MR Diffusion Perfusion Mismatch V1.0 is an automatic calculation tool indicated for use in radiology. The device is an image processing software allowing computation of parametric maps from (1) MR Diffusion-weighted imaging (DWI) and (2) MR Perfusion-weighted imaging (PWI) and extraction of volumes of interest based on numerical thresholds applied to the aforementioned maps. Computation of mismatch between extracted volumes is automatically provided. The device is intended to assist trained radiologists and surgeons in the imaging assessment workflow by communication of metrics from MR Diffusion-weighted imaging (DWI) and MR Perfusion-weighted imaging (PWI). The results of MR Diffusion Perfusion Mismatch V1.0 are intended to be used in conjunction with other patient information and, based on professional judgment, to assist the clinician in the medical imaging assessment. Trained radiologists and surgeons are responsible for viewing the full set of native images per the standard of care. The device does not alter the original mage. MR Diffusion Perfusion Mismatch V1.0 is not intended to be used as a standalone diagnostic device and shall not be used to take decisions with diagnosis or therapeutic purposes. Patient management decisions should not solely be based on MR Diffusion Perfusion Mismatch V1.0 results. MR Diffusion Perfusion Mismatch V1.0 can be integrated and deployed through technical platforms, responsible for transfering, storing, converting formats, notifying of detected image variations and display of DICOM imaging data.
Device Story
Software module for MR image processing; operates as a docker container on a technical platform (Medical Image Communications Device). Inputs: MR Diffusion-weighted (DWI), MR Perfusion-weighted (PWI), and optional MR FLAIR images. Processing: automatic identification of series via DICOM tags; motion correction; brain extraction/segmentation; computation of parametric maps (ADC, CBF, CBV, MTT, tMIP, TTP, Tmax) and mismatch volumes. Outputs: parametric maps, segmented volumes, and mismatch metrics. Used in hospitals/imaging centers by radiologists and surgeons. Provides quantitative/qualitative metrics to assist clinical assessment; does not alter original images. Benefits: automates complex volumetric calculations to support standard-of-care imaging workflows.
Clinical Evidence
Comparative clinical image study using 30 cases. Compared subject device against predicate Olea Sphere V3.0. Metrics: Pearson/Spearman correlation (>0.8 for ADC, CBF, CBV, MTT, tMIP), DICE index (0.96 for Volume 1; 0.75 for Volume 2), and Bland-Altman analysis for bias. Results showed statistical equivalence and visual equivalence confirmed by US board-certified neuroradiologist. No new clinical safety concerns identified.
Technological Characteristics
Software-only image processing module (docker container). Operates on DICOM imaging data. Features: 3D rigid motion correction; AI-based Diffusion Brain Extraction Tool (BET) and Perfusion Background Segmentation. Outputs parametric maps and volumetric segmentations. Standalone software module requiring integration with a Medical Image Communications Device (MICD) for data transfer, storage, and visualization.
Indications for Use
Indicated for use in radiology to assist trained radiologists and surgeons in imaging assessment workflows by computing parametric maps and extracting volumes of interest from MR Diffusion-weighted (DWI) and Perfusion-weighted (PWI) imaging. Not for standalone diagnosis or sole basis for patient management decisions.
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 shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
January 13, 2023
Olea Medical % John J. Smith Partner Hogan Lovells US LLP Columbia Square 555 Thirteenth Street, NW WASHINGTON DC 20004
Re: K223502
Trade/Device Name: MR Diffusion Perfusion Mismatch V1.0 Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: LLZ Dated: November 21, 2022 Received: November 21, 2022
Dear John 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 (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
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Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-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.
D. Ryk
Daniel M. Krainak, Ph.D. Assistant Director Magnetic Resonance and Nuclear Medicine Team 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
Enclosure
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510(k) Number (if known) K223502
Device Name
#### MR Diffusion Perfusion Mismatch V1.0
MR Diffusion Perfusion Mismatch V1.0 is an automatic calculation tool indicated for use in radiology. The device is an image processing software allowing computation of parametric maps from (1) MR Diffusion-weighted imaging (DWI) and (2) MR Perfusion-weighted imaging (PWI) and extraction of volumes of interest based on numerical thresholds applied to the aforementioned maps. Computation of mismatch between extracted volumes is automatically provided.
