K092039 · Optima Neuroscience, Inc. · OMB · Oct 16, 2009 · Neurology
Device Facts
Record ID
K092039
Device Name
IDENTEVENT, VERSION 1.0G
Applicant
Optima Neuroscience, Inc.
Product Code
OMB · Neurology
Decision Date
Oct 16, 2009
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 882.1400
Device Class
Class 2
Attributes
Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K092039 · Oct 16, 2009
IDENTEVENT, VERSION 1.0G
Optima Neuroscience, Inc.
Retrospective long-term scalp EEG recordings from clinical diagnostic/pre-surgical evaluations
The sponsor used a retrospective cohort of 55 clinical EEG recordings to evaluate the seizure detection performance (sensitivity and false detection rate) of the IdentEvent software compared to a predicate device.
IdentEvent™ is a software-only product with an algorithm intended to analyze previously acquired, adult (≥18 years) scalp EEG signals and mark events that may correspond to electrographic seizures for the purpose of reviewing prolonged EEG traces. The marked events are reviewed, possibly deleted, and interpreted by qualified clinical practitioners who will exercise professional judgment in using the information. IdentEvent also includes the display of the quantitative EEG parameters Amplitude Variation and Maximum Frequency, which are intended to help the user analyze the EEG waveform after it has been collected. IdentEvent requires the use of EEGs recorded with at least a 16-channel scalp montage following the standard 10/20 electrode placement system. IdentEvent does not provide any diagnostic conclusion about the patient's condition to the user.
Device Story
IdentEvent is a software-only post-hoc analysis tool for reviewing long-term digital scalp EEG recordings. It processes previously recorded EEG data (16-channel, 10/20 system) to automatically detect and mark potential electrographic seizure events. The device calculates and displays two quantitative EEG (qEEG) measures: Amplitude Variation (standard deviation of signal within a window) and Maximum Frequency (zero-crossing frequency in overlapping time windows). Used by neurologists and trained EEG technicians in clinical settings to assist in reviewing prolonged EEG traces. The software provides raw/filtered signal displays, seizure markers, and qEEG metrics. Users review, edit, or delete detected events; the device provides no diagnostic conclusions. By automating the identification of potential seizure events, it facilitates efficient review of long-term recordings, aiding clinical decision-making regarding patient seizure activity.
Clinical Evidence
Clinical study evaluated seizure detection performance on 1,208.24 hours of scalp EEG from 55 adult patients with medically refractory seizures (436 segments). Reference standard established by three blinded neurologists using majority rule. IdentEvent achieved 79.5% positive percent agreement (95% CI: 70%, 87%) and a negative disagreement rate of 2/24h (95% CI: 1.3, 3.3). Performance was statistically compared to Persyst Reveal; IdentEvent showed similar sensitivity with significantly lower false detection rates.
Technological Characteristics
Software-only device for post-hoc analysis of digital EEG files. Requires 16-channel scalp EEG (10/20 system). Features include raw/filtered signal display, seizure detection algorithm, and qEEG parameter calculation (Amplitude Variation, Maximum Frequency). Operates on standard PC hardware. No specific material or sterilization requirements as it is a software-only product.
Indications for Use
Indicated for adults (≥18 years) with previously acquired scalp EEG signals for the purpose of reviewing prolonged EEG traces to identify potential electrographic seizures. Requires at least 16-channel scalp montage using standard 10/20 electrode placement.
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.
