K231130 · SimBioSys, Inc. · QIH · Dec 26, 2023 · Radiology
Device Facts
Record ID
K231130
Device Name
TumorSight Viz
Applicant
SimBioSys, Inc.
Product Code
QIH · Radiology
Decision Date
Dec 26, 2023
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2050
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K231130 · Dec 26, 2023
TumorSight Viz
SimBioSys, Inc.
Retrospective clinical MRI datasets from 12 U.S. clinical sites; Routine clinical care dynamic contrast enhanced (DCE) MR protocols
The retrospective clinical dataset was used to train, tune, and validate the device's deep learning algorithm for breast MRI visualization and analysis. Performance was assessed by comparing device-generated measurements against radiologist-established ground truth and a predicate device.
Retrospective clinical data; Breast MRI; Algorithm validation; Routine clinical care
Clinical Evidence
Study Design
Population
Comparator
Key Endpoints
TumorSight Viz Performance Validation; Retrospective analysis of clinical MRI data
Patients with pathologically confirmed invasive, early-stage or locally advanced breast cancer; Sample Size: 897 patients (736 training/tuning, 161 validation); Number of Sites: 12 clinical sites in the U.S.
CADstream version 5
Mean absolute error of tumor volume and landmark distances (tumor-to-nipple, skin, chest); Dice coefficient for segmentation accuracy
TumorSight Viz is intended to be used in the visualization and analysis of breast magnetic resonance imaging (MRI) studies for patients with biopsy proven early-stage or locally advanced breast cancer. TumorSight Viz supports evaluation of dynamic MR data acquired from breast studies during contrast administration. TumorSight Viz performs processing functions (such as image registration, subtractions, measurements, 3D renderings, and reformats). TumorSight Viz also includes user-configurable features for visualizing and analyzing findings in breast MRI studies. Patient management decisions should not be made based solely on the results of TumorSight Viz.
Device Story
TumorSight Viz is a cloud-based software system for visualization and analysis of breast DCE-MRI studies. It ingests DICOM MRI data; processes images via deep learning algorithms to perform segmentation, registration, subtractions, and kinetic curve generation; and outputs 3D renderings, parametric maps, and quantitative measurements (tumor volume, dimensions, and distances to anatomical landmarks like nipple, skin, and chest). Used by radiologists in clinical settings to assist in pre-operative planning. The system provides automated measurements that are reviewed by the clinician; it does not replace clinical judgment. Benefits include standardized, reproducible tumor quantification and visualization to support surgical planning.
Clinical Evidence
Bench testing only. Performance validated on 163 samples from 6 U.S. sites, independent of 766 training/tuning samples. Ground truth established by consensus of 3 board-certified radiologists. Metrics included Mean Absolute Error (MAE) for tumor volume (6.48 ± 12.67 cc) and landmark distances (e.g., tumor-to-skin 0.63 ± 0.60 cm). Segmentation accuracy assessed via Dice coefficient (Volume Dice 0.676 ± 0.289; Surface Dice 0.873 ± 0.264). Comparison to predicate (CADstream) showed comparable performance to inter-radiologist variability.
Technological Characteristics
Software-based image processing system; DICOM compatible; cloud-hosted; accessed via off-the-shelf computer. Employs deep learning-based segmentation and automated measurement algorithms. Features include standard viewing tools, MIPs, reformats, image registration, kinetic curves, and parametric maps. No specific hardware materials; non-invasive software.
Indications for Use
Indicated for patients with biopsy-proven early-stage or locally advanced breast cancer undergoing breast MRI studies.
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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SimBioSys, Inc. % John J. Smith, M.D., J.D. Official Correspondent Hogan Lovells US LLP 180 North Lasalle Street. Suite 3250 Chicago, Illinois 60601
Re: K231130
Trade/Device Name: TumorSight Viz Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH Dated: November 21, 2023 Received: November 21, 2023
Dear John J. Smith:
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" (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).
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Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-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,
Wenbo Li for
Daniel M. Krainak, Ph.D. Assistant Director DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
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# Indications for Use
Submission Number (if known)
K231130
Device Name
TumorSight Viz
Indications for Use (Describe)
TumorSight Viz is intended to be used in the visualization and analysis of breast magnetic resonance imaging (MRI) studies for patients with biopsy proven early-stage or locally advanced breast cancer. TumorSight Viz supports evaluation of dynamic MR data acquired from breast studies during contrast administration. TumorSight Viz performs processing functions (such as image registration, subtractions, measurements, 3D renderings, and reformats).
