145 CT image studies for bone identification; 130 CT image studies for metal identification
—
82 CT image studies
3 (U.S. Orthopedic surgeons)
Indications for Use
Bonelogic software is to be used by orthopaedic healthcare professionals for diagnosis and surgical planning in a hospital or clinic environment. Bonelogic software provides: - Semi-automatic segmentation with manual or assisted input of bony structure identification from CT imaging input, - Three-dimensional mathematical models of the anatomical structures of foot and ankle, - Measurement templates containing radiographic measures of foot and tools for manually obtaining linear and angular measurements, - Surgical planning application for foot and ankle using three-dimensional models of the anatomical structures and radiographic measures. The three-dimensional models of the anatomical structures combined with the measurements can be used for the diagnosis of orthopaedic healthcare conditions. The surgication containing the three-dimensional structural models combined with the measurements can be used for the planning of treatments and operations to correct orthopaedic healthcare conditions of foot and ankle.
Device Story
Bonelogic 2.2 is a software tool for orthopaedic diagnosis and surgical planning; processes CT imaging inputs to segment foot and ankle bone anatomy. Operates via semi-automatic workflow requiring manual user input or optional assisted input using a locked artificial neural network (ANN) model. Produces 3D mathematical models of anatomical structures and measurement templates for linear/angular radiographic assessment. Used by orthopaedic healthcare professionals in hospitals or clinics. Healthcare providers use 3D models and measurements to diagnose conditions and plan surgical interventions. Benefits include improved anatomical visualization and standardized measurement for treatment planning.
Clinical Evidence
Bench testing and software verification/validation performed. Clinical performance assessment of AI algorithms conducted on 82 CT image series. Results: 100% correct bone identification; metal identification specificity 98%, sensitivity 100%. Training/tuning data (145 CT studies for bone, 130 for metal) independent from test data. Ground truth established by three U.S. orthopaedic surgeons via majority vote. Test data included diverse demographics, imaging devices (Curvebeam, Planmed, Carestream, Toshiba), and clinical conditions (Hallux Valgus, fractures, implants).
Technological Characteristics
Software-based medical image management and processing system. Inputs: CT DICOM images. Processing: Semi-automatic segmentation with manual or optional locked ANN-based assisted input. Outputs: 3D anatomical models and radiographic measurements. Connectivity: Standalone software. No specific materials or sterilization required as it is non-patient contacting software.
Indications for Use
Indicated for orthopaedic healthcare professionals for diagnosis and surgical planning of foot and ankle conditions in hospital or clinic settings using CT imaging.
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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Image /page/0/Picture/0 description: The image contains two logos. On the left is the Department of Health & Human Services logo. On the right is the FDA logo, which is a blue square with the letters "FDA" in white, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Disior Ltd % Alex Cadotte Associate Director, Software and Digital Health Regulatory Affairs Mcra. LLC 1050 K St NW Suite 1000 Washington, DC 20001
Re: K223757
December 8, 2023
Trade/Device Name: Bonelogic Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH Dated: November 7, 2023 Received: November 13, 2023
Dear Alex Cadotte:
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.
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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).
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-regulatory
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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,
Jessica Lamb
Jessica Lamb Assistant Director Iamging Sofware 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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## Indications for Use
510(k) Number (if known) K223757
Device Name Bonelogic 2.2
#### Indications for Use (Describe)
Bonelogic software is to be used by orthopaedic healthcare professionals for diagnosis and surgical planning in a hospital or clinic environment.
Bonelogic software provides:
- · Semi-automatic segmentation with manual or assisted input of bony structure identification from CT imaging input,
- · Three-dimensional mathematical models of the anatomical structures of foot and ankle,
• Measurement templates containing radiographic measures of foot and tools for manually obtaining linear and angular measurements,
• Surgical planning application for foot and ankle using three-dimensional models of the anatomical structures and radiographic measures.
The three-dimensional models of the anatomical structures combined with the measurements can be used for the diagnosis of orthopaedic healthcare conditions. The surgication containing the three-dimensional structural models combined with the measurements can be used for the planning of treatments and operations to correct orthopaedic healthcare conditions of foot and ankle.
| Type of Use (Select one or both, as applicable) | |
|-------------------------------------------------|--|
|-------------------------------------------------|--|
X Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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# 510(k) Summary
| Device Trade Name: | Bonelogic 2.2 | | | |
|--------------------|---------------------------------------------------------------------------------------------------------|--|--|--|
| Manufacturer: | Disior Ltd<br>HTC Helsinki, Building PINTA, 4th floor<br>Tammasaarenkatu 3<br>Helsinki, Finland 00180 | | | |
| Contact: | Aarno Jussila<br>Director of Enabling Technologies<br>Phone: +358 40 6734939<br>Email: aarno@disior.com | | | |
| Prepared by: | Alex Cadotte<br>MCRA, LLC<br>803 7th St NW<br>Washington, DC 20001<br>Office: 202.552.5800 | | | |
| Date Prepared: | December 7, 2023 | | | |
| Classifications: | 21 CFR 892.2050 Medical image management and processing<br>system. | | | |
| Class: | II | | | |
| Product Code: | QIH | | | |
| Primary Predicate: | K203290 | | | |
### Indications For Use:
Bonelogic software is to be used by orthopaedic healthcare professionals for diagnosis and surgical planning in a hospital or clinic environment.