The device is intended to assist trained radiologists and surgeons in the imaging assessment workflow by communication of metrics from MR Diffusion-weighted imaging (DWI) and MR Perfusion-weighted imaging (PWI).
The results of MR Diffusion Perfusion Mismatch V1.0 are intended to be used in conjunction with other patient information and, based on professional judgment, to assist the clinician in the medical imaging assessment. Trained radiologists and surgeons are responsible for viewing the full set of native images per the standard of care.
The device does not alter the original mage. MR Diffusion Perfusion Mismatch V1.0 is not intended to be used as a standalone diagnostic device and shall not be used to take decisions with diagnosis or therapeutic purposes. Patient management decisions should not solely be based on MR Diffusion Perfusion Mismatch V1.0 results.
MR Diffusion Perfusion Mismatch V1.0 can be integrated and deployed through technical platforms, responsible for transfering, storing, converting formats, notifying of detected image variations and display of DICOM imaging data.
Type of Use (Select one or both, as applicable)
区 Prescription Use (Part 21 CFR 801 Subpart D)
□ Over-The-Counter Use (21 CFR 801 Subpart C)
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### 510(k) SUMMARY
### Olea Medical's MR Diffusion Perfusion Mismatch V1.0
### Submitter
Olea Medical 93 avenue des Sorbiers, ZI ATHELIA IV 13600, La Ciotat France Phone: +33 4 42 71 24 20 Facsimile: +33 4 42 71 24 27 Contact Person: Nathalie Palumbo
Date Prepared: January 9, 2023
Name of Device: MR Diffusion Perfusion Mismatch V1.0
Common or Usual Name: Picture archiving and communication system (PACS)
Regulation Name: Medical Image Management and Processing System
Regulatory Class: 21 CFR 892.2050
Product Code: LLZ
Predicate Device: Olea Sphere V3.0 (K152602)
#### Indications for Use
MR Diffusion Perfusion Mismatch V1.0 is an automatic calculation tool indicated for use in radiology. The device is an image processing software allowing computation of parametric maps from (1) MR Diffusion-weighted imaging (DWI) and (2) MR Perfusion-weighted imaging (PWI) and extraction of volumes of interest based on numerical thresholds applied to the aforementioned maps. Computation of mismatch between extracted volumes is automatically provided.
The device is intended to assist trained radiologists and surgeons in the imaging assessment workflow by extraction and communication of metrics from MR Diffusion-weighted imaging (DWI) and MR Perfusion-weighted imaging (PWI).
The results of MR Diffusion Perfusion Mismatch V1.0 are intended to be used in conjunction with other patient information and, based on professional judgment, to assist the clinician in the medical imaging assessment. Trained radiologists and surgeons are responsible for viewing the full set of native images per the standard of care.
The device does not alter the original medical image. MR Diffusion Mismatch V1.0 is not intended to be used as a standalone diagnostic device and shall not be used to make decisions with diagnosis or therapeutic purposes. Patient management decisions should not solely be based on MR Diffusion Perfusion Mismatch V1.0 results.
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MR Diffusion Perfusion Mismatch V1.0 can be integrated and deployed through technical platforms, responsible for transferring, storing, converting formats, notifying of detected image variations and display of DICOM imaging data.
## Device Description
# Introduction
The MR Diffusion Perfusion Mismatch V1.0 application can be used to automatically compute gualitative as well as quantitative perfusion maps based on the dynamic (first-pass) effect of a contrast agent (CA). The perfusion application assumes that the input data describes a well-defined and transient signal response following rapid administration of a contrast agent.
Olea Medical proposes MR Diffusion Perfusion Mismatch V1.0 as an image processing application, Picture Archiving Communications System (PACS) software module that is intended for use in a technical environment which incorporates a Medical Image Communications Device as its technical platform.
## MR Diffusion Perfusion Mismatch V1.0 interaction with the technical platform
To be used, the docker needs a technical base, which is provided by a Medical Image Communications Device (MICD). The technical platform allows the docker to:
- . receive the inputs
- provide the outputs ●
- visualize the outputs as the docker has no interface.
The technical base can support several applications encapsuled in a docker such as MR Diffusion Perfusion Mismatch V1.0.
