Segmentron Viewer is a software product intended for processing and manipulating maxillofacial radiographic images. Segmentron Viewer allows users to perform the following functions: 1. Viewing patient images (provides tools for image processing and viewing functions); 2. Reading and 3D visualization of CBCT images; 3. Generating editable 3D STL files (for educational purposes only).
Device Story
Segmentron Viewer is a web-based SaMD for dental image processing; inputs are maxillofacial CBCT images. Device uses supervised machine learning (AI) to automatically segment and label teeth, dental pulp, and eight maxillofacial anatomical structures. Output includes multi-planar reconstruction (MPR) views, 3D visualizations, and editable 3D STL files. Operated by dentists or radiologists in clinical settings; facilitates navigation and evaluation of dental anatomy. HCPs use generated segmentation reports to assist in patient evaluation. Benefits include automated anatomical identification and enhanced visualization for clinical workflow.
Clinical Evidence
Four retrospective standalone validation studies compared algorithm output against reference standards established by board-certified radiologists. 1) Tooth segmentation (n=126): Dice Coefficient (DSC) 0.96 (95% CI: 0.95, 0.96; p<0.0001). 2) Pulp segmentation (n=43): DSC 0.88 (95% CI: 0.87, 0.89; p<0.0001). 3) Anatomy segmentation (n=56): DSC met pre-defined performance goals. 4) Labeling performance (n=40): 100% accuracy. No clinical prospective data.
Technological Characteristics
Software-only, web-based application. Utilizes supervised machine learning (AI) for image segmentation and labeling. DICOM compatible. Features include MPR, 3D visualization, and STL file generation. Operates in a network environment.
Indications for Use
Indicated for use by medical professionals (dentists, radiologists) for processing and manipulating maxillofacial radiographic images in patients 14 years and older with permanent teeth.
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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FDA U.S. FOOD & DRUG ADMINISTRATION
September 9, 2025
DGNCT, LLC
% Kelliann Payne
Partner
Hogan Lovells US LLP
1735 Market Street, Floor 23
PHILADELPHIA, PA 19103
Re: K251072
Trade/Device Name: Segmentron Viewer
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical image management and processing system
Regulatory Class: Class II
Product Code: QIH
Dated: April 7, 2025
Received: August 11, 2025
Dear Kelliann Payne:
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.
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
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K251072 - Kelliann Payne
Page 2
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 (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the quality 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-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-
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K251072 - Kelliann Payne
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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,

Lu Jiang, Ph.D.
Assistant Director
Diagnostic X-Ray Systems Team
DHT8B: Division of Radiologic 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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FORM FDA 3881 (6/20)
Page 1 of 1
PSC Publishing Services (301) 443-6740
EF
| DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration Indications for Use | Form Approved: OMB No. 0910-0120 Expiration Date: 06/30/2023 See PRA Statement below. |
| --- | --- |
| 510(k) Number (if known) K251072 | |
| Device Name Segmentron Viewer | |
| Indications for Use (Describe) Segmentron Viewer is a software product intended for processing and manipulating maxillofacial radiographic images. Segmentron Viewer allows users to perform the following functions: 1. Viewing patient images (provides tools for image processing and viewing functions); 2. Reading and 3D visualization of CBCT images; 3. Generating editable 3D STL files (for educational purposes only). | |
| The device is indicated for use by medical professionals (such as dentists and radiologists), in patients 14 years and older with permanent teeth. Segmentron Viewer is a web application. It can be used in a network environment. | |
| Type of Use (Select one or both, as applicable) ☑ Prescription Use (Part 21 CFR 801 Subpart D) ☐ Over-The-Counter Use (21 CFR 801 Subpart C) | |
| CONTINUE ON A SEPARATE PAGE IF NEEDED. | |
| This section applies only to requirements of the Paperwork Reduction Act of 1995. *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.* | |
| The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to: Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff@fda.hhs.gov | |
| "An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number." | |
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510(k) Summary
DGNCT LLC'S Segmentron Viewer (K251072)
Submitter
DGNCT LLC
333 Southeast 2nd Avenue, 20th Floor#563,
Miami, Florida 33131, USA
Phone: + 1 (519) 619-4212
E-mail: support@diagnocat.com
Contact Person: Anastasya Melnikov
Date Prepared: September 9, 2025
Name of Device: Segmentron Viewer
Device Classification: CFR 892.2050 Medical image management and processing system
Regulatory Class: II
Product Code: QIH
Predicate Device: Ez3D-i/E3 device (K231757) (Ewoosoft Co., Ltd.)
