AI/ML, Software as a Medical Device, Real-World Evidence, Pediatric
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K974748 · Jul 10, 1998
NEUROMETRIC ANALYSIS SYSTEM
Nxlink, Ltd.
Clinical patient records from the Brain Research Laboratory (BRL) at New York University's Medical Center
The device utilizes a 20-year retrospective clinical and normative database to provide statistical comparisons (Z-scores and Mahalanobis Distance) and discriminant analysis for EEG interpretation.
BRL Normative and Clinical Database Projects; Retrospective database analysis; Follow-up/Duration: 20-year effort; Study Period: Historical (spanning 20 years prior to 1997)
Individuals aged 6 to 90 years, including volunteers and clinical patients referred for neurometric evaluation; Number of Sites: 1 (New York University Medical Center)
Not applicable for this study
Statistical characterization of EEG features; classification of patient profiles
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Neurometric EEG classification
Step-wise discriminant analysis
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20-year normative and clinical database projects at the Brain Research Laboratory (BRL) at New York University's Medical Center
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Indications for Use
The Neurometric Analysis System (NAS) is to be used by qualified medical professionals for the post-hoc statistical evaluation of the human electroencephalogram (EEG).
Device Story
The Neurometric Analysis System (NAS) is a software program for statistical analysis of human EEG. Input consists of approximately 2 minutes of artifact-free, eyes-closed, resting EEG data transferred from a host system. The system performs Fast-Fourier Transformation (FFT) to extract spectral power (delta, theta, alpha, beta) and frequency information. Data is subjected to univariate, bivariate, and multivariate statistical analyses, including Mahalanobis Distance statistics, and compared against an age-regressed normative database. Output includes statistical tables and topographical brain maps of absolute/relative power, power asymmetry, and coherence. A step-wise discriminant analysis classifies the patient profile against known neurometric-defined patterns of abnormality. Used in clinical settings by physicians as an adjunct to traditional EEG interpretation. The system aids in diagnosis, treatment planning, and follow-up by providing quantitative measures of brain cortical function. It is intended to supplement, not replace, clinical judgment; reliance on maps or indices alone is contraindicated.
Clinical Evidence
Evidence based on a 20-year normative and clinical database project at New York University Medical Center. Non-clinical testing verified signal processing accuracy against host systems and reproducibility of results. Clinical testing involved patients aged 6-90 years; results were compared against host system analyses to ensure consistency in discriminant classification. No new clinical trials were conducted; the device relies on the validation of the neurometric method and agreement with established clinical databases. Testing confirmed the system provides reproducible results when using artifact-free, eyes-closed, resting EEG.
Technological Characteristics
Software-based EEG frequency spectrum analyzer. Operates on IBM-compatible PCs under DOS/Windows. Uses Fast-Fourier Transformation (FFT) for spectral analysis. Employs univariate, bivariate, and multivariate statistical procedures (Z-scores, Mahalanobis Distance) and step-wise discriminant analysis. Connectivity via standard computer platforms and I/O devices. Programmed in C++.
Indications for Use
Indicated for qualified medical professionals to perform post-hoc statistical evaluation of human EEG in patients aged 6 to 90 years. Contraindicated for use as a stand-alone diagnostic tool; must be used as an adjunct to traditional clinical assessment.
Regulatory Classification
Identification
An electroencephalograph is a device used to measure and record the electrical activity of the patient's brain obtained by placing two or more electrodes on the head.
Lexicor Medical Technology Neurosearch-24 Specuran 22 (200000 1 (42020038))
QS1-9200 (FDA 510(k) information not available)
Nicolet BEAM I, II (no FDA 510(k) information available)
Submission Summary (Full Text)
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## JUL 10 1998
K974748
FDA 510(k) Summary
#### Section 807.92
(a)(1). Submitter's Name: NxLink, Ltd., 1706 Gaillard Place, Richland, WA. 99352. Phone: (509)-943-1023; Fax: (509)-946-1208; email: nxduane@oneworld.owt.com.
Contact Person: Duane Shuttlesworth, Ph.D. NxLink, Ltd., 1706 Gaillard Place, Eichland, WA. 99352. Phone: (509)-943-1023; Fax: (509)-946-1208; email: nxduane@oneworld.ow.com.
## Date of preparation: 10/25/97
(a)(2). Name of the Device: Neurometric Analysis System (NAS). Classification name: EEG Frequency Spectrum Analyzer.