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#### SECTION 5 510(K) SUMMARY
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## 510(k) Notification (21 CFR 807.90(e))
### Traditional Premarket 510(k) Submission
OCT 1 6 2009
ldentEvent™
| Date: | July 2, 2009 |
|----------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Submitted by: | Optima Neuroscience, Inc.<br>13400 Progress Boulevard<br>Alachua, FL 32615 |
| Representative: | Ryan T. Kern, MD<br>President |
| Phone: | 352-371-8281 |
| FAX: | 386-462-0606 |
| Proprietary Name: | IdentEvent ™ |
| Regulation Number: | 21 CFR 882.1400 |
| Classification Name: | Electroencephalograph, Electroencephalogram (EEG) signal spectrum analyzer |
| Classification Code: | OMB, OLT |
| Regulatory Class: | II |
| Predicate Devices: | Persyst Reveal® (K011397)<br>NeuroGuide Analysis System (K041263) |
| Description: | Optima's IdentEvent™ analyzes digital scalp electroencephalograph (EEG) signals recorded from standard recording systems and displays information about brain electrical activity to the user. The application only analyzes and displays information from previously recorded digital EEG files. IdentEvent is designed for post-hoc EEG review of long-term EEG recordings, including the detection of seizure events. |
| | IdentEvent requires previously recorded, digitized scalp EEG recordings with electrodes placed according to the standard 10/20 system. The recording can then be automatically analyzed to detect seizures which are then marked for review by the user. IdentEvent includes the following features (outputs): |
| | <ul><li>Display of raw & filtered EEG signals for review</li><li>Display of the following quantitative EEG (qEEG) measures: <ul><li>Amplitude Variation</li><li>Maximum Frequency</li></ul></li><li>Review (and possible deletion) of detected seizures</li><li>Entry, editing and display of user-entered comments</li></ul> |
- Entry, editing and display of user-entered comments
Graphical and text reports of detected seizures and comments .
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IdentEvent displays two measures for use in post-hoc analysis of EEG: Amplitude Variation and Maximum Frequency.
Amplitude Variation is the calculation of standard deviation of one EEG signal within each calculation window, a statistical measure which increases with larger signal peaks (amplitude changes). The Amplitude Variation for one signal is calculated as follows:
Image /page/1/Figure/3 description: The image shows a mathematical formula. The formula is for calculating the standard deviation of a sample. It involves summing the squared differences between each data point and the sample mean, dividing by the sample size minus one, and then taking the square root. The formula is commonly used in statistics to measure the spread or dispersion of a set of data.
Maximum Frequency is the maximum number of one-directional (neqative to positive) zero crossings among 11 overlapping 1 second time windows within a given window defined by the sample period (5.12 seconds).
Suppose that {x1, x2, x3, ..., x2048} represents a sample period of 5.12 second 400 Hz EEG epoch. Then 11 overlapping (600ms) 1 second windows can be constructed as:
W1 = {x1, x2, ... , x400}, W2 = {x161, x162, ..., x560}, W3 = {x321, x322, ..., x720}, ..., W11= (x1601, x1602, ..., x2000)
For each Wi, i=1, 2, ..., 11, frequency of one-directional (negative to positive) žero crossings is observed as fi, then the Frequency Maximum for this sample period is defined as:
The application will be used only by neurologists and EEG technicians who are trained in the interpretation of EEGs. The graphical user interface will be designed to be intuitive and easy to setup and use without the need for extensive user training.
Indications for Use:
IdentEvent ™ is a software-only product with an algorithm intended to analyze previously acquired, adult (≥18 vears) scalp EEG signals and mark events that may correspond to electrographic seizures for the purpose of reviewing prolonged EEG traces. The marked events are reviewed, possibly deleted, and interpreted by qualified clinical practitioners who will exercise professional judgment in using the information.
IdentEvent also includes the display of the quantitative EEG parameters Amplitude Variation and Maximum Frequency, which are intended to help the user analyze the EEG waveform after it has been collected.
IdentEvent requires the use of EEGs recorded with at least a 16-channel scalp montage following the standard 10/20 electrode placement system. IdentEvent does not provide any diagnostic conclusion about the patient's condition to the user.
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### Technological Characteristics:
Optima's IdentEvent marks events that may correspond to electrographic seizures for the purpose of reviewing prolonged EEG traces and displays quantitative EEG parameters to help the user analyze the EEG waveform after it has been collected. These characteristics may be viewed in comparison with the technological characteristics of the identified predicate devices in Table 5.1.