Device Description
Segmentron Viewer is a semi-automated software as a medical device (SaMD) for dental image processing and management. The device's main function is to perform automated analysis of maxillofacial Cone Beam Computed Tomography (CBCT) images uploaded by the user, which consists of applying artificial neural network models (AI) to such images to obtain automatically generated 3D segmentations of teeth and anatomy. The user is able to edit these segmentations. The device also provides functions for enhancement and 3D visualization of the images. It additionally enables uploading, saving, and sharing CBCT images for the clinician's ease of use.
Segmentron Viewer identifies each tooth and tooth pulp present in the upper and the lower jaw (as shown on the input scan), numbers them, and segments them. Similarly, the device identifies each of eight maxillofacial anatomy structures in a CBCT scan, and segments them. The software facilitates navigation through the images for detailed evaluation and produces multi-planar reconstruction (MPR) views of each segmented object. The device generates a segmentation report from the input CBCT scan, for the healthcare provider's (HCP) use to further evaluate a patient's teeth and anatomy.
Intended Use / Indications for Use
Segmentron Viewer is a software product intended for processing and manipulating maxillofacial radiographic images. Segmentron Viewer allows users to perform the following functions:
1. Viewing patient images (provides tools for image processing and viewing functions);
2. Reading and 3D visualization of CBCT images;
3. Generating editable 3D STL files (for educational purposes only).
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The device is indicated for use by medical professionals (such as dentists and radiologists), in patients 14 years and older with permanent teeth.
Segmentron Viewer is a web application. It can be used in a network environment.
IFU comparison: Both the subject and predicate devices are intended as tools for dental professionals to use in processing maxillofacial radiographic imaging. The differences in specific indications for use – including Segmentron Viewer's narrower range of input imaging sources and image formats – do not raise different questions of safety or effectiveness, as the fundamental clinical use is the same.
## Summary of Technological Characteristics
Segmentron Viewer has the same general intended use and similar indications for use, technological characteristics, and principles of operation as the previously cleared predicate device, Ez3D-i /E3 (K231757). Both devices are based on the same fundamental scientific technology and principles of operation: Both are software-only, AI-based devices that utilize a supervised machine learning algorithm to provide comparable tools for processing and manipulation of maxillofacial radiographic images. At a high level, the two devices are based on the following same technological elements:
- Both devices segment the teeth and anatomical structures in a CBCT image.
- Both devices offer tools to export data to interface with other software.
- Both devices offer features such as MPR and 3D image analysis, enabling the viewing and evaluation of radiographic images in similar ways.
- Both devices are DICOM compatible, ensuring similar usability and integration capabilities.
- Both devices support the generation of reports to document findings and enhance clinical workflow.
Moreover, the primary differences in technology between the two devices do not raise different questions of safety and effectiveness. Performance test data demonstrate Segmentron Viewer's ability to safely and effectively achieve its intended use and its various individual functions, and support its substantial equivalence to the predicate.
The predicate supports a broader range of imaging modalities than Segmentron Viewer. However, both products focus on providing accurate visualization and segmentation/labeling of dental and maxillofacial images. The differences in specific visualization and processing functions do not raise different questions of safety or effectiveness. The additional features of the subject device (such as segmentation of pulp in addition to teeth) serve only to enhance the flexibility of its analysis. Similarly, features of the predicate that are absent in Segmentron Viewer serve only to supplement the core functionality of the predicate without altering the general intended use which Segmentron shares. A table comparing the key features of the subject and predicate devices is provided below.
## Performance Data
DGNCT LLC evaluated the performance of Segmentron Viewer in four retrospective standalone validation studies, comparing the software's segmentation and labeling performance against reference standards ("ground truth"). The primary objective in each study was to assess the agreement between the algorithm's output and the reference standard.
U.S. board-certified radiologists established a reference standard for each CBCT image, using manual segmentation (for the segmentation studies) or annotation (for the labeling study). The same CBCT
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images were then analyzed using the Segmentron Viewer. For the segmentation studies, Dice Coefficient (DSC) was used as the primary endpoint to evaluate segmentation agreement between the algorithm and the reference standard. For the labeling study, overall accuracy was used to evaluate labeling agreement. The dataset used for the studies included CBCT scans sourced from a variety of geographic regions and demographics. The studies are outlined below.
1. Tooth Segmentation: This study assessed the ability of Segmentron Viewer to segment and number teeth in 126 CBCT scans of permanent teeth. Across all teeth, Segmentron demonstrated strong segmentation agreement with the reference standard, as evidenced by the Dice Coefficient (primary endpoint) exceeding the pre-defined performance goal (PG) with a result of 0.96 (95% CI: 0.95, 0.96; p < 0.0001), as well as success on the pre-defined secondary endpoints.
