K252565 · Clouds of Care · OLX · Feb 13, 2026 · Neurology
Device Facts
Record ID
K252565
Device Name
PreOp v3
Applicant
Clouds of Care
Product Code
OLX · Neurology
Decision Date
Feb 13, 2026
Decision
SESE
Submission Type
Special
Regulation
21 CFR 882.1400
Device Class
Class 2
Attributes
Software as a Medical Device, Pediatric
Indications for Use
PreOp V3 is intended for use by a trained/qualified EEG technologist or physician on both adult and pediatric subjects at least 3 years of age for the visualization of human brain function by fusing a variety of EEG information with rendered images of an individualized head model and an individualized MRI image.
Device Story
PreOp V3 is medical device software for 3D Electrical Source Imaging (ESI) of brain activity; inputs include EEG data and MRI images provided via Persyst software; device processes data using sLORETA source estimation and Finite Difference Model (FDM) head modeling; outputs include 3D visualizations and clinical reports; used in clinical settings by EEG technologists or physicians; integrated into Persyst EEG Review and Analysis interface; assists clinicians in evaluating brain function; benefits include improved workflow efficiency through automated preprocessing, spike detection, MRI segmentation, and clustering; modernized containerized microservices architecture improves scalability and performance.
Clinical Evidence
No clinical data. Substantial equivalence established via non-clinical software verification and validation testing conducted in accordance with IEC 62304:2006 + A1:2015.
Technological Characteristics
SaaS product hosted on AWS infrastructure. Core technologies: sLORETA source estimation, Finite Difference Model (FDM) for head modeling. Software architecture: containerized microservices. Features: automated EEG preprocessing, spike detection, MRI segmentation, and clustering. Connectivity: integrated with Persyst EEG Review and Analysis software. Standards: IEC 62304:2006 + A1:2015.
Indications for Use
Indicated for adult and pediatric subjects at least 3 years of age requiring visualization of human brain function via EEG and MRI fusion.
Regulatory Classification
Identification
An electroencephalograph is a device used to measure and record the electrical activity of the patient's brain obtained by placing two or more electrodes on the head.
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FDA U.S. FOOD & DRUG ADMINISTRATION
February 13, 2026
Clouds of Care
Gregor Strobbe
CEO
Kliniekstraat 27a
Ghent, 9050
Belgium
Re: K252565
Trade/Device Name: PreOp v3
Regulation Number: 21 CFR 882.1400
Regulation Name: Electroencephalograph
Regulatory Class: Class II
Product Code: OLX
Dated: January 14, 2026
Received: January 14, 2026
Dear Gregor Strobbe:
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 (the 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 available 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.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
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K252565 - Gregor Strobbe
Page 2
Your device is also subject to, among other requirements, the Quality Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13484 clause 8.3 (Nonconforming product), and ISO 13485 clause 8.5 (Corrective and preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
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 Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the Quality Management System Regulation (QMSR) (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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-devices/medical-device-safety/medical-device-reporting-mdr-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/medical-devices/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-devices/device-advice-comprehensive-regulatory-assistance/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).
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K252565 - Gregor Strobbe
Page 3
Sincerely,
Patrick Antkowiak -S
for
Jay Gupta
Assistant Director
DHT5A: Division of Neurosurgical,
Neurointerventional, and
Neurodiagnostic Devices
OHT5: Office of Neurological and
Physical Medicine Devices
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
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FORM FDA 3881 (6/20)
Page 1 of 1
PSC Publishing Services (301) 443-6740
EF
| DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration Indications for Use | Form Approved: OMB No. 0910-0120 Expiration Date: 06/30/2023 See PRA Statement below. |
| --- | --- |
| 510(k) Number (if known) K252565 | |
| Device Name PreOp V3 | |
| Indications for Use (Describe) PreOp V3 is intended for use by a trained/qualified EEG technologist or physician on both adult and pediatric subjects at least 3 years of age for the visualization of human brain function by fusing a variety of EEG information with rendered images of an individualized head model and an individualized MRI image. | |
| 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." | |
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Page 1 of 3
# 510(k) Summary
This 510(k) summary of safety and effectiveness information is prepared in accordance with 21 CFR 807.92.
