K201310 · Maxq AL , Ltd. · QAS · Aug 7, 2020 · Radiology
Device Facts
Record ID
K201310
Device Name
Accipiolx
Applicant
Maxq AL , Ltd.
Product Code
QAS · Radiology
Decision Date
Aug 7, 2020
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2080
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
K201310 · Aug 7, 2020
Accipiolx
Maxq AL , Ltd.
Retrospective clinical CT cases from multiple sites
A retrospective study of 360 clinical cases was conducted to evaluate the sensitivity, specificity, and processing time of the Accipiolx device in identifying acute intracranial hemorrhage.
Retrospective study; Clinical performance; Acute intracranial hemorrhage; Head CT
Clinical Evidence
Study Design
Population
Comparator
Key Endpoints
Retrospective study
Adult patients indicated for non-contrast head CT; Sample Size: 360 cases; Number of Sites: Multiple sites across 17 US states
Not applicable for this study
Sensitivity, Specificity, NPV, PPV, and per-case processing time
Training CT cases collected from multiple institutions and CT manufacturers including pilot development, optimization, and threshold selection.
—
Retrospective study of 360 cases collected from multiple sites across 17 US states.
—
Indications for Use
Accipiolx is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage in the acute care environment. Accipiolx analyzes cases using an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. Accipiolx is not intended to direct attention to specific portions of an image or to anomalies other than acute intracranial hemorrhage. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT cases.
Device Story
Accipiolx is a DICOM-compliant software workflow tool for acute care settings; it operates in parallel to standard radiology workflows. The device receives non-contrast head CT (NCCT) scans via a Medical Image Communications Device (MICD) like the Accipio Agent. It uses a deep learning-based AI algorithm to perform skull stripping, image registration, tissue segmentation, and feature identification (hyperdensity detection). The device generates a case-level output indicating suspected aICH, which is sent to a PACS or workstation to prioritize the radiologist's worklist. It does not direct attention to specific image regions or anomalies other than aICH. By flagging potential hemorrhages, it assists clinicians in rapid assessment and triage, potentially reducing time-to-diagnosis for critical patients.
Clinical Evidence
Retrospective study of 360 NCCT cases from multiple US sites. Primary endpoints: Sensitivity 97% (95% CI: 92.8-98.8%) and Specificity 93% (95% CI: 88.6-96.6%). Subgroup sensitivity: 100% for intra-axial, 92% for extra-axial hemorrhage. Secondary endpoints: NPV 99.8%, PPV 43.3%. Average processing time 1.17 minutes. Results exceeded predefined performance goals and showed improvement over the predicate device.
Technological Characteristics
DICOM-compliant software; operates within clinical radiology networks. Utilizes deep learning (CNN-based) for image segmentation and feature identification. Inputs: NCCT scans. Outputs: Case-level triage indicator. Connectivity: Integrates with PACS/workstations via MICD (e.g., Accipio Agent).
Indications for Use
Indicated for adult patients (21+ years) undergoing non-contrast head CT in acute care settings to prioritize clinical assessment of cases with features suggestive of acute intracranial hemorrhage (aICH). Not for stand-alone diagnosis or ruling out hemorrhage.
Regulatory Classification
Identification
Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.
Special Controls
Radiological computer aided triage and notification software must comply with the following special controls: 1. Design verification and validation must include: i. A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations. ii. A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (e.g., improved time to review of prioritized images for pre-specified clinicians). iii. Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment. iv. Standalone performance testing protocols and results of the device. v. Appropriate software documentation (e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results). 2. Labeling must include the following: i. A detailed description of the patient population for which the device is indicated for use. ii. A detailed description of the intended user and user training that addresses appropriate use protocols for the device. iii. Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (e.g., poor image quality for certain subpopulations), as applicable. iv. A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images. v. Device operating instructions. vi. A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.