TumorSight Viz also includes user-configurable features for visualizing and analyzing findings in breast MRI studies. Patient management decisions should not be made based solely on the results of TumorSight Viz.
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
Dver-The-Counter Use (21 CFR 801 Subpart C)
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## Submitter Details
SimBioSys, Inc. 180 North Lasalle Street, Suite 3250 Chicago IL 60601 United States Contact: Dr. John J. Smith, MD, JD Contact Telephone: (202) 637-3638 Contact Email: john.smith@hoganlovells.com
## Details of the Submitted Device
Proprietary Name: TumorSight Viz Common Name: Medical image management and processing system Classification Name: System, Image Processing, Radiological Regulation Number: 892.2050 Product Code: QIH Committee/Panel: Radiology Device Class: II
# Type of 510(k) Submission:
Traditional
### Identification of the Legally Marketed Predicate Device
Predicate #: K092954
Predicate Trade Name: CADstream Version 5
Product Code: LLZ
### Device Description
TumorSight Viz is an image processing system designed to assist in the visualization and analysis of breast DCE-MRI studies.
TumorSight reads DICOM magnetic resonance images. TumorSight processes and displays the results on the TumorSight web application.
Available features support:
- Visualization (standard image viewing tools, MIPs, and reformats)
- Analysis (registration, subtractions, kinetic curves, parametric image maps, segmentation and 3D ● volume rendering)
- . Communication and storage (DICOM import, retrieval, and study storage)
The TumorSight system consists of proprietary software developed by SimBioSys, Inc. hosted on a cloud-based platform and accessed on an off-the-shelf computer.
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#### Intended Use and Indications for Use
TumorSight Viz is intended to be used in the visualization and analysis of breast magnetic resonance imaging (MRI) studies for patients with biopsy proven early-stage or locally advanced breast cancer. TumorSight Viz supports evaluation of dynamic MR data acquired from breast studies during contrast administration. TumorSight Viz performs processing functions (such as image registration, subtractions, measurements, 3D renderings, and reformats).
TumorSight Viz also includes user-configurable features for visualizing and analyzing findings in breast MRI studies. Patient management decisions should not be made based solely on the results of TumorSight Viz.
#### Indications for Use Comparison
CADstream is intended to be used in the visualization, analysis, and reporting of magnetic resonance imaging (MRI) studies. CADstream supports evaluation of dynamic MR data acquired during contrast administration. CADstream performs other user selected processing functions (such as image registration, subtractions, measurements, 3D renderings, and reformats). Although the Indication for Use statement for Tumorsight Viz are not identical to that of the predicate, the differences do not alter the intended use as an image visualization device, nor do they affect the safety and effectiveness of the device relative to the predicate. Both the subject and predicate devices have the same intended use for the visualization and analysis of dynamic magnetic resonance imaging (MRI) studies.
#### Technological Characteristics
Visualization of dynamic magnetic resonance imaging (MRI) studies is the technological principle for both the subject and predicate devices. It is based on the use of dynamic MRI images in DICOM format which are to be viewed and analyzed by a skilled physician. Both the subject and predicate devices perform the following same technological features:
- . Standard Image Viewing Tools (zoom, pan, window/level)
- Image Post Processing (MIPs, reformats, image registration) ●
- Parametric Maps ●
- Kinetic Curves
- Automatic Volume Segmentation
- Automatic Linear Measurements (distance to nipple, chest, and closest skin surface) ●
- . DICOM Image Import
The following technological features differ between the subject and predicate devices:
- Ability to review additional imaging modalities (mammography and ultrasound)
- Interventional planning ●
- User created collage of study images ●
- Serial comparisons ●
- Customizable reporting ●
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## Performance Tests
SimBioSys has completed performance testing on an independent dataset to ensure TumorSight Viz meets clinically acceptable levels.
DCE-MRI were obtained from seven hundred thirty-six (736) patients (corresponding to 766 samples when accounting for bilateral disease) were obtained from twelve (12) clinical sites in the U.S. for use in training and tuning the device. DCE-MRI were obtained for one hundred sixty-one (161) patients (corresponding to 163 samples when accounting for bilateral disease) were obtained from six (6) clinical sites in the U.S. for use in validating the device. All patients had pathologically confirmed invasive, early stage or locally advanced breast cancer.