## MR Diffusion Perfusion Mismatch V1.0 principle of operations and technological characteristics
MR Diffusion Perfusion Mismatch V1.0 image processing application is designed as a docker installed on a technical platform (i.e., a Medical Image Communications Device), as depicted below.
Image /page/4/Figure/13 description: The image shows a diagram of the MR Diffusion Perfusion Mismatch V1.0 application. The inputs to the application are MR Diffusion data, MR Perfusion data, and MR FLAIR data (optional). The outputs of the application are parametric maps, volumes segmentation, and mismatch computation.
MR Diffusion Perfusion Mismatch V1.0 inputs/outputs
MR Diffusion Perfusion Mismatch V1.0 is a docker totally independent from the technical platform in which it is integrated:
- Input DICOM images are received by the technical platform;
- Relevant series are automatically identified among the images received in the platform by reading ● the relevant DICOM tags and are provided to the application;
- The arrival of identified series will launch MR Diffusion Perfusion Mismatch V1.0;
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- The MR Diffusion Perfusion Mismatch V1.0 processes the identified series:
- The generated results are automatically pushed to the technical platform; ●
- The results can be visualized with any DICOM viewer comprising but not limited to the technical platform or exported to dedicated file location.
### Substantial Equivalence
#### Substantial equivalence comparison table
| MR Diffusion Perfusion Mismatch V1.0 | Olea Sphere® V3.0 (K152602) |
|--------------------------------------|-----------------------------|
| Parametric maps | YES |
| Volumes segmentation | YES |
| Mismatch computation | YES |
MR Diffusion Perfusion Mismatch V1.0 represents a subset of the predicate Olea Sphere V3.0. Comparative performance testing was conducted using the comparable Diffusion. Perfusion and Analysis modules in Olea Sphere V3.0.
Both MR Diffusion Perfusion Mismatch V1.0 and Olea Sphere® V3.0 are user-defined software analysis tools used for the analysis of MR studies. Both devices are intended for use in hospitals and imaging centers. Importantly, neither software product is used for diagnosis. Patient management decisions should not be based solely on the results of either software. Therefore, the intended use of the software is the same.
Both the MR Diffusion Perfusion Mismatch V1.0 and Olea Sphere V3.0 have similar technological characteristics as they both:
- . provide processing capabilities for the analysis of MR series;
- . are designed to be able to process MR series:
- . are able to provide same outputs:
- are able to automatically compute the mismatch between extracted volumes. .
The three minor differences between the two devices are:
- 1. MR Diffusion Perfusion Mismatch V1.0 represents a subset of Olea Sphere V3.0. Accordingly, the indications for use of the subject device, MR Diffusion Mismatch V1.0, is considered substantially equivalent to Diffusion, Perfusion and Analysis modules of the predicate, Olea Sphere V3.0. Olea Sphere® V3.0 provides other processing capabilities both on MR and CT, whereas the MR Diffusion Perfusion Mismatch V1.0 only analyzes MR images.
- 2. Olea Sphere V3.0 is equipped with a visualization interface, whereas MR Diffusion Perfusion Mismatch V1.0 needs to communicate with the technical platform to visualize the outputs.
- 3. The following are differences in the algorithms applied prior to maps computation:
- . MR Diffusion Perfusion Mismatch V1.0 uses a motion correction algorithm based on a 3D rigid method, while Olea Sphere® V3.0 uses a 2D rigid motion correction algorithm. This difference does not impact the calculation method of the outputs.
- . MR Diffusion Perfusion Mismatch V1.0 uses a Diffusion Brain Extraction Tool (BET) and a Perfusion Background Segmentation step based on Al algorithms. This difference does not impact the calculation method of the outputs.
In sum, these minor differences do not raise new questions of safety or effectiveness of the subject device.
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### Performance Data
Olea Medical has conducted extensive validation testing of the MR Diffusion Perfusion Mismatch V1.0. Internal verification and validation testing confirm that the product specifications are met and supports substantial equivalence of the intended use and technological characteristics to the predicate device.
MR Diffusion Perfusion Mismatch V1.0 has been validated to ensure that the system meets all performance specifications necessary to operate according to its intended use and in a manner substantially equivalent to the predicate device.