*Classification.* Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
*e.g.,* improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
*e.g.,* subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
*e.g.,* device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use;
(ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
*e.g.,* poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
*e.g.,* improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (*e.g.,* confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.
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MaxQ Al Ltd. % Joshua Schulman, Ph.D. Vice President, Clinical, Regulatory and Quality Affairs 96 Yigal Alon Street, 1st Floor Tel Aviv, 6789140 ISRAEL
August 7, 2020
Re: K201310
Trade/Device Name: Accipiolx Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QAS Dated: May 15, 2020 Received: May 15, 2020
Dear Dr. Schulman:
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.
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
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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 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.
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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#### 510(k) Number (if known)
#### K201310
Device Name
Accipiolx
Indications for Use (Describe)
Accipiolx is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage in the acute care environment. Accipiolx analyzes cases using an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.
Accipiolx is not intended to direct attention to specific portions of an image or to anomalies other than acute intracranial hemorrhage. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT cases.
| 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
### MaxQ AI's Accipiolx
### Submitter
MaxQ AI Ltd. 96 Yigal Alon Street, Tel Aviv, Israel 6789140 Tel: +1-617-765-0333 Contact Person: Josh Schulman, Ph.D.
Date Prepared: May 15, 2020
Name of Device: Accipiolx
## Common or Usual Name/ Classification Name:
Radiological Computer-Assisted Triage and Notification Software
Regulatory Class: Class II
# Regulatory Classification and Product Code: 21 C.F.R. § 892.2080; QAS
Predicate Device: Accipiolx, K182177, Manufactured by MaxQ Al
### Device Description
Accipiolx is a software device designed to be installed within healthcare facility radiology networks to identify and prioritize non-contrast head CT (NCCT) scans based on algorithmically-identified findings of acute intracranial hemorrhage (alCH). The device, which utilizes deep learning technologies, facilitates prioritization of CT scans containing findings of alCH. Accipiolx receives CT scans identified by the Accipio Agent or other compatible Medical Image Communications Device (MICD), processes them using algorithmic methods involving execution of multiple computational steps to identify suspected presence of alCH, and generates a results file to be transferred by the Accipio Agent or a similar MICD device for output to a PACS system or workstation for worklist prioritization.
Accipiolx works in parallel to and in conjunction with the standard care of workflow. After a CT scan has been performed, a copy of the study is automatically retrieved and processed by Accipiolx. The device performs identification and classification of objects consistent with alCH, and provides a case-level indicator which facilitates prioritization of cases with potential acute hemorrhagic findings for urgent review.
### Intended Use / Indications for Use
Accipiolx is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage in the acute care environment. Accipiolx analyzes cases using an artificial intelligence algorithm to identify
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suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.
Accipiolx is not intended to direct attention to specific portions of an image or to anomalies other than acute intracranial hemorrhage. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT cases.
### Summary of Technological Characteristics
The technological characteristics and principles of operation of the subject Accipiolx device are substantially equivalent to the predicate device, Accipiolx (K182177). The subject Accipiolx and its predicate are DICOM-compliant software devices incorporated into the radiology infrastructure of a clinical center. Both employ algorithms developed through artificial intelligence methodologies to analyze head CT images received from a CT scanner.
The subject Accipiolx, like its predicate device, was developed using a training CT cases collected from multiple institutions and CT manufacturers. This training process included pilot development, optimization of object and feature identification, algorithmic training and selection/optimization of thresholds above which cases are considered positive.
The subject device and the predicate device also have highly similar principles of operation: the software analyzes CT images and prioritizes cases for physician review when there is suspected alCH. In both the predicate and subject devices, the algorithm identifies applicable CT cases based on image parameters. In both devices, skull stripping and registration steps are performed, the relevant tissues are identified and segmented, and a feature identification process, which includes measures of hyperdensity compared to a pre-defined threshold, is performed. Findings above this threshold cause the software device to generate a case-level identifier which is used for prioritization of cases based on a suspected cerebrovascular finding. The system output is used for prioritization within radiological workflow and viewing systems. In both cases, the procedure is performed in parallel to and in conjunction with the standard processing of image and their availability for clinician assessment.