Bonelogic software provides:
- Semi-automatic segmentation with manual or assisted input of bony structure ● identification from CT imaging input,
- Three-dimensional mathematical models of the anatomical structures of foot and ankle, ●
- Measurement templates containing radiographic measures of foot and ankle, and tools ● for manually obtaining linear and angular measurements,
- Surgical planning application for foot and ankle using three-dimensional models of the . anatomical structures and radiographic measures.
The three-dimensional models of the anatomical structures combined with the measurements can be used for the diagnosis of orthopaedic healthcare conditions. The surgical planning application containing the three-dimensional structural models combined with the
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measurements can be used for the planning of treatments and operations to correct orthopaedic healthcare conditions of foot and ankle.
#### Device Description:
The Bonelogic is a software tool that segments bone anatomy using dedicated semiautomatic tools and fully automatic algorithms. More specifically, Bonelogic is intended to segment foot and ankle bones from computed tomography (CT) images. The segmented structures may then be used to create 3D models of their respective bones and replicate the anatomy of a patient. The semi-automatic tools of the software require a healthcare professional to mark the different bones in an initial 3D rendered model prior to when the segmentation process is initialized. This method is called the semi-automatic workflow with manual input. The software also comprises an optional semiautomatic workflow with assisted input that replaces the required user input with an estimate based on a locked artificial neural network (ANN) model. The fully automatic algorithm processes the final result in the same way based on input generated by the semi-automatic workflow with user input and with ANN model. The user still needs to mark the laterality and as a new step acknowledge the bones discovered by the ANN model.
#### Predicate Device:
Disior submits the following information in this Premarket Notification to demonstrate that, for the purposes of FDA's regulation of medical devices. K223757 is substantially equivalent in indications, design principles, and performance to the following subject device:
| | Predicate Device K203290 | Subject Device K223757 | |
|---------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Information | (Bonelogic) | (Bonelogic 2.2) | Comparison |
| Classification Name | Medical image management<br>and processing system | Medical image management<br>and processing system | Identical |
| Service Type | Software | Software | Identical |
| Classification | 21 CFR 892.2050 | 21 CFR 892.2050 | Identical |
| Class | II | II | Identical |
| Product Code | LLZ | QIH | Identical |
| Indications for Use | Bonelogic software is intended<br>to be used by specialized<br>medical practitioners to assist<br>in the characterization of<br>human anatomy with 3D<br>visualization and specific<br>measurements. The medical<br>image modalities intended to<br>be used in the software are<br>computed tomography (CT)<br>images, cone beam computed<br>tomography (CBCT) images<br>and weight-bearing cone beam<br>CT (WBCT) images. The<br>intended patient population is<br>adults over 16 years of age.<br>Bonelogic software contains<br>the measurement template | Bonelogic software is to be<br>used by orthopaedic healthcare<br>professionals for diagnosis and<br>surgical planning in a hospital<br>or clinic environment.<br>Bonelogic software provides:<br>Semi-automatic segmentation<br>with manual or assisted input<br>of bony structure identification<br>from CT imaging input,<br>Three-dimensional<br>mathematical models of the<br>anatomical structures of foot<br>and ankle,<br>Measurement templates<br>containing radiographic<br>measures of foot and ankle,<br>and tools for manually | Removal of wrist<br>and hand related<br>functionality and<br>addition of an<br>optional<br>semiautomatic<br>segmentation<br>workflow with<br>assisted bone<br>identification<br>process in the<br>subject device |
#### Substantial Equivalence:
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| Information | Predicate Device K203290<br>(Bonelogic) | Subject Device K223757<br>(Bonelogic 2.2) | Comparison |
|---------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------|
| | with a set of distance and<br>angular measures. The<br>measurements can be used for<br>diagnostic purposes. The<br>three-dimensional (3D)<br>models are displayed and can<br>be manipulated in the<br>software. Together, the<br>information from the<br>measurements and the 3D<br>visualization can be used for<br>treatment planning in the field<br>of orthopedics (foot and ankle,<br>and hand and wrist). The 3D<br>models can be outputted from<br>the software for traditional or<br>additive manufacturing. The<br>physical models generated<br>based on the 3D digital models<br>are not intended for diagnostic<br>use. | obtaining linear and angular<br>measurements,<br>Surgical planning application<br>for foot and ankle using three-<br>dimensional models of the<br>anatomical structures and<br>radiographic measures.<br>The three-dimensional models<br>of the anatomical structures<br>combined with the<br>measurements can be used for<br>the diagnosis of orthopaedic<br>healthcare conditions. The<br>surgical planning application<br>containing the three-<br>dimensional structural models<br>combined with the<br>measurements can be used for<br>the planning of treatments and<br>operations to correct<br>orthopaedic healthcare<br>conditions of foot and ankle. | |
