K201501 · MeVis Medical Solutions AG · OEB · Feb 23, 2021 · Radiology
Device Facts
Record ID
K201501
Device Name
Veolity
Applicant
MeVis Medical Solutions AG
Product Code
OEB · Radiology
Decision Date
Feb 23, 2021
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2050
Device Class
Class 2
Attributes
Software as a Medical Device
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Solid pulmonary nodule detection
—
—
Equivalent sensitivity and false positive rates compared to the primary predicate device (K043617).
—
—
Multi-center dataset with modern and multivendor CT data.
—
Indications for Use
Veolity is intended to: - display a composite view of 2D cross-sections, and 3D volumes of chest CT images, - allow comparison between new and previous acquisitions as well as abnormal thoracic regions of interest, such as pulmonary nodules, - provide Computer-Aided Detection ("CAD") findings, which assist radiologists in the detection of solid pulmonary nodules between 4-30 mm in size in CT images with or without intravenous contrast. CAD is intended to be used as an adjunct, alerting the radiologist - after his or her initial reading of the scan - to regions of interest that may have been initially overlooked. The system can be used with any combination of these features. Enabling is handled via licensing or configuration options.
Device Story
Veolity is a software-only medical imaging platform for chest CT analysis; operates on standard PCs in standalone or client-server configurations. Inputs include digital chest CT images; processes data via automated image registration, lung segmentation, and nodule segmentation. Provides CAD findings for solid pulmonary nodules (4-30 mm) as an adjunct to initial radiologist review; calculates quantitative metrics (volume, mass, doubling time, density) and supports temporal comparison of studies. Includes a malignancy risk calculator based on the McWilliams et al. (2013) model. Radiologists use the output to identify, characterize, and track nodules; facilitates clinical decision-making in diagnostic and screening evaluations (e.g., Low Dose CT Lung Cancer Screening). Benefits include improved detection of overlooked nodules and standardized quantitative assessment of growth patterns.
Clinical Evidence
No clinical testing conducted. Substantial equivalence is supported by non-clinical bench testing, including verification and validation of software specifications and performance assessment of the CAD system. CAD performance (sensitivity and false positive rates) was compared to the primary predicate using a multi-center dataset of modern, multi-vendor CT data, demonstrating equivalent detection of solid actionable pulmonary nodules.
Technological Characteristics
Software-only medical imaging platform; runs on standard off-the-shelf PCs. Supports standalone or distributed client-server network architecture. Features automated image registration, lung/nodule segmentation, and volumetric analysis. Implements CAD for solid nodule detection (4-30 mm) and a malignancy risk calculator based on the McWilliams et al. (2013) model. Connectivity via standard network protocols for image transfer. No direct patient contact; non-sterile.
Indications for Use
Indicated for radiologists to review and analyze chest CT examinations in asymptomatic populations, specifically for the detection and characterization of solid pulmonary nodules (4-30 mm).
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).
Lung Nodule Assessment and Comparison Option (LNA) (K162484)
Submission Summary (Full Text)
{0}------------------------------------------------
ADMINISTRATION
MeVis Medical Solutions AG % Rolf Rzodeczko Manager Regulatory Affairs Caroline-Herschel-Strasse 1 Bremen, Bremen 28359 GERMANY
February 23, 2021
Re: K201501
Trade/Device Name: Veolity Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: OEB, LLZ Dated: January 20, 2021 Received: January 26, 2021
Dear Rolf Rzodeczko:
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. 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 located 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.
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 803) for
{1}------------------------------------------------
devices or postmarketing safety reporting (21 CFR 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 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to 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-regulatoryassistance/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.
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
{2}------------------------------------------------
## Indications for Use
510(k) Number (if known) K201501
Device Name Veolity
Indications for Use (Describe) Veolity is intended to:
- display a composite view of 2D cross-sections, and 3D volumes of chest CT images,
- allow comparison between new and previous acquisitions as well as abnormal thoracic regions of interest, such as pulmonary nodules,
- provide Computer-Aided Detection ("CAD") findings, which assist radiologists in the detection of solid pulmonary nodules between 4-30 mm in size in CT images with or without intravenous contrast. CAD is intended to be used as an adjunct, alerting the radiologist - after his or her initial reading of the scan - to regions of interest that may have been initially overlooked.
The system can be used with any combination of these features. Enabling is handled via licensing or configuration options.
Intended Patient Population:
Veolity is an imaging software including computer assisted reading CT examinations of the chest on an asymptomatic population.
Intended Conditions of Use:
As a software-only product, Veolity does not come into direct contact with patients, and it is neither a sterile device nor an in vitro diagnostic device. Additionally, the environmental and location requirements as defined for the hardware running Veolity apply. The software does not alter these requirements.
Intended User Profile: Veolity is intended to be used by medical professionals (e.g., radiologists) with adequate training.
| Type of Use (Select one or both, as applicable) |
|-------------------------------------------------|
|-------------------------------------------------|
> Prescription Use (Part 21 CFR 801 Subpart D)
[_] Over-The-Counter Use (21 CFR 801 Subpart C)
### CONTINUE ON A SEPARATE PAGE IF NEEDED.
This section applies only to requirements of the Paperwork Reduction Act of 1995.