The following performance evaluations were conducted:
- . Product risk assessment;
- Software modules verification tests; ●
- Software validation test; ●
- . Comparative clinical image study
Based on the clinical performance as documented in a comparative clinical image study, the MR Diffusion Perfusion Mismatch V1.0 has a safety and effectiveness profile that is similar to the predicate device. Testing results are summarized below:
- . Parametric maps result comparison: ADC, CBF, CBV, MTT and tMIP parametric maps computed with MR Diffusion Perfusion Mismatch V1.0 and Olea Sphere® V3.0 were statistically equivalent with Pearson and Spearman correlation coefficients greater than 0.8, while TTP and Tmax temporal maps did not meet the acceptance criteria. Indeed, TTP and Tmax values are more prone to differences induced by slight variations because these parameters depend on the acquisition grid. However, the qualitative assessment allowed an US board-certified neuroradiologist to conclude that all parametric maps were substantially equivalent.
- Volume 1: Bland-Altman analysis showed that the average estimated bias (average of . differences) was close to zero (-0.33 ml), with 95% of the measurement differences ranging between -1.83 ml and +1.16 ml. These values were considered as acceptable, since they are within the range of inter-software variability reported in the literature for threshold-based volume segmentations. Moreover, they were also considered as acceptable according to an US board-certified neuroradiologist.
Mean DICE index (similarity coefficient) was excellent and equal to 0.96 between MR Diffusion Perfusion Mismatch V1.0 and Olea Sphere® V3.0 Volume_1 segmentations. Moreover, the absolute mean of the differences was close to zero (0.63 ml). Scatterplot linear regression (excellent correlation with R2 = 0.99) and boxplots supported these findings. Finally, the appraisal performed by an US board-certified neuroradiologist led to the conclusion that Volume 1 was visually equivalent for all 30 cases.
- . Volume 2: Bland-Altman analysis showed that the average estimated bias (average of differences) was small (-3.74 ml), with 95% of the measurement differences ranging between -33.59 ml and +26.10 ml. These values were considered as acceptable, since they are within the range of inter-software variability reported in the literature for threshold-based volume
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segmentations. Moreover, they were also considered as acceptable according to an US boardcertified neuroradiologist.
The variability of Volume 2 between both devices was reflected by a 0.75 mean DICE index. Moreover, the absolute mean of the differences remained low (11.77 ml), which is acceptable according to an US board-certified neuroradiologist, and the scatterplot linear regression showed an excellent correlation (R2 = 0.95). Moreover, the visual inspection performed by an US board-certified neuroradiologist led to the conclusion that Volume 2 was equivalent for all 30 cases.
- . Mismatch Ratio: Bland-Altman analysis showed that the average estimated bias was close to zero (0.88) with 95% of the measurement differences ranging between -11.01 and +12.77, which is an acceptable range according to an US board-certified neuroradiologist. The absolute mean of the differences was 1.87, which is acceptable according to an US boardcertified neuroradiologist.
- . Mismatch Volume: Bland-Altman analysis showed that the average estimated bias was -3.09 ml, with 95% of the measurement differences ranging between -32.82 ml and +26.64 ml, which is an acceptable range according to an US board-certified neuroradiologist. The absolute mean of the differences was 11.81 ml, which is acceptable according to an US board-certified neuroradiologist.
- Relative Mismatch: Bland-Altman analysis showed that the average estimated bias was -6.57 ● %, with 95% of the measurement differences ranging between -57.28 % and +44.15 %, which is an acceptable range according to an US board-certified neuroradiologist. The absolute mean of the differences was 13.21 %, which is acceptable according to an US board-certified neuroradiologist.
## Conclusions
MR Diffusion Perfusion Mismatch V1.0 is substantially equivalent to the predicate device, Olea Sphere® V3.0. The MR Diffusion Perfusion Mismatch V1.0 has the same intended use and similar indications for use, technological characteristics, and principles of operation as its predicate device.
In addition, the minor technological differences between the MR Diffusion Mismatch V1.0 and its predicate devices raise no new questions of safety or effectiveness. Performance data demonstrate that the MR Diffusion Perfusion Mismatch V1.0 is as safe and effective as the Olea Sphere® V3.0. Thus, the MR Diffusion Perfusion Mismatch V1.0 is substantially equivalent.
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.