K241546 · Fovia, Inc. · LLZ · Feb 26, 2025 · Radiology
Device Facts
Record ID
K241546
Device Name
aiCockpit AI Viewer
Applicant
Fovia, Inc.
Product Code
LLZ · Radiology
Decision Date
Feb 26, 2025
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2050
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device
Indications for Use
Al Viewer is software for clinical review intended for use in General Radiology and other healthcare imaging applications. Al Viewer is intended to be used with off the shelf computing devices. Al Viewer supports major desktop browsers such as Microsoft Edge, Chrome, Safari. Al Viewer can display annotations and measurements as an overlay on images from DICOM objects, and from Al software. The viewer can perform 3D Multi-Planar Reformatting (MPR), 3D Maximum Intensity Projection (MIP) and 3D Volume Rendering (VR). Al Viewer is purposed to aid in reviewing findings through its ability to display clinical documents and reports side by side with the images.
Device Story
aiCockpit AI Viewer is a zero-footprint, web-based software as a medical device (SaMD) for clinical review of DICOM images. It operates on off-the-shelf computing devices via standard desktop browsers (Edge, Chrome, Safari). The viewer displays images alongside AI-generated findings, annotations, and measurements; it does not generate diagnoses. Clinicians use the interface to perform 2D and 3D image manipulations, including window/level, rotation, pan, and zoom. The device allows physicians to accept, reject, or edit AI-provided annotations and quantifiable measurements (e.g., lengths, volumes). By organizing images and reports side-by-side, the viewer aids radiologists in reviewing clinical findings, potentially improving workflow efficiency and diagnostic review accuracy.
Clinical Evidence
No clinical data. Evidence consists of bench-only software verification and validation testing, including full regression testing, bug fix verification, and end-to-end functional testing to ensure performance against design specifications.
Technological Characteristics
Zero-footprint, web-based JavaScript application. Operates on off-the-shelf hardware via standard desktop browsers (Edge, Chrome, Safari). Supports DICOM image display, 2D/3D image processing (MPR, MIP, VR), and overlay of AI-derived annotations/measurements. Software-based, non-AI generating viewer.
Indications for Use
Indicated for use by radiologists in General Radiology and healthcare imaging applications to review DICOM images, display AI-generated annotations/measurements, and perform 3D image manipulations (MPR, MIP, VR).
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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February 26, 2025
Fovia, Inc. Paula Brown Head of Regulatory P.O. Box 1858 Ross. California 94957
Re: K241546
Trade/Device Name: aiCockpit AI Viewer Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: LLZ Dated: January 24, 2025 Received: January 24, 2025
Dear Paula Brown:
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 (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.
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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).
Your device is also subject to, among other requirements, the Quality System (OS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100. Corrective and preventive action. Please note that regardless of whether a change requires premarket review. the OS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 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 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-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 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-device-advicecomprehensive-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-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatory
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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).
Sincerely,
Samul
for
Jessica Lamb, PhD Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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### Indications for Use
Submission Number (if known)
K241546
Device Name
aiCockpit Al Viewer
Indications for Use (Describe)
Al Viewer is software for clinical review intended for use in General Radiology and other healthcare imaging applications.
Al Viewer is intended to be used with off the shelf computing devices. Al Viewer supports major desktop browsers such as Microsoft Edge, Chrome, Safari.
Al Viewer can display annotations and measurements as an overlay on images from DICOM objects, and from Al software. The viewer can perform 3D Multi-Planar Reformatting (MPR), 3D Maximum Intensity Projection (MIP) and 3D Volume Rendering (VR). Al Viewer is purposed to aid in reviewing findings through its ability to display clinical documents and reports side by side with the images.
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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| 510(k) #: | K241546 |
|----------------|-------------------------|
| 510(k) Summary | Prepared on: 2025-01-24 |
Contact Details
| Applicant Name | Fovia, Inc. |
|-----------------------------|-------------------------------------------|
| Applicant Address | P.O. Box 1858 Ross CA 94957 United States |
| Applicant Contact Telephone | 415-328-3358 |
| Applicant Contact | Ms. Paula Brown |
| Applicant Contact Email | paula.brown@fovia.com |
Device Name
| Device Trade Name | aiCockpit Al Viewer |
|---------------------|------------------------------------------------|
| Common Name | Medical image management and processing system |
| Classification Name | System, Image Processing, Radiological |
| Regulation Number | 892.2050 |
| Product Code(s) | LLZ |
Legally Marketed Predicate Devices
| Predicate # | Predicate Trade Name (Primary Predicate is listed first) | Product Code |
|-------------|----------------------------------------------------------|--------------|
| K222476 | Ramsoft OmegaAl Image Viewer | LLZ |
## Device Description Summary
Al Viewer is a stand-alone software as medical device (Stand-alone SaMD) for clinicians able to use web browsers at client stations to view DICOM image data. It is intended to provide image and related information to render and interact with Al findings and annotations from Lung Nodule detection Al algorithms, but it does not directly generate any potential findings or diagnosis.