### *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.*
The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:
> Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov
"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."
{3}------------------------------------------------
## 001_005: 510(k) Summary
K201501
#### 1 Submitter
| Submitted Name: | MeVis Medical Solutions AG<br>Caroline-Herschel-Straße 1<br>28359 Bremen<br>Germany |
|---------------------------------------|----------------------------------------------------------------------------------------------------------|
| Establishment Name: | MeVis Medical Solutions AG |
| Establishment<br>Registration Number: | 3010601176 |
| Date Prepared: | 06/03/2020 |
| Date Adjusted: | 02/22/2021 |
| Contact Person: | Rolf Rzodeczko<br>Manager Regulatory Affairs |
| Telephone: | +49 (0)421 22495-120 |
| Fax: | +49 (0)421 22495-999 |
| 2 Device | |
| Device Trade Name: | Veolity |
| Common Name: | Medical Imaging Processing Software, Medical Imaging<br>Workstation |
| Regulation: | CFR 21 892.2050 |
| Classification Name: | Lung Computed Tomography System, Computer-aided Detection<br>Picture archiving and communications system |
| Product Code: | OEB, LLZ |
| Class: | Class II |
| Panel: | Radiology |
{4}------------------------------------------------
### 3 Predicate Devices
| 510(k) Number | Predicate Device | Product Code |
|---------------|--------------------------------------------|--------------|
| K043617 | ImageChecker CT CAD Software System | OEB |
| K023003 | ImageChecker-CT Workstation | LLZ |
| K040028 | Comprehensive Chest Analysis Tools (C-CAT) | LLZ |
#### 4 Reference Device
| 510(k) Number | Reference Device | Product Code |
|---------------|-------------------------------------------------------|--------------|
| K162484 | Lung Nodule Assessment and Comparison Option<br>(LNA) | LLZ, JAK |
### 5 Device Description
Veolity is a medical imaging software platform that allows processing, review, and analysis of multi-dimensional digital images.
The system integrates within typical clinical workflow patterns through receiving and transferring medical images over a computer network. The software can be loaded on a standard off-theshelf personal computer (PC). It can operate as a stand-alone workstation or in a distributed server-client configuration across a computer network.
Veolity is intended to support the radiologist in the review and analysis of chest CT data. Automated image registration facilitates the synchronous display and navigation of current and previous CT images for follow-up comparison
The software enables the user to determine quantitative and characterizing information about nodules in the lung in a single study, or over the time course of several thoracic studies. Veolity automatically performs the measurements for segmented nodules, allowing lung nodules and measurements to be displayed. Afterwards nodule segmentation contour lines can be edited by the user manually with automatic recalculation of geometric measurements post-editing. Further, the application provides a range of interactive tools specifically designed for segmentation and volumetric analysis of findings in order to determine growth patterns and compose comparative reviews.
Veolity requires the user to identify a nodule and to determine the type of nodule in order to use the appropriate characterization tools. Additionally, the software provides an optional/licensable CAD package that analyzes the CT images to identify findings with features suggestive of solid pulmonary nodules between 4-30 mm in size. The CAD is not intended as a detection aid for either part-solid or non-solid lung nodules. The CAD is intended to be used as an adjunct, alerting the radiologist – after his or her initial reading of the scan – to regions of interest that may have been initially overlooked.
{5}------------------------------------------------
The system can be used with any combination of these features. Enabling/disabling is handled via licensing or configuration options.
From workflow perspective, incoming chest CT data is processed and prepared by the CAD server software. Ready to read data is transferred to the viewing client software. Radiologists can read and analyze the data by using different tools and compare current and prior images. Finally, findings can be reported and forwarded to long-term archives.
Veolity may be utilized in both diagnostic and screening evaluations supporting Low Dose CT Lung Cancer Screening1.
Key Features
- 1. Synchronized side-by-side viewing between studies from different time points (temporal comparative review 2D/3D)
- 2. Automatic lung segmentation
- 3. Automatic pulmonary nodule segmentation with manual editing tools
- 4. Propagation of previously segmented nodules from prior studies for comparison
- 5. Automatic calculation of the following measurements for each segmented nodule:
- a. Quantification of nodule measurements:
- i. Long Axis (longest diameter on an axial slice (mm))
- ii. Short Axis (longest diameter perpendicular to the long axis on the same slice (mm))
- iii. Average \ Equivalent Diameter (mm)
- iv. Volume (mm3)
- v. Density (HU)
- vi. Mass (mq)
Manual edit of the nodule segmentation contour lines with automatic recalculation of geometric measurements post-editing
- b. Specification of the following nodule characteristics:
- i. Nodule type (solid, part-solid, non-solid, calcified, perifissural, endobronchial)
- ii. Lobe/seqment location
- iii. Nodule spiculation
- c. Temporal comparison of the following matching nodule measurements between each follow-up scan and the previous scan:
- i. Mass and volume doubling time in days
- ii. Volume change in percent (%)
- iii. Absolute change of nodule measurements
- 6. Reporting results including patient related information and nodule measurements
- 7. Pre-fill function for reporting based on ACR Lung-RADS guidelines
- 8. Nodule Risk Calculator tool based on patient and nodule characteristics for estimation of the probability that lung nodules detected on baseline screening low-dose CT scans are malignant, based on McWilliams, Annette, et al. "Probability of cancer in pulmonary nodules detected on first screening CT." New England Journal of Medicine 369.10 (2013): 910-919.