#### Table 5.1 Comparison of Device Characteristics
| Optima Neuroscience<br>IdentEvent Software<br>(K092039/A02) | Persyst Reveal<br>(K011397)<br>Appendix A-01 | Neuroguide<br>Analysis System<br>(K041263)<br>Appendix A-02 | |
|-------------------------------------------------------------|------------------------------------------------------|-------------------------------------------------------------|-------------------------------------------------------|
| Identifies spikes | No | Yes | No |
| Identifies seizures | Yes | Yes | No |
| Displays calculated<br>EEG measures | Yes | No | Yes |
| Calculated EEG<br>measures<br>displayed: | Amplitude Variation &<br>Maximum Frequency | None | Power, Coherence &<br>Fast Fourier<br>Transform (FFT) |
| User-adjustable<br>seizure detection | No | Yes | No |
| Users can<br>add/delete events | Yes | Yes | No |
| Number of EEG<br>channels | 21 channels (standard<br>10/20 scalp EEG<br>montage) | Unknown | Unknown |
| Type of EEG<br>recording supported | Scalp EEG only | Unknown | Unknown |
| Type of EEG<br>analysis | Post-hoc only | Post-hoc only | Post-hoc only |
| Population age | Adults (age > 18) | Unknown | Unknown |
| Product Code | OMB, OLT | GWS | GWQ, GWS |
| Indications for Use | See Section 4 | See Section 12 | See Section 12 |
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#### Non-Clinical and Clinical Testing
Non-Clinical: The IdentEvent seizure detection algorithm relies upon underlying mathematical analyses, including signal regularity, maximum frequency, and amplitude variation. Each mathematical analysis was independently calculated and verified against results generated from published methods.
Clinical: Optima conducted an extensive clinical test to: 1) Evaluate the positive percent agreement (i.e., detection sensitivity based on independent EEG review panel) and negative percent agreement (i.e., false detection rate based on independent EEG review panel) of Optima's IdentEvent on long-term scalp EEG recordings; and, 2) Demonstrate the seizure detection performance, in terms of positive percent agreement and negative percent agreement, of Optima's IdentEvent is equal to or better than those of Persyst Reveal.
#### Subiect Population and Test Dataset
The seizure detection performance of IdentEvent's algorithm was evaluated on scalp EEG recordings from patients with medically refractory seizures. All patients 18 years of age or older with a history of intractable seizures admitted to multiple clinical sites for long term EEG-video recordings for diagnostic or pre-surgical evaluation were asked to participate.
A total of 436 EEG segments sampled from 55 long-term scalp EEG recordings were included in the test dataset. These 55 recordings (from 55 patients) represented the first 50 recordings with at least one seizure recorded and the first 5 without seizures recorded (based on initial clinical reports). Under the constraint that no more than 3% of the total seizures were included from one subject; detection performance was tested on 146 seizures in a total of 1,208.24 hours of scalp EEG recordings from these 55 patients. Otherwise, no additional inclusion/exclusion criteria were applied in the data selection process. The number of seizures for all the test subjects is shown in Graph 5.1 and the number of hours for each of the samples may be viewed in Graph 5.2.
Image /page/3/Figure/7 description: This image is a bar graph showing the adjusted number of seizures for 55 subjects. The y-axis represents the adjusted number of seizures, ranging from 0 to 8. The x-axis represents the subject number, ranging from 1 to 55. The graph also includes the range of seizures (0-5), the mean (2.65), the standard deviation (1.81), and the median (3).
Graph 5.1 Number of test electrographic seizures for all 55 test subjects
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Image /page/4/Figure/1 description: The image is a bar graph titled "Graph 5.2 Number of sampled EEG hours for all 55 test subjects". The x-axis is labeled "Subject" and represents the 55 test subjects. The y-axis is labeled "Length of Test EEG Data (Hours)" and ranges from 0 to 30 hours. The range of the data is 14.25 to 30.51, the mean is 21.97, and the standard deviation is 4.49.
#### Reference Standard
Each of the 436 sampled EEG segments was reviewed by three independent, blinded EEG experts (all neurologists/epileptologists) to identify electrographic seizures. The end point of this independent review was to identify, if any, the seizure onset times in each of the sampled EEG segments.