The algorithmic functionality of Accipiolx is substantially equivalent in all aspects to that of the predicate device and differs only with respect to the segmentation method used and inclusion of additional algorithmic features for enhanced detection of alCH. These improvement changes do not alter the safety and efficacy profile of the device.
The subject Accipiolx and the predicate device have the same intended use and indications for use. Both devices are assistive software tools, designed to analyze head CT images for findings suggestive of a pre-specified clinical condition -- specifically, alCH. Both the subject and predicate devices support the rapid assessment of alCH. For these reasons, the subject device is substantially equivalent to the predicate device.
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A summary table comparing the key features of the subject and predicate devices is provided below.
| | Subject Device:<br>MaxQ Al Accipiolx | Predicate Device:<br>MaxQ Al Accipiolx<br>(K182177) |
|---------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Intended Use /<br>Indications for Use | Accipiolx is a software workflow<br>tool designed to aid in prioritizing<br>the clinical assessment of adult<br>non-contrast head CT cases with<br>features suggestive of acute<br>intracranial hemorrhage in the<br>acute care environment. Accipiolx<br>analyzes cases using an artificial<br>intelligence algorithm to identify<br>suspected findings. It makes case-<br>level output available to a<br>PACS/workstation for worklist<br>prioritization or triage.<br>Accipiolx is not intended to direct<br>attention to specific portions of an | Accipiolx is a software workflow<br>tool designed to aid in prioritizing<br>the clinical assessment of adult<br>non-contrast head CT cases with<br>features suggestive of acute<br>intracranial hemorrhage in the<br>acute care environment. Accipiolx<br>analyzes cases using an artificial<br>intelligence algorithm to identify<br>suspected findings. It makes case-<br>level output available to a<br>PACS/workstation for worklist<br>prioritization or triage.<br>Accipiolx is not intended to direct<br>attention to specific portions of an |
| | image or to anomalies other than<br>acute intracranial hemorrhage. Its<br>results are not intended to be used<br>on a stand- alone basis for clinical<br>decision-making nor is it intended<br>to rule out hemorrhage or<br>otherwise preclude clinical<br>assessment of CT cases. | image or to anomalies other than<br>acute intracranial hemorrhage. Its<br>results are not intended to be used<br>on a stand- alone basis for clinical<br>decision-making nor is it intended<br>to rule out hemorrhage or<br>otherwise preclude clinical<br>assessment of CT cases. |
| Population | Adult patients (21 years of age and<br>older) indicated for head CT | Adult patients (21 years of age and<br>older) indicated for head CT |
| Notification-only, parallel workflow<br>tool | Yes | Yes |
| Intended users | Hospital networks and trained<br>clinicians | Hospital networks and trained<br>clinicians |
| Setting | Acute care | Acute care |
| Identify patients with a pre-<br>specified clinical condition | Yes | Yes |
| Clinical condition | Cerebrovascular Event:<br>Intracranial hemorrhage | Cerebrovascular Event:<br>Intracranial hemorrhage |
| Alert to finding | Yes; flagged for review | Yes; flagged for review |
| Independent of standard of care | Yes; No cases are removed from | Yes; No cases are removed from |
| workflow | worklist | worklist |
| Modality | Non-Contrast head CT | Non-Contrast head CT |
| Artificial Intelligence algorithm | Yes | Yes |
| Software segmentation method | CNN-based segmentation | Machine-vision based segmentation |
| Case level assessment of clinical<br>condition probability | Yes - case-level assessment is<br>derived both from detection of<br>individual aICH findings and<br>algorithmic assessment of the full<br>case. | Yes - case-level assessment is<br>derived from detection of individual<br>aICH findings |
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| Limited to analysis of imaging data | Yes | Yes |
|--------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Non-Diagnostic DICOM Preview | No | No |