Al Viewer allows trained medical professionals to perform 2D, MPR & 3D image manipulations using the Adjustment Tools, including window level, rotate, flip, pan, stack scroll, and magnify. Al Viewer is also capable of organizing all the images and presenting them in a zero-footprint, web user interface, allowing the users to view images in their preferred layout.
Al Viewer allows trained medical professions with the Al Findings such as accept/reject/edit. Editing tools include selecting descriptive features for findings as well as allowing the physician to control editing quantifiable measurements first computed by AI such as lengths and volumes.
Al Viewer supports major desktop browsers such as Microsoft Edge, Chrome, Safari.
## Intended Use/Indications for Use
Al Viewer is software for clinical review intended for use in General Radiology and other healthcare imaging applications.
21 CFR 807.92(a)(5)
Al Viewer is intended to be used with off the shelf computing devices. Al Viewer supports major desktop browsers such as Microsoft Edge, Chrome, Safari.
Al Viewer can display annotations and measurements as an overlay on images from DICOM objects, and from Al software. The viewer
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can perform 3D Multi-Planar Reformatting (MPR), 3D Maximum Intensity Projection (MIP) and 3D Volume Rendering (VR). Al Viewer is purposed to aid in reviewing findings through its ability to display clinical documents and reports side by side with the images.
# Indications for Use Comparison
### 21 CFR 807.92(a)(5)
21 CFR 807.92(a)(6)
The indications for use are the same
## Technological Comparison
The Ramsoft OmegaAl Image Viewer is the predicate device for the Fovia Al Viewer. The two devices are substantially equivalent as they both provide the same functionality and possess the same technological characteristics.
From a high-level overview, both devices can "display annotations and measurements as an overlay on images from DICOM objects, and from Al software" (providing the same functionality) and both devices are based on the same architecture of "Server-based software solution that displays images and reports from a PACS using a zero-footprint application (browser)" (using the same technological characteristics).
From a more detailed level:
- 1. Both devices are intended for use in Radiology
- 2. Both devices are zero-footprint web-based JavaScript applications supporting common desktop browsers (e.g., Edge, Chrome, Safari)
- 3. Both devices display DICOM images
- 4. Both devices allow common image viewing tools such as Pan, Zoom, Window/Level, Rotate, & Scroll/Paqe
- 5. Both devices display annotations and measurement results from Al algorithms
- 6. Both devices allow the physician to control the editing and adjustment of the Al results (annotations and measurements)
- 7. Both devices provide flexible layout preferences
The only difference between the two devices is that Ramsoft OmegaAl Image Viewer provides more functionality than the Fovia Al Viewer - it could also be described as Fovia Al Viewer being a subset of the Ramsoft OmegaAl Image Viewer. For example:
1. Ramsoft OmegaAl Image Viewer supports images in a long list of -ology's (including radiology), while the Fovia Al Viewer is intended to be used only in radiology.
2. Ramsoft OmegaAl Image Viewer supports both Desktop Browser and Mobile environments while Fovia Al Viewer supports only Desktop Browser environments.
3. Ramsoft OmegaAl Image Viewer can be "used for image manipulation by radiology technologists (as well as radiologists for its other functionality)" while the Fovia Al Viewer is intended to be used by radiologists (for the same functionality at Ramsoft OmegaAl Image Viewer and not by radiology technologists at all).
#### Conclusion:
The comparison of the Subject Device with the predicate device shows that the aiCockpit Al Viewer software has similar functionality, intended use and technological characteristics as the predicate device. Based on the predicate and the performance testing conducted, the Subject Device is considered substantially equivalent to the predicate device.
#### Non-Clinical and/or Clinical Tests Summary & Conclusions 21 CFR 807.92(b)
Software Validation and Verification Testing Summary
The performance data provided in the form of test cases as well as verification test reports conducted on the Al Viewer include the following:
The verifications of features were performed by members of the product Al Viewer has been performed with the following steps
· Full regression tests have been executed on the build with the implementations of all features. The purpose is to uncover as much as possible all the potential bugs that could be detected by QA's test scripts.
· Bug fix verification for reported issues and regression test on related features
· End-to-end full tests have been executed on the candidate build for the release to verify all functionalities and fixes.
Not Applicable
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Software Verification and Validation Testing was conducted, and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" and "Content of Premarket Submission for Management of Cybersecurity in Medical Devices." In conclusion, the subject device is considered substantially equivalent to the predicate device
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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.
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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).
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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.
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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.
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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.
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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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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. Clicking a regulation takes you to the regulations tree.
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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.
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A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
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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.