K251629 · Medicrea International S.A.S. (Medtronic) · QIH · Aug 7, 2025 · Radiology
Device Facts
Record ID
K251629
Device Name
UNiD™ Spine Analyzer
Applicant
Medicrea International S.A.S. (Medtronic)
Product Code
QIH · Radiology
Decision Date
Aug 7, 2025
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2050
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device, Pediatric
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Compensatory mechanism prediction
—
One-tailed paired T-tests for non-inferiority with alpha of 0.025 and at least 90% power
Met defined acceptance criterion; non-inferiority demonstrated
Training set (No. of subjects=776); Testing set (No. of subjects=274)
>1 (Medtronic measurement technicians)
—
—
Indications for Use
The UNiD™ Spine Analyzer is intended for assisting healthcare professionals in viewing and measuring images as well as planning orthopedic surgeries. The device allows surgeons and service providers to perform generic, as well as spine related measurements on images, and to plan surgical procedures. The device also includes tools for measuring anatomical components for placement of surgical implants. Clinical judgment and experience are required to properly use the software.
Device Story
Web-based application for preoperative/postoperative spine surgery planning; processes radiological images (JPEG, PNG, GIF). User (surgeon/service provider) performs measurements, simulates surgical strategies (wedge, open, resect, spondy), and plans implant placement (rods, screws, cages). AI-enabled predictive models estimate compensatory spinal mechanisms post-fixation. Operates in standalone or connected mode via cloud (AWS). Output includes surgical plans and implant configurations; surgeon validates plan before manufacturing. Benefits include patient-specific surgical strategy and implant design; assists in complex spinal deformity correction.
Clinical Evidence
No clinical testing. Bench testing included software verification (IEC 62304) and validation (IEC 82304-1). AI model performance evaluated using 1050 retrospective patient cases (776 training, 274 testing). Non-inferiority of the updated degenerative predictive model vs predicate confirmed via MAE comparison (alpha 0.025, 90% power). Cybersecurity (ANSI AAMI SW96, IEC 81001-5-1) and usability (IEC 62366-1) testing performed.
Indicated for healthcare professionals to view/measure images and plan orthopedic spine surgeries. Applicable to adult and pediatric patients requiring spinal surgical planning and implant placement.
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
Medicrea International S.A.S. (Medtronic)
Cecile Humbert
Sr Regulatory Affairs Specialist
5389 Route de Strasbourg - Vancia
Rillieux-la-Pape, 69140
France
August 7, 2025
Re: K251629
Trade/Device Name: UNiD™ Spine Analyzer
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical Image Management And Processing System
Regulatory Class: Class II
Product Code: QIH, LLZ
Dated: May 28, 2025
Received: May 28, 2025
Dear Cecile Humbert:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device"
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
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K251629 - Cecile Humbert
Page 2
(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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K251629 - Cecile Humbert
Page 3
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, PhD
Assistant Director
Imaging Software Team
DHT8B: Division of Radiological Imaging Devices and Electronic Products
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
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UNiD™ Spine Analyzer
Page 9 of 32
| Indications for Use | | |
| --- | --- | --- |
| Please type in the marketing application/submission number, if it is known. This
textbox will be left blank for original applications/submissions. | K251629 | ? |
| Please provide the device trade name(s). | | ? |
| UNiD™ Spine Analyzer | | |
| Please provide your Indications for Use below. | | ? |
| The UNiD™ Spine Analyzer is intended for assisting healthcare professionals in viewing and measuring
images as well as planning orthopedic surgeries. The device allows surgeons and service providers to
perform generic, as well as spine related measurements on images, and to plan surgical procedures. The
device also includes tools for measuring anatomical components for placement of surgical implants. Clinical
judgment and experience are required to properly use the software. | | |
| Please select the types of uses (select one or both, as
applicable). | ☑ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C) | ? |
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510(k) Summary
UNiD™ Spine Analyzer
K251629
Date prepared: May 28, 2025
| I. Submitter information | | |
| --- | --- | --- |
| Submitter | MEDICREA INTERNATIONAL S.A.S. (MEDTRONIC)
5389 Route de Strasbourg – Vancia
Rillieux-la-Pape, 69140
France
Phone : 00 33 4 72 01 87 87 | |
| Contact Person | Cécile HUMBERT
Sr Regulatory Affairs Specialist
MEDICREA INTERNATIONAL S.A.S. (MEDTRONIC) | |
| II. Device identification | | |
| Trade name | UNiD™ Spine Analyzer | |
| Classification Regulation | Common Name | Automated radiological image processing software |
| | Primary Product Code | QIH |
| | Associated Product Code | LLZ |
| | Regulation Number | 21 CFR 892.2050 |
| | Regulation Name | Medical image management and processing system |
| | Class | 2 |
| III. Predicate and reference devices | | |
| Primary predicate device | Device name | UNiD™ Spine Analyzer |
| | 510(k) information | K212005, cleared on 01/12/2022 |
| | Common Name | system, image processing, radiological |
| | Product Code | LLZ |
| | Regulation Number | 21 CFR 892.2050 |
| | Regulation Name | Medical image management and processing system |
| | Class | 2 |
| IV. Subject device description | | |
| The UNiD™ Spine Analyzer is a web-based application developed to perform preoperative and postoperative patient image measurements and simulate preoperative planning steps for spine surgery. It aims to make measurements on a patient image, simulate a surgical strategy, draw patient-specific rods or choose from a pre-selection of standard implants. The UNiD™ Spine Analyzer allows the user to:
1. Measure radiological images using generic tools and “specialty” tools
2. Plan and simulate aspects of surgical procedures
3. Estimate the compensatory effects of the simulated surgical procedure on the patient’s spine
The planning of surgical procedures is done by Medtronic as part of the service of pre-operative planning. The surgical plan may then be used to assist in designing patient-specific implants. Surgeons will have to validate the surgical plan before Medtronic manufactures any implant.