| Aids prompt identification and<br>prioritization of cases with<br>indicated findings | Yes | Yes |
| Medical systems/devices used in<br>conjunction with the device | 1. Device is used with CT systems<br>for DICOM input and with PACS<br>systems, which may present/use<br>the data.<br>2. Device is used with Medical<br>Image Communications Devices<br>(MICDs), such as the Accipio<br>Agent, to receive native CT<br>images for processing and to<br>generate visual results | 1. Device is used with CT systems<br>for DICOM input and with PACS<br>systems, which may present/use<br>the data.<br>2. Device is used with Medical<br>Image Communications Devices<br>(MICDs), such as the Accipio<br>Agent, to receive native CT<br>images for processing and to<br>generate visual results |
| Output | Suspected hemorrhage /<br>No suspected hemorrhage | Suspected hemorrhage /<br>No suspected hemorrhage |
| Systems where results are<br>displayed | PACS / Workstation | PACS / Workstation |
| Performance results | Sensitivity- 97%<br>(95% Cl: 92.8% - 98.8%)<br>Specificity- 93%<br>(95% Cl: 88.6% - 96.6%)<br>Processing time - 1.17 minutes<br>(95% Cl: 1.16 - 1.18 minutes) | Sensitivity - 92%<br>(95% Cl: 87.29 - 95.68%)<br>Specificity - 86%<br>(95% Cl: 80.18 - 90.81%)<br>Processing time - 4.1 minutes<br>(95% Cl: 3.8 - 4.3 minutes) |
# Performance Testing
Performance testing was conducted using the same approach as that used for the predicate device. MaxQ AI conducted a retrospective study to test the clinical performance of Accipiolx in processing non-contrast head CT cases with a high or low probability of ICH. Performance of the device was analyzed based on validation testing results that were compared to the predefined performance qoals.
- . Analysis of 360 newly tested cases collected from multiple sites across 17 US states demonstrated device Sensitivity and Specificity of 97% (95% Cl: 92.8%) and 93% (95% Cl: 88.6% - 96.6%), respectively.
- Sensitivity for intra-axial and extra-axial hemorrhages was 100% (95% Cl: 96.6% 100%) and . 92% (95% Cl: 82.7% - 96.9%), respectively. Although the Sensitivity observed in the extra-axial subgroup was lower compared to the intra-axial subgroup, the result was still notably high, considering the fact that extra-axial hemorrhages are known to be more difficult for accurate assessment among radiologists.
- The average per-case processing time (confirmatory secondary endpoint) was 1.17 minutes (95% Cl: 1.16 - 1.18 minutes), demonstrating that when using the subject Accipiolx for alCH prioritization, the intended users will have improved benefit in time saving compared to the predicate device.
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- Analysis of additional accuracy parameters, tested as secondary endpoints, demonstrated NPV . and PPV of 99.8% (95% Cl: 99.7% - 100%) and 43.3% (95% - 53%), respectively, The very high NPV result reflects relatively very low probability of false positive results given by Accipiolx.
These results exceeded the predefined performance goals and demonstrated improved clinical performance, which is substantially equivalent to the predicate device.
Based on the software testing and clinical performance, Accipiolx has a safety and effectiveness profile that is substantially equivalent to the predicate device for the proposed indications for use.
## Conclusion
The Accipiolx device is as safe and effective as its predicate device. Accipiolx has the same intended use and indications for use, very similar technological characteristics and principles of operation as its predicate device. The minor differences do not alter the intended prioritization and triage use of the device and do not affect its safety and effectiveness when used as labeled, and also do not raise any new or different questions of safety or effectiveness. Clinical testing demonstrates an improved performance profile and supports the intended software function. Thus, the subject Accipiolx is substantially equivalent to the predicate Accipiolx (K182177).
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Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
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.
