K243933 · Ceevra, Inc. · QIH · Mar 4, 2025 · Radiology
Device Facts
Record ID
K243933
Device Name
Ceevra Reveal 3+
Applicant
Ceevra, Inc.
Product Code
QIH · Radiology
Decision Date
Mar 4, 2025
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2050
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Prostate segmentation
—
—
0.90 DSC
133 imaging studies
>1 (medical professionals)
—
—
Bladder segmentation
—
—
0.93 DSC
133 imaging studies
>1 (medical professionals)
—
—
Neurovascular bundle segmentation
—
—
6.6 mm HD-95
133 imaging studies
>1 (medical professionals)
—
—
Kidney segmentation
—
—
0.92 DSC (CT), 0.89 DSC (MR)
133 imaging studies
>1 (medical professionals)
—
—
Artery segmentation
—
—
0.90 DSC (CT), 0.87 DSC (MR)
133 imaging studies
>1 (medical professionals)
—
—
Vein segmentation
—
—
0.88 DSC (CT), 0.82 DSC (MR)
133 imaging studies
>1 (medical professionals)
—
—
Pulmonary artery segmentation
—
—
0.82 DSC
133 imaging studies
>1 (medical professionals)
—
—
Pulmonary vein segmentation
—
—
0.83 DSC
133 imaging studies
>1 (medical professionals)
—
—
Airway segmentation
—
—
0.82 DSC
133 imaging studies
>1 (medical professionals)
—
—
Bronchopulmonary segment segmentation
—
—
0.86 DSC
133 imaging studies
>1 (medical professionals)
—
—
Indications for Use
Ceevra Reveal 3+ is intended as a medical imaging system that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from CT or MR imaging devices and that such processing may include the generation of preliminary segmentations of normal anatomy using software that employs machine learning and other computer vision algorithms. It is also intended as software for preoperative surgical planning, and as software for the intraoperative display of the aforementioned multi-dimensional digital images. Ceevra Reveal 3+ is designed for use by health care professionals and is intended to assist the clinician who is responsible for making all final patient management decisions.
Device Story
Software as a medical device (SaMD) processing CT/MR images to generate 3D anatomical models; used by clinicians for preoperative planning and intraoperative display. Input: patient CT/MR DICOM images. Processing: internal operators generate 3D segmentations using machine learning and computer vision algorithms; clinicians interact with models via mobile/desktop viewers or VR headsets (rotate, zoom, pan, measure). Output: 3D visualizations and anatomical measurements (volume, diameter, distance). Used in hospitals/clinics by healthcare professionals to assist in surgical planning and intraoperative visualization; clinician retains final decision-making authority. Benefits: provides enhanced anatomical visualization to support surgical decision-making.
Software-based medical imaging system; runs on mobile devices, computers, and VR headsets. Uses machine learning and computer vision algorithms for anatomical segmentation. Connectivity: DICOM-based image processing. Standards: IEC 62304:2006/Amd 1:2015 (software lifecycle). No hardware components; standalone software.
Indications for Use
Indicated for adult patients (22 and over) for medical imaging processing, review, analysis, and communication of CT/MR images, including preliminary segmentation of normal anatomy, preoperative surgical planning, and intraoperative display. Machine learning algorithms are restricted to adult patients (22+); patients under 22 or of unknown age are processed without machine learning.
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).
{0}------------------------------------------------
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo features the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text.
Ceevra, Inc. Ken Koster CTO 149 New Montgomery St. 4th Floor San Francisco, California 94105
March 4, 2025
Re: K243933
Trade/Device Name: Ceevra Reveal 3+ Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: OIH Dated: January 10, 2025 Received: January 10, 2025
Dear Ken Koster:
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.
{1}------------------------------------------------
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 (QS) 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 QS 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 medical devices and radiation-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
{2}------------------------------------------------
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,
Jessica Lamb
Jessica Lamb, Ph.D. Assistant Director 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
{3}------------------------------------------------
## Indications for Use
Submission Number (if known)
K243933
Device Name
Ceevra Reveal 3+
Indications for Use (Describe)
Ceevra Reveal 3+ is intended as a medical imaging system that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from CT or MR imaging devices and that such processing may include the generation of preliminary segmentations of normal anatomy using software that employs machine learning and other computer vision algorithms. It is also intended as software for preoperative surgical planning, and as software for the intraoperative display of the aforementioned multi-dimensional digital images. Ceevra Reveal 3+ is designed for use by health care professionals and is intended to assist the clinician who is responsible for making all final patient management decisions.