Data was collected to ensure adequate coverage of MRI manufacturer and field strength, and to ensure similarity with the broader population of early-stage and locally advanced breast cancer patients in the U.S. Specifically, patient age at diagnosis, breast cancer subtype, T stage, histologic subtype, and race/ethnicity all reflect the broader U.S. population.
| | Training Dataset<br>(n=390 samples) | Tuning Dataset<br>(n=376 samples) | Validation Dataset<br>(n=163 samples) |
|--------------------------------------|-------------------------------------|-----------------------------------|---------------------------------------|
| Age | | | |
| <30 | 10 (2.6%) | 9 (2.4%) | 4 (2.5%) |
| 30-39 | 72 (18.5%) | 62 (16.5%) | 33 (20.2%) |
| 40-49 | 103 (26.4%) | 104 (27.7%) | 37 (22.7%) |
| 50-59 | 117 (30.0%) | 109 (29.0%) | 48 (29.4%) |
| 60-69 | 65 (16.7%) | 66 (17.6%) | 32 (19.6%) |
| >70 | 21 (5.4%) | 26 (6.9%) | 9 (5.5%) |
| Missing | 2 (0.5%) | 0 (0.0%) | 0 (0.0%) |
| Race/Ethnicity | | | |
| Black† | 73 (18.7%) | 92 (24.5%) | 19 (11.7%) |
| Asian and Pacific Islander† | 20 (5.1%) | 17 (4.5%) | 8 (4.9%) |
| White† | 267 (68.5%) | 226 (60.1%) | 121 (74.2%) |
| American Indian or Alaska<br>Native† | 6 (1.5%) | 0 (0.0%) | 0 (0.0%) |
| Other | 9 (2.3%) | 3 (0.8%) | 2 (1.2%) |
| Hispanic | 0 (0.0%) | 8 (2.1%) | 6 (3.7%) |
| Missing/Unknown | 22 (5.6%) | 31 (8.2%) | 7 (4.3%) |
* Non-Hispanic
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The following subgroups present in the dataset were comparable to the U.S. population: cancer subtype, grade, histology, T stage, and N stage.
Images were acquired from sites that utilize standard of care dynamic contrast enhanced MR protocols from GE, Philips, and Siemens scanners with both 1.5T and 3T field strength magnets.
Seven (7) U.S. Board Certified radiologists reviewed 163 validation samples to establish the ground truth for the dataset according to predefined guidelines. For each case, two radiologists measured various characteristics about the cancer including longest dimensions along three axes and tumor to landmark (chest, nipple, skin) distances. Each study was reviewed by two radiologists to determine if the candidate segmentation was appropriate. In cases where the two radiologists did not agree on whether the segmentation was appropriate, a third radiologist provided an additional opinion and established a ground truth by majority consensus.
Independence of validation data from training data was ensured by confirming there was no overlap of patients between training/tuning and validation datasets.
The validation samples were tested using both the TumorSight Viz device and the CADstream device.
The measurements generated from the device result directly from the segmentation methodology and are an inferred reflection of the performance of the deep learning algorithm. For example, the distance from chest or skin is calculated after the deep learning segmentation identifies the region of interest and then the resulting measurement is output.
The mean absolute error and variability between the automated measurements (Validation Testing) and ground truth for tumor volume (measured in cc) and landmark distances (measured in cm) was similar to the variability between device-to-radiologist measurements and inter-radiologist variability. This demonstrates that the error in measurements is consistent to the variability between expert readers. Performance data for the automated measurements is summarized below:
| Measurement Description | Units | Validation Testing<br>(Mean Abs. Error ± Std.<br>Dev.) |
|--------------------------------------|------------------------|--------------------------------------------------------|
| Tumor Volume (n=157) | cubic centimeters (cc) | 6.48 ± 12.67 |
| Tumor-to-breast volume ratio (n=157) | % | 0.56 ± 0.93 |
| Tumor longest dimension (n=163) | centimeters (cm) | 1.48 ± 1.46 |
| Tumor-to-nipple distance (n=161) | centimeters (cm) | 1.00 ± 1.03 |
| Tumor-to-skin distance (n=163) | centimeters (cm) | 0.63 ± 0.60 |
| Tumor-to-chest distance (n=163) | centimeters (cm) | 0.94 ± 1.34 |
| Tumor center of mass (n=157) | centimeters (cm) | 0.735 ± 1.26 |
The tumor segmentation was assessed using the Dice coefficient, utilizing both the volumetric and surface Dice coefficients, which together validate the location, volume, and surface agreement with a reference standard.
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The surface Dice coefficient is particularly useful as a proxy for the accuracy of 3D rendering and surfaceto-surface distances. Additionally, to further assess the tumor segmentation localization accuracy, we used the distance between the centers of mass of the reference standards and device-generated regions.