(a)(3). Predicate/legally marketed devices upon which substantial equivalence is (a)(a); I resicatellerie any and acter to PDA information available); TECA Corporation Neurolab I, II (K844481), Brain Mapper (K890-881), Neuromapper 386 (K894889); Nicolet BEAM I, II (no FDA 510(k) information available); Pathinder II (K801604) Brain Functional Map (K843598); Cadwell Laboratories, Inc. 8400 (K860801) and Spectrum 32 (K860801 reference); Lexicor Medical Technology Neurosearch-24 Specuran 22 (200000 1 (42020038); Neuroscience, Inc. Map-10 EEG (K840430), (1990-209); 1620 (K870263); Biologics Systems Corporation, Inc., Modified Brain Nethomapper 1020 (107-1209), Automatic Event Analysis (K951594); Quantified Signal Imaging, Inc. QS1-9500 (K904294), QS1-9200 (FDA Š10(k) information not available); Stellate Systems, Inc. Rhythm Software (K912938).
(a)(4). Device Description: The Neurometric Analysis System (NAS) is a software (4)(9). Device Desting statistical analysis of the human electroencephalogram (EEG). program for the poor noo sective (i.e., the host system) is transferred to the NAS for display and user-review. The system requires that the user select approximately 2.00 minutes of artifact-free, eyes-closed, resting EEG from the recording for analysis. Analysis consists of the Fast-Fourier Transformation (FFT) of the data to extract the spectral power for each of the four primary frequency bands (delta, theta, alpha, and beta), and frequency information from the EEG. The results of this analysis are then subjected to univariate, bivariate, and multivariate statistical analyses and displayed in statistical tables and topographical brain maps of absolute and relative power, power asymmetry, and coherence for 19 monopolar and 8 selected bipolar derivations of the EEG. In all over 1200 measures are derived for comparison against a carefully constructed and statistically controlled age-regressed, normative database in which the variables have been transformed and confirmed for their Gaussian distribution. Each variable extracted by the analysis is compared to the database using parametric statistical procedures that express the differences between the patient and their appropriate age-matched reference group in the form of Z-scores. Multivariate features are compared to the normative database using
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Mahalanobis Distance Statistics. The Mahalanobis Distance statistic controls for the interrelationship of the measures of brain cortical function in the feature set, and provides an accurate estimate of their difference from normal. The multivariate measures permit an evaluation of regional indices of brain function that reflect the perfusion fields of the brain. Extracted feature sets are further analyzed to determine if the pattern of 'hits' (statistically, significant feature score values identified for the patient) are consistent with patterns of 'hits' identified in prior neurometric evaluations of clinical patients with known disorders. A step-wise discriminant analysis program classifies the patient in terms of their similarity to known neurometric-defined patterns of abnormality, providing a probability estimate of the patient's profile with the average profile of groups of individuals constituting the normative and clinical database. The discriminant classification program is restricted by confiniters potential outcomes to specific patient symptoms derived from the patient history. profile. Established discriminant functions were evaluated through the use of Receiver Operating Characteristic (ROC) curves for their sensitivity and specificity. The outcome of the statistical analysis is presented in report form that includes (a) patient demographic and history information, (b) selected EEG epochs, (c) statistical tables of monopolar, the lastery and multivariated feature values, and topographical brain maps. This information is to be read and interpreted within the context of the current clinical assessment of the patient by the attending physician. The decision to accept or reject the results of the neurometric analysis, and incorporate these results into their clinical appraisal of the patient, is dependent upon the judgment of the attending physician.
The Neurometric Analysis System is complete in a set of five 3.5 diskettes, which contains. a demonstration program with sample neurometric studies, the NAS program, and the a demonstration program. The NAS was designed for implementation under DOS and Windows, and programmed using C++. The user interface was carefully designed and implemented to programmed comb procedures are used to record steps used in program usage, and the conduct of the analysis to insure appropriate function and operation of the software. The NAS can be installed in any appropriately configured IBM-compatible computer system, including systems designed specifically for the recording of digital EEG. The system functions with systems access of standard computer platforms and input-output devices, and printers.
(a)(5). Statement of Indications of Use: Indications for the use of the Neurometric Analysis System (NAS) are as follows:
### Indications of Use
The Neurometric Analysis system is to be used by qualified medical profedssionals for the post-hoc statistical evaluation of the human electroencephalogram (EEG).