2. Pulp Segmentation: This study assessed the ability of Segmentron Viewer to segment dental pulp in 43 CBCT scans. Across the pulp of all teeth, Segmentron demonstrated strong segmentation agreement with the reference standard, as evidenced by Dice Coefficient (primary endpoint) = 0.88 (95% CI: 0.87, 0.89; p < 0.0001) – exceeding the pre-defined PG – and success on the secondary endpoints.
3. Anatomy Segmentation: This study assessed the ability of Segmentron Viewer to segment 8 anatomical structures in 56 CBCT scans. Across all anatomical structures, Segmentron demonstrated strong segmentation agreement with the reference standard. The primary endpoint was met, as the Dice Coefficients for each anatomical region exceeded their respective pre-defined PGs.
4. Labeling Performance: This study aimed to validate the accuracy of labels automatically generated by the device for teeth, anatomical structures, and pulp on 40 CBCT scans from the larger validation dataset. Across all teeth, pulp, and anatomical structures in all CBCT scans, Segmentron Viewer achieved a labeling accuracy of 100%, demonstrating strong concordance between the labels automatically generated by the device and those determined by an expert radiologist.
## Conclusion
Segmentron Viewer is as safe and effective as the predicate device, Ez3D-i (K231757). The subject device has the same intended use and similar indications for use, technological characteristics, and principles of operation. The minor differences in indications do not alter the intended clinical use of the device as compared to its predicate, nor do they affect its safety and effectiveness when used as labeled. In addition, the minor technological differences between Segmentron Viewer and its predicate raise no new questions of safety or effectiveness. Performance data demonstrates that the subject device functions as intended and further supports substantial equivalence.
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| Criteria | Subject Device: Segmentron Viewer | Predicate Device: Ez3D-i /E3 (K231757) | Comparison |
| --- | --- | --- | --- |
| Regulation # | 21 CFR 892.2050 | 21 CFR 892.2050 | |
| Device | Automated radiological image processing software | Automated radiological image processing software | Same |
| Product Code | QIH | QIH | Same |
| Intended Use/Indications for Use | Segmentron Viewer is a software product intended for processing and manipulating maxillofacial radiographic images. Segmentron Viewer allows users to perform the following functions:
1. Viewing patient images (provides tools for image processing and viewing functions);
2. Reading and 3D visualization of CBCT images;
3. Generating editable 3D STL files (for educational purposes only).
The device is indicated for use by medical professionals (such as dentists and radiologists), in patients 14 years and older with permanent teeth. Segmentron Viewer is a web application. It can be used in a network environment. | Ez3D-i/E3 is dental imaging software that is intended to provide diagnostic tools for maxillofacial radiographic imaging. These tools are available to view and interpret a series of DICOM compliant dental radiology images and are meant to be used by trained medical professionals such as radiologist and dentist.
Ez3D-i/E3 is intended for use as software to load, view and save DICOM images from CT, panorama, cephalometric and intraoral imaging equipment and to provide 3D visualization, 2D analysis, in various MPR (Multi-Planar Reconstruction) functions | Same intended use. The minor differences in indications for use do not raise different questions of safety or effectiveness or alter the fundamental clinical purpose. |
| Functions and Capabilities | • View and save images from CBCT scanners
• Image measurement
• Multi-Planar Reconstruction (MPR) functions
• 3D image viewing and reformation
• Rendering functions, e.g., MPR and 3D rendering (visualization), 3D zoom
• Image segmentation
• Export capabilities
• Generates reports
• Manage and add objects
• Image manipulation (e.g., magnify, hue, brightness)
• Image annotation
• Cloud-based storage | • View and save images from CT, panorama, cephalometric and intraoral imaging equipment
• Image measurement
• Multi-Planar Reconstruction (MPR) functions
• 2D image viewing and analysis
• 3D image viewing and reformation (including canal drawing)
• Rendering functions, e.g., Volume Rendering, MIP, miniIP, X-ray, and 3D zoom
• Image Segmentation
• Transfer images
• Generates reports
• Manage and add objects, color maps, fine tuning
• Image manipulation (e.g., magnify, hue, brightness)
• Image annotation
• Implant simulation tools for treatment planning
• Bone density profiling | Both are AI-based, software-only devices providing tools for viewing/evaluating pre-existing maxillofacial radiographic images. The differences in specific functions do not raise different questions. |
| Algorithm | Supervised machine learning | Supervised machine learning | Same |
| Image Format | DICOM | DICOM | Same |
| Configuration | Web application | Desktop application | Similar |
Page 4 of 4
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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.