The UNiD™ Spine Analyzer interface is accessible in either standalone mode or connected mode. | | |
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V. Intended use and indications for use
Intended use / Indications for use
The UNiD™ Spine Analyzer is intended for assisting healthcare professionals in viewing and measuring images as well as planning orthopedic surgeries. The device allows surgeons and service providers to perform generic, as well as spine related measurements on images, and to plan surgical procedures. The device also includes tools for measuring anatomical components for placement of surgical implants. Clinical judgment and experience are required to properly use the software.
VI. Comparison of technological characteristics
The subject device has similar fundamental scientific technology, overall design, features, computer configuration characteristics, intended use, and indications as the predicate device. The subject device and the predicate device are intended to assisting healthcare professionals in viewing and measuring images as well as planning orthopedic surgeries.
The subject device and the predicate device are compared in the table below.
| Feature/ Attribute | Subject Device
UNiD™ Spine Analyzer 5.0.0 | Predicate
UNiD™ Spine Analyzer 4.0.0 |
| --- | --- | --- |
| Product code(s) | QIH, LLZ | LLZ |
| Regulation number | 892.2050 | 892.2050 |
| Classification | 2 | 2 |
| Intended use / Indications for use | The UNiD™ Spine Analyzer is intended for assisting healthcare professionals in viewing and measuring images as well as planning orthopedic surgeries. The device allows surgeons and service providers to perform generic, as well as spine related measurements on images, and to plan surgical procedures. The device also includes tools for measuring anatomical components for placement of surgical implants. Clinical judgment and experience are required to properly use the software. | The UNiD™ Spine Analyzer is intended for assisting healthcare professionals in viewing and measuring images as well as planning orthopedic surgeries. The device allows surgeons and service providers to perform generic, as well as spine related measurements on images, and to plan surgical procedures. The device also includes tools for measuring anatomical components for placement of surgical implants. Clinical judgment and experience are required to properly use the software. |
| Fundamental scientific technology | Standalone software | Standalone software |
| Computer | PC Compatible | PC Compatible |
| Operating System | Windows + Mac | Windows + Mac |
| Supply mean | Cloud-based | Cloud-based |
| Health Data Host | AWS | AWS |
| Frontend code | Angular 14 & Angular JS 1.7 | Angular JS 1.7 |
| Backend code | .NET 8.0 | .NET 4.8 |
| Databases server | SQL Server | MySQL |
| Human intervention | Required for interpretation and manipulation of images | Required for interpretation and manipulation of images |
| Supported image format | JPEG; PNG; GIF | JPEG; PNG; GIF |
| Access mode | Standalone or connected mode | Standalone or connected mode |
| Feature
- Image setting | Contains all image setting tools (contrast; brightness; zoom in/out; flip, rotate, text overlay, calibration)
Intermodal calibration has been removed as part of a bug fix. | Contains all image setting tools (contrast; brightness; zoom in/out; flip, rotate, text overlay, calibration; intermodal calibration) |
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| Feature - Generic measurements tools | Contains all generic measurements tools (Line; circle; angle; measured, simulated, and normative values display; values color-coding) Color-coding thresholds are reworked but the operating mode is similar. | Contains all generic measurements tools (Line; circle; angle; measured, simulated, and normative values display; values color-coding) |
| --- | --- | --- |
| Feature - Spine measurements tools | Contains all spine-related measurements tools (Llordo; TKypho; pelvic; T1SPi; SVA; SA Analysis; Sagittal wizard; Coronal wizard; Transitional anatomy (as part of Sagittal wizard); Cervical; Lenke classification; Coronal correction factor) Sagittal Wizard, SA Analysis, Cervical tool and Lenke tool are reworked for improvement and adding new calculations, but the operating mode remains similar. The Guide Spline tool has been removed as part of a bug fix. | Contains all spine-related measurements tools (Llordo; TKypho; pelvic; T1SPi; SVA; SA Analysis; Sagittal wizard; Coronal wizard; Transitional anatomy (as part of Sagittal wizard); Cervical; Lenke classification; Guide spline; Coronal correction factor) |
| Feature - Surgical tools | Contains surgical tools to simulate surgical gestures (wedge; open; resect; spondy; wedge auto; open auto; resect auto) | Contains surgical tools to simulate surgical gestures (wedge; open; resect; spondy, wedge auto; open auto; resect auto) |
| Feature - Implants tools | Contains tools to place or draw rods, screws or cages (Free Rod; cage; screw; screw selection; Rod Auto; cage auto; screw wizard; postop screw). Some tools are reworked and relabeled. Tool functionalities have been improved, and bugs have been fixed, but there is no change in output data of the reworked tools. | Contains generic implants and tools to place and/or draw rods, screws or cages (UNiD Rod; cage; cage selection; screw; screw selection; UNiD Rod Auto; cage auto; screw wizard; postop screw). |
| Feature - Implants database | Contains commercially available interbody devices. Some TLIF and ALIF implants have been removed from the database. | Contains commercially available interbody devices. |