Due to the anticipated inter-rater variability among EEG experts, a majority rule (at least 2 out of 3) was applied to make the final determination of "true" electrographic seizure events.
#### Statistical Analysis
#### 1) Inter-Rater Variability
Inter-rater variability was assessed using Cohen's kappa statistic between any pair of EEG experts. The pair-wise kappa statistics among pairs of experts range from 0.641 to 0.790. with a weighted average of 0.680. Based on the interpretation of kappa statistic by Landis and Koch (Biometrics 33: 159-174, 1997), the kappa statistics for all pairs of EEG reviewers indicated "substantial agreement" (0.61 ~ 0.80).
#### 2) Detection Performance
Based on the seizure samples determined by the independent EEG review panel, the positive percentage agreement (i.e., detection sensitivity based on the Reference Standard) and negative disagreement rate (i.e., false detection rate based on the Reference Standard) were estimated for both IdentEvent and the predicate device. Bootstrap method was applied to construct 95% confidence intervals for the estimated performance statistics, as well as to statistically compare positive percentage agreement between IdentEvent and the predicate device. In addition, Wilcoxon signed-rank test (non-parametric paired two-sample t-test) was applied to compare negative disagreement rates between the two detection devices.
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#### Results - Summary
Table 5.2 provides a summary of the detection performance statistics for IdentEvent and Reveal.
| | IdentEvent | Reveal (0.5) | Reveal (0.8) | Reveal (0.9) |
|----------------------------------------------|-------------------------|----------------------------|--------------------------|-------------------------|
| Positive % Agreement<br>(95% C. I.) | 79.5%<br>(70%, 87%) | 80.8%<br>(72%, 88%) | 76.0%<br>(66%, 84%) | 74.0%<br>(64%, 83%) |
| Negative<br>Disagreement Rate<br>(95% C. I.) | 2/24h<br>(1.3, 3.3)/24h | 13/24h<br>(10.1, 17.1)/24h | 8/24h<br>(5.8, 10.5)/24h | 6/24h<br>(4.1, 7.8)/24h |
Table 5.2 Summary of Detection Performance Statistics
#### Results - Detailed
The overall seizure detection positive percent agreement of IdentEvent was 79.5% (bootstrap 95% Cl = [70%, 87%]) with a negative disagreement rate of 2 per 24 hours (bootstrap 95% Cl = [1.3, 3.3]). The positive percent agreements of Reveal were 80.8%, 76%, and 74% (bootstrap 95% CIs = [72%, 88%], [66%, 84%], [64%, 83%1. respectively) using its three detection thresholds (perception score = 0.5 (default), 0.8 and 0.9), respectively. Reveal's negative disagreement rates were 13, 8, and 6 per 24 hour (bootstrap 95% CIs = [10.1, 17.1], [5.8, 10.5], [4.1, 7.8], respectively).
The statistical comparisons to the predicate device were based on a type I error of .05 and a .10 non-inferiority margin. A Wilcoxon signed rank test and bootstrap estimates to generate 95% confidence intervals were calculated. A plot of negative disagreement rate (on the X-axis) and positive percent agreement (on the Y-axis) is given in Graph 5.3. Confidence intervals for positive percent agreement and neqative disagreement rate are presented in Graphs 5.4 and 5.5. IdentEvent has similar positive percent agreement to the predicate while the negative percent agreement is significantly higher suggesting the false detection rate is lower with IdentEvent.
Image /page/5/Figure/8 description: The image is a graph titled "Graph 5.3 Detection Performance". The graph plots "Positive Percentage Agreement (Detection Sensitivity)" on the y-axis and "Negative Disagreement Rate (False Detection Rate / 24 Hrs)" on the x-axis. There are data points for "IdentEvent" and "Reveal", with perception scores of 0.9, 0.8, and 0.5 indicated for the "Reveal" data points.