(a)(6). Comparison to Predicate Devices: The Neurometric Analysis System uses accepted methods of data selection and analysis to extract the feature measures upon which statistical determination of normal/abnormal are made, and from which derivations of probability estimates of clinical classification are derived. The neurometric method of
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EEG selection, analysis, and interpretation have been previously implemented, in whole on in part, in a variety of digital EEG and analysis systems marketed in prior years for the quantitative analysis of the EEG in Man. The NAS database was carefully constructed to quandtative andiyas of the BLG M. H. J. Type II errors in the use of database comparisons in clinical electrophysiological assessment of the human EEG. The compansons in childed clectrophysions of the NAS has been enhanced relative to these purposendi, caby to ass the careful consideration of user interactions with this technologically advanced method of analysis.
(b). Non-clinical and Clinical Tests: The Neurometric Analysis System's design and implementation was based upon the results of an extensive, 20-year effort to construct a implementative and clinical database at the Brain Research Laboratory (BRL) at New York University's Medical Center. The NAS incorporates the basic methods of data Your children data selection, analysis, and interpretation developed at the BRL during the conduct of numerous government and privately funded normative and clinical database projects.
(b)(1). Non-clinical Testing: Non-clinical testing of the NAS included the evaluation of (0)(1). Non-cannear restlug. Non oneed for data analysis. Specifically, control signals, in the form of signal generated waveforms, were analyzed for frequency and power. EEG signals were analyzed for conformity between the host digital EEG system and the NAS. signals were and your to reproduces sampling frequency in the host digital EEG The 1070 menoves a learing and evaluation of the EEG waveforms for accuracy System, and permans the MAS translation. In addition, data obtained in previous implementations of the neurometric method were evaluated for consistency and accuracythe results of the NAS's analysis of stored subject data had to conform to that of the prior analysis (which was conducted using the same method and procedures, algorithms and method of analysis as that implemented on the NAS).
(b)(2). Clinical Testing: The ability of the NAS to accurately translate and present EEGs from clinical patients was confirmed by the nonclinical testing. In order for the NAS to be an effective implementation of the neurometric method for clinical use, the results of the analysis (both statistical tables and topographical brain maps) had to be in agreement with the results of the analysis conducted on the host system used in the processing of patient information at the Brain Research Laboratory. In addition, the outcome of the discriminant analysis had to be consistent, not resulting in errors of misclassification (that is, the classification on the NAS had to be consistent with that of the host system used to perform the neurometric analysis at the BRL). These tests confirmed that when eyes-closed, resting, and artifact-free EEG was selected for analysis, the results were reproducible within an acceptable degree of variation consistent with reliability estimates identified in the normative studies.
Subjects upon which this device has been tested included individuals who ranged in age from 6 to 90 years, and who were either volunteers or clinical patients referred for neurometric evaluation to the Brain Research Laboratory by the Department of Psychiatry
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and Department of Neurology at New York University Medical Center. The results of the and Department of Neurology at New York Une visa asked to use the information as analysis were conveyed in the recenting prysicial who neaditional EEG. The information
an adjunct to their clinical interpretation of the patiented for statistical tables an adjunct to their clinical interpreation of the poches selected for analysis, staistical tables
was provided in report form (including EPG epochs selected for analysis) to was provided in report form (including LLC Cfreedinant and diagnosis or treat and topographic brain maps, and the result of the clises and disgnosis or treatment
physician to determine its relevance to the clinical evaluation and disgrosis of treatment of the patient. When the results are used in this manner, the likelihood of introducing of the patient. When the results are used in the mannely reduced. That is, the test is viewed as error into diagnosis and treatment is substantially resease. " which he subsidiary basis for the diagnosis.
Potential adverse effects of the use of the device are known if the Neurometric Analysis Potential adverse entects of the use of the Sevien (a use that is specifically contraindicated System is used as a stata-alone angliopers) in the absence of other clinical data from more by NXLMK and the system's developed by this a only upon the use of a single index (such thatitional means of patient overaphical maps alone) without reviewing the traditional as relative power, or the topograpisca inthe complete set of statistical summary tables is
and epochs selected for analysis, or the complete set of statistical summers of sp also contraindicated and a source of potential error. Additional sources of error could arise from the inappropriate selection of EEG (selecting artifacted EEG epochs, or selecting EEG representative of other states, such as drowsiness or eyes-open EEG, or by selecting ECO representative of other than those specified. Additionally, it is purposely selecting concirculars through the purposeful falsification of symptoms in the patient history, and patient age.
(b)(3) Conclusions Drawn From Non-Clinical and Clinical Testing: The appropriate (0)(0) Concellent Dransysis System as an adjunct to the traditional visually-appraised ase of the Noardinonia is and your with the ability to quantify EEG variables and use them to answer questions drawn from their clinical experience with the patient. When used by an experienced, qualified practitioner, or under the proper supervision of a qualified medical professional, the NAS is concluded to be a useful and beneficial addition to the array of clinically accepted medical tests and devices used to evaluate brain structure and finction.