# 1 General information
| Date prepared | 13 August 2025 | |
| --- | --- | --- |
| Company | Clouds of Care
Kliniekstraat 27a
Gent 9050 BELGIUM | |
| Primary Contact Person | Nick de la Croix
Head of Compliance
Email: nick.delacroix@cloudsofcare.com
Phone: +32 494 23 81 53 | |
| Device | Trade Name | PreOp V3 |
| | Common Name | Electroencephalograph software |
| | Classification Name (Regulation) | Source Localization Software for Electroencephalography or magnetoencephalography |
| | Classification Number | 882.1400 |
| | Classification Panel | Neurology |
| | Device Class | Class II |
| | Primary Product Code | OLX |
| Predicate Device | Trade Name | PreOp |
| | Common Name | Electroencephalograph software |
| | Company | Epilog |
| | 510(k) clearance | K172858 |
| | Classification Name (Regulation) | Source Localization Software for Electroencephalography or magnetoencephalography |
| | Classification Number | 882.1400 |
| | Classification Panel | Neurology |
| | Device Class | Class II |
| | Product Code | OLX |
| Device description | PreOp is medical device software that combines EEG data and MRI images to visualize recorded EEG activity in 3D in the brain. PreOp can be subdivided in 3 main modules: 3D Electrical Source Imaging (i.e. 3D ESI), Report generation and Viewer generation. The device's input is the MRI and EEG data that are provided by means of Persyst software. The output of the device is a report containing the results of the visualization and the ability to evaluate the results in 3D using the 3D viewer. The user can access the output through Persyst software. | |
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# 2 Technological characteristics
# 2.1 Technological similarities between subject PreOp and the predicate PreOp V1
The following table provides a detailed overview of the technological similarities between the subject PreOp and the predicate PreOp.
| Features | Subject PreOp V3 | Predicate PreOp K172858 |
| --- | --- | --- |
| Indications for Use | PreOp V3 is intended for use by a trained/qualified EEG technologist or physician on both adult and pediatric subjects at least 3 years of age for the visualization of human brain function by fusing a variety of EEG information with rendered images of an individualized head model and an individualized MRI image. | PreOp is intended for use by a trained/qualified EEG technologist or physician on both adult and pediatric subjects at least 3 years of age for the visualization of human brain function by fusing a variety of EEG information with rendered images of an individualized head model and an individualized MRI image. |
| Software only product | Yes | Yes |
| Computer OS | SaaS product in AWS infrastructure | SaaS product in AWS infrastructure |
| Source estimation methods | sLORETA | sLORETA |
| Forward head modeling | Finite difference Model (FDM) | Finite difference Model (FDM) |
| Prescription / over the counter | Prescription | Prescription |
# 2.2 Technological differences between subject PreOp and the predicate PreOp V1
The subject device, PreOp V3, incorporates technological enhancements compared to the predicate device PreOp V1 (K172858) while maintaining the same intended use, patient population, and core functional principles. PreOp V3 includes integration with the Persyst EEG Review and Analysis Software, allowing clinical users to access PreOp functionality directly within the Persyst interface, whereas PreOp V1 required operators to access the system through a separate cloud-based workflow. PreOp V3 also standardizes the anatomical workflow by supporting both an idealized (average) MRI model and individualized, patient-specific MRI data, whereas PreOp V1 supported only individualized MRI. PreOp V3 automates multiple functions that were previously performed manually in the
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predicate device, including EEG data preprocessing, spike detection, MRI segmentation, and clustering, thereby improving workflow efficiency without altering user control of final review steps. The spike clustering algorithms in PreOp V3 is enhanced to incorporate morphology and topography-based features, supporting improved precision of spike classification.
PreOp V3 also introduces a modernized, modular, containerized software architecture that replaces the monolithic design of PreOp V1. This updated architecture uses cloud-native microservices, enabling improved scalability, maintainability, and system performance, particularly when handling larger datasets. Additionally, PreOp V3 includes 3D visualization capabilities for head models and electrode positioning, improved temporal and spatial accuracy of source estimation, and refined reporting features with automated quality control checks.
These updates represent workflow, architectural, and performance improvements but do not change the intended use, fundamental technology, or overall operational principles of the device.
# 2.3 Summary of non-clinical performance data
# 2.3.1 Software Verification and Validation Testing
Software verification and validation testing were conducted and documentation was provided as recommended by FDA's 2023 guidance, "Content of Premarket Submissions for Device Software Functions."
PreOp was tested in accordance with IEC 62304:2006 + A1:2015: Medical device software – Software life-cycle processes (FDA Recognition Number: 13-79).
All required verification and validation activities have been completed to support the safety and performance of the software.
# 2.3.2 Summary of Clinical Performance Data
Not applicable. Clinical studies are not necessary to establish the substantial equivalence of PreOp.
# 3 CONCLUSION
The information discussed above and provided in the 510(k) submission demonstrate that PreOp V3 is substantially equivalent to the predicate.
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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
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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.
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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.
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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.