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Image /page/6/Figure/1 description: The image is a graph that shows the positive percent agreement (detection sensitivity) and 95% confidence interval for different events. The x-axis ranges from 0.5 to 1.0, and the y-axis lists the events: IdentEvent, Reveal (0.5), Reveal (0.8), and Reveal (0.9). Each event has a horizontal line with a diamond in the middle, representing the confidence interval and the positive percent agreement, respectively. The graph shows that IdentEvent has the highest positive percent agreement, followed by Reveal (0.5), Reveal (0.8), and Reveal (0.9).
Graph 5.4 Positive Percent Agreement (Detection Sensitivity) and 95% Confidence Interval
Graph 5.5 Negative Disagreement Rate (False Detection Rate) and 95% Confidence Interval
Image /page/6/Figure/4 description: The image is a plot showing the negative disagreement rate (false detection rate per 24 hours) for different events. The events are IdentEvent, Reveal (0.5), Reveal (0.8), and Reveal (0.9). The negative disagreement rate for IdentEvent is between 1 and 2. The negative disagreement rate for Reveal (0.5) is between 11 and 17, for Reveal (0.8) it is between 6 and 11, and for Reveal (0.9) it is between 4 and 7.
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Safety testing was performed during validation testing off all software requirements which included safety requirements. Safety requirements included proper handling of patient information, checking data integrity, and prompt notification of the user upon detection of abnormalities. Validation tests demonstrated that all safety requirements were met.
A copy of all study data may be found in Appendix A-05.
#### Conclusion
Compared to Persyst Reveal, Optima's IdentEvent is substantially equivalent in safety and performance, including sensitivity (positive percent agreement) and false positive rate (negative percent agreement).
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Image /page/8/Picture/1 description: The image shows the logo for the Department of Health & Human Services USA. The logo is a circular seal with the words "DEPARTMENT OF HEALTH & HUMAN SERVICES USA" around the perimeter. Inside the circle is an image of an eagle with its wings spread, symbolizing the department's mission to protect the health of all Americans. The eagle is a stylized design with three lines representing its wings and a simple head and body.
Food and Drug Administration 10903 New Hampshire Avenue Document Mail Center - WO66-G609 Silver Spring, MD 20993-0002
Optima Neuroscience, Inc. c/o Ms. Paula Wilkerson RAC. CRA Actualized Science, LLC 12337 NW 9th Lane Newberry, FL 32669
OCT 1 6 2009
Re: K092039
Trade/Device Name: IdentEvent™ Version 1.0H Regulation Number: 21 CFR 882.1400 Regulation Name: Electroencephalograph Regulatory Class: II Product Code: OMB, OLT Dated: July 2, 2009 Received: September 17, 2009
Dear Ms. Wilkerson:
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.
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 (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.
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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/cdrh/mdr/ 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 (240) 276-3150 or at its Internet address http://www.fda.gov/cdrh/industry/support/index.html.
Sincerely yours,
Kesia Alexander for
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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# Indications for Use
510(k) Number (if known): K092039
Device Name: IdentEvent™ (Version 1.0H)
Indications For Use:
IdentEvent™ is a software-only product with an algorithm intended to analyze previously acquired, adult (≥18 years) scalp EEG signals and mark events that may correspond to electrographic seizures for the purpose of reviewing prolonged EEG traces. The marked events are reviewed, possibly deleted, and interpreted by qualified clinical practitioners who will exercise professional judgment in using the information.
IdentEvent also includes the display of the quantitative EEG parameters Amplitude Variation and Maximum Frequency, which are intended to help the user analyze the EEG waveform after it has been collected.
ldentEvent requires the use of EEGs recorded with at least a 16-channel scalp montage following the standard 10/20 electrode placement system. IdentEvent does not provide any diagnostic conclusion about the patient's condition to the user.
Prescription Use (Part 21 CFR 801 Subpart D) AND/OR
Over-The-Counter Use (21 CFR 801 Subpart C)
(PLEASE DO NOT WRITE BELOW THIS LINE-CONTINUE ON ANOTHER PAGE IF NEEDED)
Concurrence of CDRH, Office of Device Evaluation (ODE)
Sara Doll
(Division Sign-Off) Division of Ophthalmic, Neurological and Ear. Nose and Throat Devices
510(k) Number K092039
Page 1 of
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.