The machine learning algorithms in use by Ceevra Reveal 3+ are for use only for adult patients (22 and over). Three-dimensional images for patients under the age of 22 or of unknown age will be generated without the use of any machine learning algorithms.
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."
{4}------------------------------------------------
Image /page/4/Picture/0 description: The image contains the text "K243933" at the top, followed by a logo of a blue, curved design that resembles a wave or a stylized letter "C". Below the logo, the text "CEEVRA" is displayed in a sans-serif font, with the letters in a gradient from blue to light blue. The overall impression is that of a company logo or identifier.
# 510(k) Summary
#### General Information 1.
| 510(k) Sponsor | Ceevra, Inc. |
|-----------------------|------------------------------------------------------------|
| Address | 149 New Montgomery St, 4th Floor<br>San Francisco CA 94105 |
| Correspondence Person | Ken Koster<br>CTO, Ceevra, Inc. |
| Contact Information | Email: kkoster@ceevra.com<br>Phone: 415-305-5326 |
| Date Prepared | March 2, 2025 |
#### 2. Updated Device
| Proprietary Name | Ceevra Reveal 3+ |
|---------------------|--------------------------------------------------|
| Common Name | Reveal 3+ |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | QIH |
| Regulatory Class | II |
#### 3. Originally Cleared Device
| Proprietary Name | Ceevra Reveal 3+ |
|------------------------|--------------------------------------------------|
| Common Name | Reveal 3+ |
| Premarket Notification | K233568 |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
Ceevra, Inc., Traditional 510(k) Page 1
{5}------------------------------------------------
Image /page/5/Picture/0 description: The image shows the logo for CEEVRA. The logo features a stylized, curved design in shades of blue, resembling a wave or a network of interconnected points. Below the graphic is the text "CEEVRA" in a simple, sans-serif font, also in blue.
| Product Code | QIH |
|------------------|-----|
| Regulatory Class | II |
#### Device Description 4.
Ceevra Reveal 3+, as modified, ("Modified Reveal 3+"), manufactured by Ceevra, Inc. (the "Company"), is a software as a medical device with two main functions: (1) it is used by Company personnel to generate three-dimensional (3D) images from existing patient CT and MR imaging, and (2) it is used by clinicians to view and interact with the 3D images during preoperative planning and intraoperatively.
Clinicians view 3D images via the Mobile Image Viewer software application which runs on compatible mobile devices, and the Desktop Image Viewer software application which runs on compatible computers. The 3D images may also be displayed on compatible external displays, or in virtual reality (VR) format with a compatible off-the-shelf VR headset.
Modified Reveal 3+ includes features that enable clinicians to interact with the 3D images including rotating, zooming, panning, selectively showing or hiding individual anatomical structures, and viewing measurements of or between anatomical structures.
#### ನ. Indications for Use
Ceevra Reveal 3+ is intended as a medical imaging system that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from CT or MR imaging devices and that such processing may include the generation of preliminary segmentations of normal anatomy using software that employs machine learning and other computer vision algorithms. It is also intended as software for preoperative surgical planning, and as software for the intraoperative display of the aforementioned multi-dimensional digital images. Ceevra Reveal 3+ is designed for use by health care professionals and is intended to assist the clinician who is responsible for making all final patient management decisions.
The machine learning algorithms in use by Ceevra Reveal 3+ are for use only for adult patients (22 and over). Three-dimensional images for patients under the age of 22 or of unknown age will be generated without the use of any machine learning algorithms.
#### Substantial Equivalence 6.
As detailed in the following tables, the indications for use and technological characteristics of the updated device are substantially equivalent to the originally cleared device.