Results of Dice and surface Dice are summarized below:
| Performance Measurement | Metric | Validation Testing<br>(Mean ± Std. Dev.) |
|----------------------------|--------------|------------------------------------------|
| Tumor segmentation (n=157) | Volume Dice | 0.676 ± 0.289 |
| | Surface Dice | 0.873 ± 0.264 |
We found that all tests met the acceptance criteria, demonstrating adequate performance for our intended use.
### Risk Management
The device risks were managed and controlled following the requirements of ISO 14971 standard. The device hazards were identified, their risk levels were evaluated and mitigation measures were taken to reduce the risk levels. The benefits of the TumorSight Viz software, outweigh the device residual risks.
#### Substantial Equivalence
TumorSight Viz is comparable to the predicate in terms of intended use, technological characteristics, and principle of operation.
| Predicate Device Comparison | | |
|------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| | CADstream version 5<br>(predicate) | TumorSight Viz |
| 510(k) | K092954 | TBD |
| Manufacturer | Merge CAD Inc. | SimBioSys Inc. |
| Regulation Number | 892.2050 | 892.2050 |
| Regulation Name | Medical image management and<br>processing system | Medical image management and<br>processing system |
| Classification | 2 | 2 |
| Device Common Name | Image Processing System | Image Processing System |
| Product Code | LLZ | QIH |
| Functions | - Extract dynamic contrast<br>enhanced MRI sequence from<br>MRI images for the 3D display<br>and visualization of the anatomy<br>of patient's breast | - Extract dynamic contrast<br>enhanced MRI sequence from<br>MRI images for the 3D display<br>and visualization of the anatomy<br>of patient's breast |
| | CADstream is intended to be<br>used in the visualization,<br>analysis, and reporting of<br>magnetic resonance imaging<br>(MRI) studies. CADstream<br>supports evaluation of dynamic<br>MR data acquired during<br>contrast administration.<br>CADstream performs other user<br>selected processing functions<br>(such as image registration,<br>subtractions, measurements, 3D<br>renderings, and reformats).<br><br>CADstream also includes user-<br>configurable features for<br>reporting on findings in breast or<br>general MRI studies.<br>Additionally, CADstream assists<br>users in planning MRM guided<br>interventional procedures.<br><br>When interpreted by a skilled<br>physician, this device provides<br>information that may be used for<br>screening, diagnosis, and<br>interventional planning. Patient<br>management decisions should<br>not be made based solely on<br>theresults of CADstream.<br><br>CADstream may also be used as<br>an image viewer of multi-<br>modality, digital images,<br>including ultrasound and<br>mammography. CADstream is<br>not intended for primary<br>interpretation of digital<br>mammography images. | TumorSight Viz is intended to<br>be used in the visualization and<br>analysis of breast magnetic<br>resonance imaging (MRI)<br>studies for patients with biopsy<br>proven early-stage or locally<br>advanced breast cancer.<br>TumorSight Viz supports<br>evaluation of dynamic MR data<br>acquired from breast<br>studiesduring contrast<br>administration. TumorSight Viz<br>performs processing functions<br>(such as image registration,<br>subtractions, measurements, 3D<br>renderings, and reformats).<br><br>TumorSight Viz also includes<br>user-configurable features for<br>visualizing and analyzing<br>findings in breast MRI studies.<br>Patient management decisions<br>should not be made based solely<br>on the results of TumorSight<br>Viz. |
| Intended Use | | |
| Data Source (Input) | MRI | MRI |
| Output/Accessibility | Graphic and text results of<br>breast anatomy are accessed via<br>a device with internet<br>connectivity | Graphic and text results of<br>breast anatomy are accessed via<br>a device with internet<br>connectivity |
| Physical Characteristics | "-non-invasive software package<br>-DICOM compatible" | "-non-invasive software package<br>-DICOM compatible" |
| Safety | Clinician review and assessment<br>of analysis prior to use in<br>planning MRI guided<br>interventional procedures. | Clinician review and assessment<br>of analysis prior to use in pre-<br>operative planning. |
| | Predicate Device Feature Comparison | |
| Feature | CADstream version 5<br>(predicate) | TumorSight Viz |
| Standard image viewing tools | Yes | Yes |
| MIPs | Yes | Yes |
| Reformats | Yes | Yes |
| Registration | Yes | Yes |
| Subtraction series | Yes | Yes |
| View 3D volume rendering | Yes | Yes |
| Kinetic curves | Yes | Yes |
| Parametric image maps | Yes | Yes |
| DICOM import | Yes | Yes |
| View finding volume | Yes | Yes |
| View finding location | Yes | Yes |
| View finding size | Yes | Yes |
| View kinetic curve with<br>highest uptake | Yes…
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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.