are not comparable to a reference population.
#### 5.5 Intended use
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.
#### 5.6 Comparison of technological characteristics with the predicate device
| Device | icobrain | NeuroQuantTM |
|----------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Regulation<br>Number | 21 CFR 892.2050 | 21 CFR 892.2050 |
| Device<br>Classification<br>Name | System, Image processing, Radiological | System, Image processing, Radiological |
| Product<br>Code | LLZ | LLZ |
| Regulatory<br>Class | II | II |
| Indications<br>for use | icobrain is intended for automatic labeling, visualization and<br>volumetric quantification of segmentable brain structures from<br>a set of MR images. This software is intended to automate the<br>current manual process of identifying, labeling and quantifying<br>the volume of segmentable brain structures identified on MR<br>images.<br><br>Icobrain consists of two distinct image processing pipelines:<br>icobrain cross and icobrain long.<br><br>icobrain cross is intended to provide volumes<br>from images acquired at a single timepoint icobrain long is intended to provide changes in volumes<br>between two images that were acquired on the same<br>scanner, with the same image acquisition protocol and<br>with same contrast at two different timepoints The results of icobrain cross cannot be compared with the<br>results of icobrain long. | NeuroQuantTM is intended for automatic labeling, visualization<br>and volumetric quantification of segmentable brain structures<br>from a set of MR images. This software is intended to automate<br>the current manual process of identifying, labeling and<br>quantifying the volume of segmental brain structures identified<br>on MR images. |
The device and predicate device (K061855) have an identical classification of:
Table I: Comparison with predicate device
Device and predicate device are software for automatically identifying and quantifying the volumes of brain structures, automatic labeling and visualization. Both devices take 3D MR images of the brain as input and generate an electronic report with similar quantitative information. The output volumes are for both devices compared to a normative dataset computed based on MRI data from normal control subjects.
icobrain and NeuroQuant 11 achieve their intended use based on a similar principle, since the quantification system relies on skull stripping (brain extraction), a brain segmentation based on a
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probabilistic atlas and image intensity information, and volume calculations of the segmented brain structures. Both devices normalize the volumes to allow the statistical comparison with a normative dataset.
Both devices are DICOM compatible and operate on off-the-shelf hardware.
icobrain is used by trained professionals in hospitals, imaging centers or in image processing labs.
NeuroQuant™ is used by physicians skilled in brain MR imaging.
#### 5.7 Performance testing
To demonstrate the performance of icobrain, the measured volume changes of the segmentable brain structures are validated for accuracy and reproducibility. The subjects upon whom the device was tested include healthy subjects, Alzheimer's disease patients, multiple sclerosis patients, traumatic brain injury patients, depression patients.
In the accuracy experiments, the volumes / volume changes are compared to simulated and/or manually labeled ground truth volume changes; in the reproducibility experiments, the volumes / volume changes are compared on test-retest image data sets. A literature review has been performed to set relevant acceptance criteria for each type of experiments passed the acceptance criteria.
The experiments encompassed 349 subject datasets in total. Averaged over all experiments, the Pearson correlation coefficient between the compared measurements was 0.90 and the intraclass correlation coefficient was 0.89.
Besides the validation experiments, verification tests demonstrate the system as a whole provides all the capabilities necessary to operate according to its intended use.
#### 5.8 Conclusions
The performance testing presented above establishes that the icobrain is safe and effective for its intended use. The comparison above demonstrates that the icobrain device is substantially equivalent to the predicate device.
| Declarations: | This summary includes only information that is also covered in the body of the 510(k). This summary does not contain any puffery or unsubstantiated labeling claims. This summary does not contain any raw data, i.e., contains only summary data. This summary does not contain any trade secret or confidential commercial information. This summary does not contain any patient identification information. |
|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
This document is reviewed and approved by Dirk Loeckx, CEO of icometrix, based on the present data and information.
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Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.