{6}------------------------------------------------
Image /page/6/Picture/0 description: The image contains the logo for CEEVRA. The logo features a stylized letter 'C' formed by a series of blue arcs that transition from solid to dotted, suggesting movement or connectivity. Below the symbol, the word 'CEEVRA' is written in a sans-serif font, with a gradient effect that mirrors the color scheme of the arc above.
| | | | Table 6.1: Comparison of Indications for Use Statements |
|--|--|--|---------------------------------------------------------|
|--|--|--|---------------------------------------------------------|
| Updated Device: | Originally Cleared Device: |
|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Modified Ceevra Reveal 3+ | Ceevra Reveal 3+ (K233568) |
| Ceevra Reveal 3+ is intended as a medical imaging<br>system that allows the processing, review, analysis,<br>communication and media interchange of multi-<br>dimensional digital images acquired from CT or MR<br>imaging devices and that such processing may include<br>the generation of preliminary segmentations of normal<br>anatomy using software that employs machine learning<br>and other computer vision algorithms. It is also intended<br>as software for preoperative surgical planning, and as<br>software for the intraoperative display of the<br>aforementioned multi-dimensional digital<br>images.<br>Ceevra Reveal 3+ is designed for use by health care<br>professionals and is intended to assist the clinician who<br>is responsible for making all final patient management<br>decisions. | Ceevra Reveal 3+ is intended as a medical imaging<br>system that allows the processing, review, analysis,<br>communication and media interchange of multi-<br>dimensional digital images acquired from CT or MR<br>imaging devices and that such processing may include<br>the generation of preliminary segmentations of normal<br>anatomy using software that employs machine learning<br>and other computer vision algorithms. It is also intended<br>as software for preoperative surgical planning, and as<br>software for the intraoperative display<br>of the<br>aforementioned multi-dimensional digital<br>images.<br>Ceevra Reveal 3+ is designed for use by health care<br>professionals and is intended to assist the clinician who<br>is responsible for making all final patient management<br>decisions. |
| The machine learning algorithms in use by Ceevra | The machine learning algorithms in use by Ceevra |
| Reveal 3+ are for use only for adult patients (22 and | Reveal 3+ are for use only for adult patients (22 and |
| over). Three-dimensional images for patients under the | over). Three-dimensional images for patients under the |
| age of 22 or of unknown age will be generated without | age of 22 or of unknown age will be generated without |
| the use of any machine learning algorithms. | the use of any machine learning algorithms. |
| Feature/<br>Function | Updated Device:<br>Modified Ceevra Reveal 3+ | Originally Cleared Device:<br>Ceevra Reveal 3+ (K233568) |
|----------------------------|--------------------------------------------------------|----------------------------------------------------------|
| Supported image Modalities | CT and MR | CT and MR |
| Intended users | Healthcare Professionals | Healthcare Professionals |
| Intended environment | Healthcare facilities such as<br>hospitals and clinics | Healthcare facilities such as<br>hospitals and clinics |
| Device Class | Class II | Class II |
| Image analysis features | Interactive manipulation and<br>3D visualization | Interactive manipulation and<br>3D visualization |
| Preoperative use | Yes | Yes |
| Intraoperative use | Yes | Yes |
Table 6.2: Comparison of Technological Characteristics
{7}------------------------------------------------
Image /page/7/Picture/0 description: The image features a logo with a stylized, abstract design above the word "CEEVRA." The design consists of curved, blue lines that appear to emanate from a central point, creating a sense of movement or flow. The lines gradually transition into a series of blue dots, adding a gradient effect to the overall shape. The word "CEEVRA" is written in a modern, sans-serif font, with the letters also rendered in a gradient blue color, complementing the design above.