<sup>1</sup> The screening must be performed within the established inclusion criteria of programs/ protocols that have been approved and published by either a governmental body or professional medical society.
Please refer to clinical literature that includes the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.
{6}------------------------------------------------
- 9. Computer-Aided Detection (CAD) of solid pulmonary nodules between 4-30 mm in size (only to be used as an adjunct, alerting the radiologist - after his or her initial reading of the scan - that mav have been initially overlooked)
## Additional Information for Risk Calculator:
The malignancy risk calculator feature in Veolity is based on the full model with spiculation developed by Brock University as described in McWilliams, et al (2013). This model allows estimating the probability that lunq nodules detected on baseline screening low-dose CT scans are malignant.
The model's performance was validated using two large population-based prospective studies: the Pan-Canadian Early Detection of Lung Cancer Study (PanCan) and the chemoprevention trials at the British Columbia Cancer Agency (BCCA), sponsored by the U.S. National Cancer Institute.
Further details can be found in McWilliams, A., Tammemagi, M.C., Mayo, J.R., Roberts, H., Liu, G., Sochrati, K., Yasufuku, K., Martel, S., Laberqe, F., Gingras, M. and Atkar-Khattra, S., (2013). Probability of cancer in pulmonary nodules detected on first screening CT. New England Journal of Medicine, 369(10), pp.910-919
#### 6 Indications for Use
Veolity is intended to
- display a composite view of 2D cross-sections, and 3D volumes of chest CT images
- . allow comparison between new and previous acquisitions as well as abnormal thoracic regions of interest, such as pulmonary nodules
- . provide Computer-Aided Detection ("CAD") findings, which assist radiologists in the detection of solid pulmonary nodules between 4-30 mm in size in CT images with or without intravenous contrast. CAD is intended to be used as an adjunct, alerting the radiologist - after his or her initial reading of the scan - to regions of interest that may have been initially overlooked.
The system can be used with any combination of these features. Enabling/disabling is handled via licensing or configuration options.
## Intended Patient Population:
Veolity is an imaging software including computer assisted reading tools for reviewing CT examinations of the chest on an asymptomatic population.
## Intended Conditions of Use:
As a software-only product, Veolity does not come into direct contact or indirect contact with patients, and it is neither a sterile device nor an in vitro diagnostic device. Additionally, the environmental and location requirements as defined for the hardware running Veolity apply. The software does not alter these requirements.
### Intended User Profile:
Veolity is intended to be used by medical professionals (e.g. radiologists) with adequate training.
{7}------------------------------------------------
### 7 Technological Characteristics Comparison
Veolity is a modified version of the previously cleared primary predicate device ImageChecker CT CAD Software System (K043617). As the new device Veolity integrates the functionality of the predicate devices and the reference device to a single device, the new device and the entirety of the predicate and reference devices are substantially equivalent in the areas of technical characteristics, general function, application, and intended use. The integrated usage was addressed and cleared for each predicate/reference device. The new device does not raise any new potential safety risks and is equivalent in performance to the existing legally marketed devices.
{8}------------------------------------------------
#### 8 Performance Data
## Non-clinical Tests Discussion:
As Veolity is the result of combining the predicate and reference devices into a single new device, the intended use changed and summarizes the intended use of all predicate devices. However, the individual functionalities of the predicate devices are used technically unchanged.
The complete functionality of subject device is covered through the Validation and Verification Plan and has been tested with respect to conformance to specifications.
In addition, the subject device's CAD system performance provides equal results in terms of sensitivity and false positive rates compared to the primary predicate device. This performance assessment is based on the panel review results that were subject in the initial submission of the predicate device.
Furthermore, the performance has been re-evaluated with a multi-center dataset with modern and multivendor CT data in terms of sensitivity and false positive rate per case. A comparable study design to the clinical study of the primary predicate device was used. The study demonstrated that the performance to detect solid actionable pulmonary nodules on modern data with subject device is equivalent to the primary predicate device.
In general, the performance of subject device is maintained using test data enriched with modern CT data.
## Clinical Tests Discussion:
N/A - No clinical testing has been conducted to demonstrate substantial equivalence.
#### 9 Conclusion
MeVis Medical Solutions has determined that its device, Veolity, is substantially equivalent to the entirety of the predicate and reference devices listed above. Resulting from the new product setup of combining the three formerly cleared separate medical devices to one medical device. the intended use changed. However, the main functionalities of the former devices remain unchanged. A comparison with the legally marketed predicate and reference devices indicates that it is substantially equivalent to the entirety of these devices, has the same (combined) intended use, principles of operation and technological characteristics and that it does not raise any new safety or efficacy concerns. Non-clinical tests demonstrate that the device is safe. effective, and is substantially equivalent to the entirety of the predicate and reference devices.
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.