K252204 · Prolaio, Inc. · PPW · Dec 16, 2025 · Cardiovascular
Device Facts
Record ID
K252204
Device Name
prolaio eVO2peak Module (Version 1.0)
Applicant
Prolaio, Inc.
Product Code
PPW · Cardiovascular
Decision Date
Dec 16, 2025
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 870.2200
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Peak oxygen consumption (eVO2peak)
Locked deep-learning model
—
Mean Error (Bias): 1.47 mL/kg/min; Mean Absolute Error: 4.39 mL/kg/min
—
—
Clinical study of 228 patients undergoing cardiopulmonary exercise tests (CPET) at multiple centers in the United States.
—
Indications for Use
The prolaio eVO2peak Module (Version 1.0) is an all-software medical device intended to be used for estimating peak oxygen consumption (eVO2peak (mL/kg/min)) in adult patients for whom such VO2 Peak cardiopulmonary functional testing is desired. The prolaio eVO2peak Module uses as inputs: demographic data and single-lead electrocardiogram (sampled at least 125 Hz) and triaxial accelerometry data (sampled at 50 Hz) from qualified third-party FDA-cleared wearable medical sensors from patients carrying out activities of daily living. The prolaio eVO2peak Module is intended to provide information as an adjunctive aid in the clinical evaluation of patients by qualified clinicians who have the responsibility for interpreting its significance in connection with other standard of care clinical findings. The prolaio eVO2peak Module is not intended to be used as a diagnostic, for active patient monitoring, to be solely relied upon for determining patient status, or as an alarm device.
Device Story
The prolaio eVO2peak Module is a software-only (SaMD) library that estimates peak oxygen consumption (eVO2peak) in adult patients. It processes demographic data, single-lead ECG (≥125 Hz), and triaxial accelerometry (≥50 Hz) collected from third-party, FDA-cleared wearable sensors during activities of daily living. The device uses a locked deep-learning model trained against reference CPET data to transform these inputs into an eVO2peak estimate. The module is intended for use by qualified clinicians as an adjunctive tool for clinical evaluation; it does not provide a user interface or display, but outputs are integrated into external clinical systems. By providing an estimate of cardiopulmonary functional capacity without requiring a formal CPET, the device assists clinicians in assessing patient status in conjunction with other standard-of-care findings.
Clinical Evidence
Clinical validation study conducted at multiple US centers with 228 demographically diverse adult patients. The study compared eVO2peak estimates against reference VO2 Peak from cardiopulmonary exercise tests (CPET). Results showed a Mean Error (Bias) of 1.47 mL/kg/min [95% CI: 0.55, 2.27] and Mean Absolute Error of 4.39 mL/kg/min [95% CI: 3.82, 4.91]. Subgroup analysis for bike modality showed Mean Error of 0.97 mL/kg/min [95% CI: 0.01, 1.96].
Indicated for adult patients requiring estimation of peak oxygen consumption (eVO2peak) as an adjunctive aid in clinical evaluation. Not for use as a diagnostic, for active patient monitoring, or as an alarm device.
Regulatory Classification
Identification
The adjunctive cardiovascular status indicator is a prescription device based on sensor technology for the measurement of a physical parameter(s). This device is intended for adjunctive use with other physical vital sign parameters and patient information and is not intended to independently direct therapy.
Special Controls
*Classification.* Class II (special controls). The special controls for this device are:(1) Software description, verification, and validation based on comprehensive hazard analysis must be provided, including:
(i) Full characterization of technical parameters of the software, including any proprietary algorithm(s);
(ii) Description of the expected impact of all applicable sensor acquisition hardware characteristics on performance and any associated hardware specifications;
(iii) Specification of acceptable incoming sensor data quality control measures; and
(iv) Mitigation of impact of user error or failure of any subsystem components (signal detection and analysis, data display, and storage) on accuracy of patient reports.
(2) Scientific justification for the validity of the status indicator algorithm(s) must be provided. Verification of algorithm calculations and validation testing of the algorithm using a data set separate from the training data must demonstrate the validity of modeling.
(3) Usability assessment must be provided to demonstrate that risk of misinterpretation of the status indicator is appropriately mitigated.
(4) Clinical data must be provided in support of the intended use and include the following:
(i) Output measure(s) must be compared to an acceptable reference method to demonstrate that the output measure(s) represent(s) the predictive measure(s) that the device provides in an accurate and reproducible manner;
(ii) The data set must be representative of the intended use population for the device. Any selection criteria or limitations of the samples must be fully described and justified;
(iii) Agreement of the measure(s) with the reference measure(s) must be assessed across the full measurement range; and
(iv) Data must be provided within the clinical validation study or using equivalent datasets to demonstrate the consistency of the output and be representative of the range of data sources and data quality likely to be encountered in the intended use population and relevant use conditions in the intended use environment.
(5) Labeling must include the following:
(i) The type of sensor data used, including specification of compatible sensors for data acquisition;
(ii) A description of what the device measures and outputs to the user;
(iii) Warnings identifying sensor reading acquisition factors that may impact measurement results;
(iv) Guidance for interpretation of the measurements, including warning(s) specifying adjunctive use of the measurements;
(v) Key assumptions made in the calculation and determination of measurements;
(vi) The measurement performance of the device for all presented parameters, with appropriate confidence intervals, and the supporting evidence for this performance; and
(vii) A detailed description of the patients studied in the clinical validation (
*e.g.,* age, gender, race/ethnicity, clinical stability) as well as procedural details of the clinical study.
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FDA
U.S. FOOD & DRUG
ADMINISTRATION
December 16, 2025
prolaio, Inc.
George Allen Hides
Vice President, Regulatory, Quality, and Clinical
230 W Monroe, