Software as a Medical Device, PCCP, Real-World Evidence, Pediatric
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K241589 · Apr 9, 2025
Ceribell Seizure Detection Software
Ceribell, Inc.
Real-world clinical EEG recordings from acute care hospital settings
The performance of the seizure detection algorithm was validated using a retrospective dataset of routine clinical EEG recordings to demonstrate substantial equivalence and support the expansion of the intended patient population to ages 1 and older.
Retrospective clinical data; EEG recordings; Acute care hospital; Algorithm validation
Clinical Evidence
Study Design
Population
Comparator
Key Endpoints
Performance Validation Dataset; Retrospective analysis of routine clinical EEG recordings
Patients aged 1 year and older (1,701 patients total); Sample Size: 1701
Not applicable for this study
Positive Percent Agreement (PPA) and False Positive rate per hour (FP/hr)
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Electrographic Seizure Detection
—
PPA lower bound of 95% CI >= 70%; FP/hr upper bound of 95% CI <= 0.446
Dataset of 1701 EEG recordings from acute care hospital settings (Ages 1-11: 450; Ages 12-17: 392; Ages 18+: 859).
>1 (expert neurologists)
Indications for Use
The Ceribell Seizure Detection Software is intended to mark previously acquired sections of EEG recordings in patients greater or equal to 1 year of age that may correspond to electrographic seizures in order to assist qualified clinical practitioners in the assessment of EEG traces. The Seizure Detection Software also provides notifications to the user when detected seizure prevalence is "Frequent", "Abundant", or "Continuous, per the definitions of the American Clinical Neurophysiology Society Guideline 14. Delays of up to several minutes can occur between the beginning of a seizure and when the Seizure Section notifications will be shown to a user. The Ceribell Seizure Detection Software does not provide any diagnostic conclusion about the subject's condition and Seizure Detection notifications cannot be used as a substitute for real time monitoring of the underlying EEG by a training expert.
Device Story
Software-only device; processes previously acquired EEG recordings to identify potential electrographic seizures. Algorithm marks seizure sections and notifies users of seizure prevalence (Frequent, Abundant, Continuous) per ACNS Guideline 14. Used in acute care hospital settings; operated by qualified clinical practitioners. Output displayed on-screen to assist clinicians in EEG assessment; does not provide diagnostic conclusions or replace real-time expert monitoring. Benefits include improved efficiency in identifying seizure activity in EEG traces. Includes an FDA-authorized Predetermined Change Control Plan (PCCP) for future algorithm performance optimizations.
Clinical Evidence
Retrospective validation using 1,701 patients (ages 1-11: 450; 12-17: 392; 18+: 859). Reference standard established by majority agreement of 3 fellowship-trained neurologists blinded to software output. Primary endpoints: Positive Percent Agreement (PPA) and False Positive rate per hour (FP/hr). Results: PPA (lower 95% CI) ≥ 70% and FP/hr (upper 95% CI) ≤ 0.446 met across all age groups. Overall PPA 93.93% (Frequent), 96.50% (Abundant), 94.12% (Continuous).
Technological Characteristics
Software-only device; no hardware. Implements seizure detection algorithm based on ACNS Guideline 14 definitions. Operates on previously acquired EEG data. Validated per IEC 62366-1:2015 usability standards. Includes PCCP for algorithm updates via validated software update process.
Indications for Use
Indicated for patients ≥ 1 year of age to mark previously acquired EEG recording sections corresponding to potential electrographic seizures. Assists qualified clinical practitioners in EEG trace assessment. Provides notifications for "Frequent", "Abundant", or "Continuous" seizure prevalence per ACNS Guideline 14. Not for diagnostic conclusions or as a substitute for real-time expert monitoring.
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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FDA U.S. FOOD & DRUG ADMINISTRATION
April 9, 2025
Ceribell, Inc.
Raymond Woo, Ph.D.
CTO
360 North Pastoria Avenue
Sunnyvale, California 94085
Re: K241589
Trade/Device Name: Ceribell Seizure Detection Software
Regulation Number: 21 CFR 882.1400
Regulation Name: Electroencephalograph
Regulatory Class: Class II
Product Code: OMB
Dated: March 10, 2025
Received: March 10, 2025
Dear Dr. Raymond Woo:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
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.
FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP). Under section 515C(b)(1) of the Act, a new premarket notification is not required for a change to a device cleared under section 510(k) of the Act, if such change is consistent with an
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
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K241589 - Raymond Woo, Ph.D.
Page 2
established PCCP granted pursuant to section 515C(b)(2) of the Act. Under 21 CFR 807.81(a)(3), a new premarket notification is required if there is a major change or modification in the intended use of a device, or if there is a change or modification in a device that could significantly affect the safety or effectiveness of the device, e.g., a significant change or modification in design, material, chemical composition, energy source, or manufacturing process. Accordingly, if deviations from the established PCCP result in a major change or modification in the intended use of the device, or result in a change or modification in the device that could significantly affect the safety or effectiveness of the device, then a new premarket notification would be required consistent with section 515C(b)(1) of the Act and 21 CFR 807.81(a)(3). Failure to submit such a premarket submission would constitute adulteration and misbranding under sections 501(f)(1)(B) and 502(o) of the Act, respectively.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
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K241589 - Raymond Woo, Ph.D.
Page 3
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/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-devices/device-advice-comprehensive-regulatory-assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Jay R. Gupta -S
Jay Gupta
Assistant Director
DHT5A: Division of Neurosurgical, Neurointerventional, and Neurodiagnostic Devices
OHT5: Office of Neurological and Physical Medicine Devices
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
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DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration
Indications for Use
Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.
Submission Number (if known)
K241589
Device Name
Ceribell Seizure Detection Software
Indications for Use (Describe)
The Ceribell Seizure Detection Software is intended to mark previously acquired sections of EEG recordings in patients greater or equal to 1 year of age that may correspond to electrographic seizures in order to assist qualified clinical practitioners in the assessment of EEG traces. The Seizure Detection Software also provides notifications to the user when detected seizure prevalence is "Frequent", "Abundant", or "Continuous, per the definitions of the American Clinical Neurophysiology Society Guideline 14. Delays of up to several minutes can occur between the beginning of a seizure and when the Seizure Section notifications will be shown to a user.
The Ceribell Seizure Detection Software does not provide any diagnostic conclusion about the subject's condition and Seizure Detection notifications cannot be used as a substitute for real time monitoring of the underlying EEG by a training expert.
Type of Use (Select one or both, as applicable)
☑ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)
## CONTINUE ON A SEPARATE PAGE IF NEEDED.
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510(k) Summary
# K241589
## 510(k) Summary
This summary is being submitted in accordance with the requirements of 21 CFR 807.92.
## Submitter Information:
Ceribell, Inc.
360 North Pastoria Ave
Sunnyvale, California 94085
Phone: 1-800-763-0183
## Contact Person:
Raymond Woo, PhD
CTO
E-mail: ray@ceribell.com
Phone: 650-556-4349
## Alternate Contact Person:
Tom McDougal
Associate Director of Regulatory
E-mail: tom.mcdougal@ceribell.com
## Device Information:
Trade Name: Ceribell Seizure Detection Software
Common Name: Automatic Event Detection Software For Full-Montage Electroencephalograph
Classification Name: Electroencephalograph (21CFR 882.1400)
Device Class: II
Product Code: OMB
## Predicate Device:
K191301, Ceribell Pocket EEG Device (Ceribell, Inc.)
## Date Prepared:
March 10, 2025
## Device Description:
The Ceribell Seizure Detection Software is a software-only device that is intended to mark previously acquired sections of EEG recordings that may correspond to electrographic seizures in order to assist qualified clinical practitioners in the assessment of EEG traces.
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# Indications for Use:
The Ceribell Seizure Detection Software is intended to mark previously acquired sections of EEG recordings in patients greater or equal to 1 year of age that may correspond to electrographic seizures in order to assist qualified clinical practitioners in the assessment of EEG traces. The Seizure Detection Software also provides notifications to the user when detected seizure prevalence is "Frequent", "Abundant", or "Continuous, per the definitions of the American Clinical Neurophysiology Society Guideline 14. Delays of up to several minutes can occur between the beginning of a seizure and when the Seizure Section notifications will be shown to a user.
The Ceribell Seizure Detection Software does not provide any diagnostic conclusion about the subject's condition and Seizure Detection notifications cannot be used as a substitute for real time monitoring of the underlying EEG by a training expert.
# Comparison of Intended Use and Technological Characteristics with the Predicate Devices:
Compared to the predicate device, the subject device has the same intended use and is indicated for an expanded patient population aged 1 year or older. The subject device does not include any hardware component; it is a software-only device that comprises only seizure detection software. The subject device software algorithm has been updated and clinically validated for the intended patient population. Apart from these differences, the subject device and predicate device share the same technological characteristics, clinical workflow, and underlying operating principles. The following tables summarize the substantial equivalence comparison between the subject device and the predicate device.
| Substantial Equivalence Table | | | |
| --- | --- | --- | --- |
| Parameter | Predicate Device
Ceribell Pocket EEG Device (K191301) | Subject Device
Ceribell Seizure Detection Software (K241589) | Substantially Equivalent? |
| Intended Use | The Ceribell Pocket EEG Device is a portable EEG monitoring system that records, stores and presents EEG signals in visual and audible formats in real time. The visual and audible signals assist trained medical staff to make neurological diagnoses. The Pocket EEG Device EEG Recording Viewer software incorporates a Seizure Detection component that is intended to mark previously acquired sections EEG recordings that may correspond to electrographic seizures in order to assist qualified clinical practitioners in the assessment of EEG traces. | Same as for the Seizure Detection component of the Predicate Device: The Ceribell Seizure Detection Software is intended to mark previously acquired sections EEG recordings that may correspond to electrographic seizures in order to assist qualified clinical practitioners in the assessment of EEG traces. | Yes; the intended use of the subject device is within the intended use of the predicate device. |
| Indications for Use | The Ceribell Pocket EEG Device is intended to record and store EEG signals, and to present the EEG signals in visual and audible formats in real time. The visual | The Ceribell Seizure Detection Software is intended to mark previously acquired sections of EEG recordings in patients | Yes; the revised indications for use fall within the same intended use as that of |
510(k) Summary
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510(k) Summary
3
| Substantial Equivalence Table | | | |
| --- | --- | --- | --- |
| Parameter | Predicate Device
Ceribell Pocket EEG Device (K191301) | Subject Device
Ceribell Seizure Detection Software (K241589) | Substantially Equivalent? |
| | and audible signals assist trained medical staff to make neurological diagnoses. The Pocket EEG Device is intended to be used in a professional healthcare facility environment.
Additionally, the EEG Recording Viewer Software component of the Pocket EEG Device incorporates a Seizure Detection component that is intended to mark previously acquired sections of EEG recordings in patients greater than or equal to 8 years of age that may correspond to electrographic seizures in order to assist qualified clinical practitioners in the assessment of EEG traces. The Seizure Detection component provides notifications to the user when detected seizure prevalence is “Frequent,” “Abundant,” or “Continuous,” per the definitions of the American Clinical Neurophysiology Society Guideline 14. Notifications include an on-screen display on the Pocket EEG Device and the optional sending of an e-mail message to a clinician. Delays of up to several minutes can occur between the beginning of a seizure and when the Seizure Detection notifications will be shown to a user.
The Pocket EEG Device does not provide any diagnostic conclusion about the subject's condition and Seizure Detection notifications cannot be used as a substitute for real time monitoring of the underlying EEG by a trained expert. | greater or equal to 1 year of age that may correspond to electrographic seizures in order to assist qualified clinical practitioners in the assessment of EEG traces. The Seizure Detection Software also provides notifications to the user when detected seizure prevalence is “Frequent”, “Abundant”, or “Continuous, per the definitions of the American Clinical Neurophysiology Society Guideline 14. Delays of up to several minutes can occur between the beginning of a seizure and when the Seizure Section notifications will be shown to a user.
The Ceribell Seizure Detection Software does not provide any diagnostic conclusion about the subject's condition and Seizure Detection notifications cannot be used as a substitute for real time monitoring of the underlying EEG by a training expert. | the predicate device. As demonstrated by clinical performance data, the change in intended age range for the patient population of the Seizure Detection Software does not raise new or different questions of safety or effectiveness. |
| Intended Patient Population | • EEG acquisition, recording, storing, and display: All ages
• Seizure Detection module: ages 18 and older | Seizure Detection software: ages 1 and older | Yes; as demonstrated by clinical performance data, the change in age range for the Seizure Detection Software does not raise any new or different questions of safety or effectiveness. The revised indications for use fall within the same intended use as |
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| Substantial Equivalence Table | | | |
| --- | --- | --- | --- |
| Parameter | Predicate Device
Ceribell Pocket EEG Device (K191301) | Subject Device
Ceribell Seizure Detection Software (K241589) | Substantially Equivalent? |
| | | | that of the predicate device. |
| Intended Location of Use | Professional healthcare facilities | Same as predicate device | Yes; no change. |
## Non-clinical Testing:
Software verification and validation testing was conducted, and documentation provided as recommended by the FDA Guidance for Industry and FDA Staff, Content of Premarket Submissions for Device Software Functions. In addition, tests according to "IEC 62366-1:2015, Medical Devices Part 1—Application of usability engineering to medical devices" have been performed.
Software verification and validation activities support the safety and effectiveness of the Ceribell Seizure Detection Software.
## Performance Data:
The following clinical performance data were submitted to support a determination of substantial equivalence:
The Ceribell Seizure Detection Software is validated by evaluating the performance of the Seizure Detection algorithm on a dataset of EEG recordings representative of the intended patient population.
The EEG recordings dataset used for performance validation was gathered from real-world clinical usage of the Ceribell Pocket EEG Device in acute care hospital settings. There were no patient inclusion or exclusion criteria applied, therefore the data are fully representative of the intended patient population. To form the reference standard for seizures, the EEG recordings were retrospectively reviewed by a panel of expert neurologists who were fellowship trained in epilepsy or neurophysiology. A two-thirds majority agreement was required to form a determination of seizures.
The reviewing neurologists did not have access to any of the outputs from the Seizure Detection Software; the experts were fully blinded. Importantly, none of the data in the validation dataset were used for training of the Seizure Detection algorithm; the validation dataset is completely independent.
Details on the validation dataset are provided in the tables below:
PERFORMANCE VALIDATION DATASET.
| | Number of Patients |
| --- | --- |
| Ages 1-11 | 450 |
| Ages 12-17 | 392 |
| Ages 18+ | 859 |
510(k) Summary
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Total
1701
DISTRIBUTION OF SEIZURE EPISODES MEETING THE ACNS DEFINITIONS OF FREQUENT, ABUNDANT, AND CONTINUOUS PER THE ESTABLISHED MAJORITY AGREEMENT BETWEEN 3 EXPERT REVIEWERS.
| | Ages 1-11 | Ages 12-17 | Ages 18+ | Total |
| --- | --- | --- | --- | --- |
| Seizure Episodes with Seizure Burden ≥10% (meeting ACNS definition of ‘Frequent’ activity) | 129 | 77 | 140 | 346 |
| Seizure Episodes with Seizure Burden ≥50% (meeting ACNS definition of ‘Abundant’ activity) | 60 | 22 | 61 | 143 |
| Seizure Episodes with Seizure Burden ≥90% (meeting ACNS definition of ‘Continuous’ activity) | 27 | 10 | 31 | 68 |
## Acceptance Criteria
Performance of the Seizure Detection algorithm was assessed by evaluating the positive percent agreement (PPA) and the false positive rate per hour (FP/hr) of the algorithm compared to the expert reviewer reference standard:
- Positive Percent Agreement (PPA):
For each threshold of Seizure Burden activity (Frequent, Abundant, Continuous)
Lower bound of the 95% confidence interval ≥ 70% PPA
- False Positive rate per hour (FP/hr):
For each threshold of Seizure Burden activity (Frequent, Abundant, Continuous)
Upper bound of the 95% confidence interval ≤ 0.446 FP/hr
## Device Performance
Performance against the acceptance criteria was assessed for the age ranges of 1-11, 2-17 and 18+. The overall performance for all patients was also evaluated. In all cases, the acceptance criteria were met, and the Seizure Detection algorithm PASSES. The detailed results for PPA and FP/hr are shown in the following table:
| Activity Category | Age Group | Positive Percent Agreement (PPA) | 95% Confidence Interval | False Positive Rate (FP/hr) | 95% Confidence Interval | Pass / Fail |
| --- | --- | --- | --- | --- | --- | --- |
| Seizure Episodes with Seizure Burden ≥10% (meeting | Ages 1-11 | 96.12% | [88.35, 99.28] | 0.2700 | [0.2445, 0.2986] | Pass |
| | Ages 12-17 | 87.01% | [73.16, 93.55] | 0.2141 | [0.1920, 0.2394] | Pass |
510(k) Summary
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| ACNS definition of ‘Frequent’ activity) | Ages 18+ | 95.71% | [91.30, 98.43] | 0.1343 | [0.1250, 0.1445] | Pass |
| --- | --- | --- | --- | --- | --- | --- |
| | Overall | 93.93% | [90.03, 96.52] | 0.1763 | [0.1670, 0.1859] | Pass |
| Seizure Episodes with Seizure Burden ≥50% (meeting ACNS definition of ‘Abundant’ activity) | Ages 1-11 | 96.67% | [87.50, 100.00] | 0.1561 | [0.1369, 0.1772] | Pass |
| | Ages 12-17 | 95.45% | [73.33, 100.00] | 0.0921 | [0.0776, 0.1082] | Pass |
| | Ages 18+ | 96.72% | [88.37, 100.0] | 0.0547 | [0.0480, 0.0615] | Pass |
| | Overall | 96.50% | [92.12, 98.77] | 0.08180 | [0.0754, 0.0885] | Pass |
| Seizure Episodes with Seizure Burden ≥90% (meeting ACNS definition of ‘Continuous’ activity) | Ages 1-11 | 92.59% | [76.00, 100] | 0.0843 | [0.0697, 0.1006] | Pass |
| | Ages 12-17 | 100.0% | [100, 100] | 0.0399 | [0.0301, 0.0511] | Pass |
| | Ages 18+ | 93.55% | [78.26, 100.0] | 0.0249 | [0.0204, 0.0299] | Pass |
| | Overall | 94.12% | [85.45, 98.48] | 0.03951 | [0.0351, 0.0443] | Pass |
| Acceptance Criteria: PPA: Lower bound of the 95% confidence interval ≥ 70% PPA FP/hr: Upper Bound of the 95% confidence interval ≤ 0.446 FP/hr | | | | | | |
## Subgroup Performance
Subgroup analyses were performed to assess the impact of EEG recording duration, site, and, as shown above, age. All subgroup analyses demonstrated acceptable variation among subgroups, supporting performance across the intended use population.
## Predetermined Change Control Plan (PCCP):
The Ceribell Seizure Detection Software has been cleared by the FDA with an Authorized PCCP. The Authorized PCCP outlines specific modifications intended to improve algorithm clinical or computational performance through the expansion of training data and optimization of the algorithm. The PCCP outlines Ceribell's data management and algorithm development practices, including how and when performance is evaluated.
The PCCP also defines validation requirements for algorithm updates. Prior to release, the updated algorithm is validated through testing against previously established acceptance criteria using an independent validation data set. Updates will be implemented using a validated Software Update process. When an update is performed, Ceribell will update this operator manual and notify customers of the update.
## Conclusions:
The Ceribell Seizure Detection Software has the same intended use as the predicate device. In addition, it has the same technological characteristic, clinical workflow, and underlying operating principles. The expansion of the intended patient age range for the Seizure Detection Software has been validated through performance testing using the same methodology and acceptance criteria as
510(k) Summary
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applied to the predicate device. Therefore, the Ceribell Seizure Detection Software is substantially equivalent to the cleared predicate device.
510(k) Summary
7
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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.