| Feature - AI algorithms | Degenerative; Adult Deformity; Pediatric Deformity The Degenerative model is updated. The functionality is similar. A new application is added (LIV=L5) | Degenerative; Adult Deformity; Pediatric Deformity |
| Features - UNiD™ plan export | Contains several export options (as image, as ZIP file or to the Medtronic HUB). The export options to the Medtronic HUB are reworked (export default view with transitional anatomy; link a rod design to a rod order) | Contains several export options (as image, as ZIP file or to the Medtronic HUB). |
Table 1: Substantial Equivalence Comparison
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# VII. Performance data
# Nonclinical tests
The following non-clinical performance data were provided in support of substantial equivalence:
# Software verification
The software verification is a combination of software code review, software unit test, unit integration testing, and system level integration to ensure the software is developed correctly and adheres to its design specifications. Software verification was conducted on the UNiD™ Spine Analyzer in accordance with IEC 62304.
# Software validation
The software validation was performed through user acceptance testing to ensure the software satisfies the specified requirements and meets the user's needs. It was conducted on the UNiD™ Spine Analyzer in accordance with IEC 82304-1.
# AI-enabled device software functions (AI-DSF)
The software incorporates AI-DSF, which give indication to the user about compensatory mechanisms the patient could experience after the surgery according to the planned instrumentation. Depending on the type of surgery (Adult, Pediatric or Degenerative), a specific predictive model is available.
Outputs of these models are intended to be used as inputs of the software system to simulate compensatory mechanisms after spinal fixation.
The subject device incorporates a new version of the Degenerative Predictive model compared to the predicate device. This new version includes degenerative constructs with Lower Instrumented level on L5 and predicts Lower non instrumented lordosis in these situations.
Preoperative and post operative images from 1050 patient surgery cases were collected from different clinical sites in the US only.
After the images were collected, they were then provided to and measured by highly trained Medtronic measurement technicians, operating within a quality-controlled environment. The viability of images for use is vetted by these technicians before beginning surgical planning. Consequently, the manufacturer of imaging equipment does not have any impact on performance.
Ground truth was derived from the measured images.
Each patient case was assigned a unique identifier and included only in either the testing or the training dataset, in order to ensure the independence of both datasets.
The demographic characteristics for the global dataset are presented in the table below.
| Degen model | Training set (No. of subjects=776) | Testing set (No. of subjects=274) | Overall (No. of subjects=1050) |
| --- | --- | --- | --- |
| Mean age, years (sd) | 62.4 (11.5) | 61.0 (12.7) | 62.0 (11.8) |
| Gender, N (%) | | | |
| Female | 389 (50%) | 127 (46%) | 516 (49%) |
| Male | 387 (50%) | 147 (54%) | 534 (51%) |
Table 2: Degenerative Model Demographics global dataset
Non-inferiority of the subject device vs the predicate device was evaluated: one-tailed paired T-tests for non-inferiority were performed between the MAEs obtained with the subject
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| | device and the ones obtained with the predicate device. The acceptance criterion for these statistical tests were set to alpha of 0.025, and at least 90% power.
The results from the degenerative predictive model performance testing met the defined acceptance criterion. The model showed non-inferiority compared to its predicate and is considered acceptable for use.
**Cybersecurity testing**
The cybersecurity testing was conducted on the UNiD™ Spine Analyzer in accordance with ANSI AAMI SW96 and IEC 81001-5-1 to ensure the software integrity, confidentiality and availability. Activities performed as part of cybersecurity testing included: security risk assessment and threat modeling, vulnerability assessment, and penetration testing.
**Usability evaluation**
Usability of the UNiD™ Spine Analyzer user interface was evaluated according to IEC 62366-1. The purpose was to assess the software ergonomics and ensure no usability issues could raise significant risks in terms of safety and effectiveness. |
| --- | --- |
| Clinical tests | No clinical testing was used in order to support this submission. |
| **VIII. Conclusion** | |
| Based on the information contained in this submission, the subject UNiD™ Spine Analyzer v5.0.0 device is substantially equivalent to the following predicate device:
— Predicate 1 (Primary Predicate): UNiD™ Spine Analyzer v4.0.0 (K212005, S.E. 01/12/2022) | |
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Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
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.
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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.
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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
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What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
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.