The results of non-clinical and clinical testing conducted over the past 20 years demonstrates that the NAS is both safe and effective for the quantitative analysis of the eyes-closed resting EEG in the alert human subject. Used to determine if the EEG is normal or abnormal, and if abnormal, to statistically characterize the distribution of selected neurometrically-derived features by their probability of being similarly distributed in specified groups of clinical patients, the NAS provides information that both complements and supplements the outcome of the analysis of a traditional EEG. This information, when properly used in conjunction with other clinical tests as a safe and effective adjunctive aid to diagnosis, treatment planning, and treatment follow-up of the neurologic and psychiatric patient.
Compared to its predicate devices, the Neurometric Analysis System's inclusion of specific, appropriate, and effective statistical controls over the method of data selection
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and analysis, the scientific rigor involved in the construction, refinement, and application and analysis, the scientific rigor involved in the constition, resulting providers with alle the normative indices of brain structure and finction that is oth safe of the notificative indices of brain structure and tunction the fish of our and of comments.
and effective and suggests hat the NAS is a significant advancement in the use of and effective, suggests that the NAS is a signinoun. Count
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Image /page/5/Picture/1 description: The image shows the logo for the Department of Health & Human Services USA. The logo consists of a circle with the text "DEPARTMENT OF HEALTH & HUMAN SERVICES USA" around the perimeter. Inside the circle is a stylized image of three human profiles facing right, with flowing lines representing hair or clothing.
Food and Drug Administration 10903 New Hampshire Avenue Document Control Room -WO66-G609 Silver Spring, MD 20993-0002
Duane Shuttlesworth,Ph.D. NxLink, Ltd. 1706 Gaillard Place Richland, Washington 99352
Re: K974748
Trade/Device Name: Neurometric Analysis System Regulation Number: 21 CFR 882.1400 Regulation Name: Electroencephalograph Regulatory Class: II Product Code: OLU Dated (Date on orig SE ltr): April 8, 1998 Received (Date on orig SE Itr): April 13, 1998
Dear Mr. Shuttlesworth:
This letter corrects our substantially equivalent letter of July 10, 1998.
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. 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.
APR - 9 2012
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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 (reporting of medical device-related adverse events) (21 CFR 803); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820); and if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
If you desire specific advice for your device on our labeling regulation (21 CFR Part 801), please go to http://www.fda.gov/AboutFDA/CentersOffices/CDRH/CDRHOffices/ucm115809.htm for the Center for Devices and Radiological Health's (CDRH's) Office of Compliance. Also, please note the regulation entitled. "Misbranding by reference to premarket notification" (21CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to
http://www.fda.gov/MedicalDevices/Safety/ReportaProblem/default.htm for the CDRH's Office of Surveillance and Biometrics/Division of Postmarket Surveillance.
You may obtain other general information on your responsibilities under the Act from the Division of Small Manufacturers, International and Consumer Assistance at its toll-free number (800) 638-2041 or (301) 796-7100 or at its Internet address
http://www.fda.gov/MedicalDevices/ResourcesforYou/Industry/default.htm.
Sincerely yours,
Kesia Alexander
Image /page/6/Picture/8 description: The image shows the word "for" written in cursive. The letters are connected and flow together smoothly. The "f" has a large loop that extends above and below the other letters. The "o" and "r" are smaller and sit in the middle of the "f".
Malvina B. Eydelman, M.D. Director Division of Ophthalmic, Neurological, and Ear. Nose and Throat Devices Office of Device Evaluation Center for Devices and Radiological Health
Enclosure
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. . . . . .
# 510(k) Number (if known): K974748
Device Name: Neurometric Analysis System
Indications For Use: The Neurometric Analysis System (NAS) is to be used by qualified medical professionals for the post-hoc statistical evaluation of the human electroencephalogram (EEG).
(PLEASE DO NOT WRITE BELOW THIS LINE-CONTINUE ON ANOTHER PAGE IF NEEDED)
| Concurrence of CDRH, Office of Device Evaluation (ODE) | | |
|--------------------------------------------------------|---------|----------------------|
| (Division Sign-Off) | | |
| Division of General Restorative Devices | | |
| 510(k) Number | K994748 | |
| Prescription Use<br>Per 21 CRF 801.109 | OR | Over-The-Counter Use |
(Optional Format 1-2-96)
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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.