| Input | Computed tomography<br>DICOM images | Computed tomography<br>DICOM Computed<br>tomography | Identical |
| Image processing | Segmentation of bone<br>structures | Segmentation of bone<br>structures | Identical |
| Output | 3D model of patient anatomy | 3D model of patient anatomy | Identical |
| Measuring and<br>planning | Perform measurements for<br>presurgical planning | Perform measurements for<br>presurgical planning | Identical |
| Bone identification | Manual process | Manual and an optional<br>semiautomatic workflow with<br>assisted input process<br>performed by artificial neural<br>network | Optional<br>semiautomatic<br>bone<br>identification<br>process in |
### Performance Testing Summary:
- Software verification and validation were carried out based on the "Guidance for . the Content of Premarket Submissions for Software Contained in Medical Devices" at the unit, integration, and system levels to determine substantial equivalence to the predicate device. The predicate device meets the subject device's established acceptance criteria of 95% model conformance within 1.0mm distance to reference model and 2.0 degrees standard deviation for angular measurements.
- Bench testing Software verification and validation for Bonelogic software ● demonstrated its substantial equivalence to the predicate device. An additional clinical data based software performance assessment study was carried out to validate the standalone performance of AI algorithms from a clinical perspective. The testing for 82 CT image series presented 100% correctly identified bones of foot and ankle. The existence of metal was identified correctly for 98.8% of the images (specificity 98%, sensitivity 100%).
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#### Study subjects:
.
The AI algorithm for bone identification was developed using 145 CT image studies and metal identification was developed using 130 CT image studies. Testing was carried out using 82 CT image studies. Out of 357 CT image studies, 340 were from individual patients with few studies from same patient with different foot alignments. The CT image series' were collected from various sites across USA and Europe with a minimum of 50% of the images originating from the USA. The CT image studies were from patients with different ages and racial groups, with minimum of 35% male/female within each dataset, with mean age approximately 47 years (SD 15 years), and representatives from White, (Non-)Hispanic. African American, and Native racial groups. Each dataset was balanced in terms of subjects with different foot alignment, demographics, imaging devices and with subjects from clinical subgroups ranging from control/normal feet (44% with test data) to pre-/post-operative clinical conditions such as Hallux Valgus, Progressive Collapsing Foot Deformity, fractures, or with metal implants (40% of the test data).
- Imaging Systems:
The 357 image studies were collected using CT imaging system made by five (5) manufacturers (7 different models in total). From the test data of 82 images, 61% of the images were acquired using Curvebeam PedCAT, 11% with Planmed Verify, and 26% with Carestream OnSight 3D Extremity. In addition, system test data contains images acquired with Toshiba Somatom. Typical imaging protocol is disclosed within the IFU, however, the test data contains wider range of parameters for generalization (tube voltages between 90-120 kV, tube currents 5-8 mA, and slice thickness/pixel spacing 0.37-1.5mm).
- Ground Truth: ●
The ground truths for bone and metal identification were independently established by three (3) U.S. Orthopedic surgeons with a 3rd party software. Each clinicians reviewed each of the DICOM series through axial/sagittal/coronal views and/or 3D reconstruction and marked on a spreadsheet the presence of a bone and metal in the image series. Based on the majority vote of three, two same responses were required to establish a ground truth on each of the DICOM series.
- Training, Tuning, and Validation Data Independence:
The Bonelogic software machine learning algorithm training and tuning data used during the algorithm development, as well as test data used in the standalone software performance assessment study, were all independent data sets. Each CT image study was allowed to be allocated to only data set.
{8}------------------------------------------------
#### Conclusion:
The subject device and the predicate devices have intended use and have similar technological characteristics. The data included in this submission demonstrate substantial equivalence to the predicate devices listed above. Bonelogic 2.2 is as safe, as effective, and performs as well as, or better, than the predicate devices.
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Part 1 — Search, results, and everyday workflows 16 min
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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.
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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.
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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.
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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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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.
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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.