| Feature/<br>Function | Updated Device:<br>Modified Ceevra Reveal 3+ | Originally Cleared Device:<br>Ceevra Reveal 3+ (K233568) |
|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------|-------------------------------------------------------------------------------|
| 3D images used intraoperatively<br>for real-time guidance, navigation<br>or otherwise integrated with<br>surgical instruments | No | No |
| Segmentation work performed by | Internal Operators | Internal Operators |
| Built-in features for end-user to<br>compare CT/MR to device output | No | No |
| Quantitative measurements<br>calculated by device | Volume of structure, diameter<br>of structure, distance between<br>two points | Volume of structure, diameter<br>of structure, distance between<br>two points |
| Software generates semi-<br>automated segmentations of<br>abnormal anatomy | No | No |
| Software generates semi-<br>automated segmentations of<br>certain normal anatomy | Yes | Yes |
| Technology used to generate semi-<br>automated segmentations of<br>pulmonary arteries within the<br>lung, pulmonary veins within the<br>lung, pulmonary airways, and<br>bronchopulmonary segments | Machine Learning | Computer vision algorithms<br>not utilizing Machine Learning |
#### 7. Performance Data
Safety and performance of Modified Ceevra Reveal 3+ has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Additionally, the software validation activities were performed in accordance with IEC 62304:2006/Amd 1: 2015-Medical device software - Software life cycle processes, in addition to the FDA Guidance documents, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" and "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions. "
Four machine learning models are included in Modified Ceevra Reveal 3+. These models were verified with datasets of actual CT or MR imaging studies of patients. A total of 133 imaging studies were used to evaluate the device. No dataset contained more than one imaging study from any particular patient. No
Ceevra, Inc., Traditional 510(k) Page 4
{8}------------------------------------------------
Image /page/8/Picture/0 description: The image shows the logo for CEEVRA. The logo consists of a blue circular design made up of smaller curved lines that resemble a stylized letter 'C'. Below the circular design, the word "CEEVRA" is written in a blue sans-serif font. The overall design is clean and modern.
imaging study used to verify performance was used for training; independence of training and testing data were enforced at the level of the scanning institution, namely, studies sourced from a specific institution were used for either training or testing but could not be used for both. The data used in the device validation ensured diversity in patient population and scanner manufacturers. Subgroup analysis was performed for patient age, patient sex, and scanner manufacturers. For non-prostate related datasets included 48% female patients and 52% male patients. Across all datasets, 31% of patients were under 60 years old, 36% were 60 to 70 years old, 27% were over 70 years old, and 6% were of unknown age. Scanner manufacturers included GE Medical Systems, Toshiba, Hitachi, and Philips Medical Systems. Ethnicity of patients in the datasets was reasonably correlated to the overall US population.
Performance was verified by comparing segmentations generated by the machine learning models against segmentations generated by medical professionals from the same imaging study. The performance of the machine learning models, characterized by the Sørensen-Dice coefficient (DSC) or the Hausdorff distance metric at the 95th percentile (HD-95), was as follows: prostate (from MR prostate imaging) 0.90 DSC; bladder (from MR prostate imaging) 0.93 DSC; neurovascular bundles (from MR prostate imaging) 6.6 mm HD-95; kidney (from CT abdomen imaging) 0.92 DSC; kidney (from MR abdomen imaging) 0.89 DSC; artery (from CT abdomen imaging) 0.90 DSC; artery (from MR abdomen imaging) 0.87 DSC; vein (from CT abdomen imaging) 0.88 DSC; vein (from MR abdomen imaging) 0.82 DSC; pulmonary attery (from CT chest imaging) 0.82 DSC; pulmonary vein (from CT chest imaging) 0.83 DSC, airways (from CT chest imaging) 0.82 DSC; bronchopulmonary segments (from CT chest imaging) 0.86 DSC.
The accuracy of measurement features has been validated on phantom data and on datasets of actual CT or MR imaging studies of patients, including CT and MR imaging studies processed with machine learning models.
#### Conclusion 8.
Based on the intended use, indications for use, technological characteristics, and performance comparison to the originally cleared Reveal 3+ device (K233568), Modified Ceevra Reveal 3+ device is deemed to not raise new questions of safety and effectiveness and is substantially equivalent to the originally cleared device in terms of safety, efficacy, and performance.
Predicate graph will load when search results are available.
Embedding visualization will load when search results are available.
PDF viewer will load when search results are available.
Loading panels...
Select an item from Submissions
Click any panel, subpart, regulation, product code, or device to see details here.
Section Matches
Results will appear here.
Product Code Matches
Results will appear here.
Special Control Matches
Results will appear here.
Loading collections...
Loading
My Alerts
You will receive email notifications based on the filters and frequency you set for each alert.
Sort by:
Create Alert
Search Filters
Agent Token
Create a read-only bearer token for Claude, ChatGPT, or other agents that can call HTTP APIs.
Copy this now. It will not be shown again.
Connected apps
Apps you authorized through browser sign-in. Disconnecting revokes their access immediately.
Learn the FDA Browser
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.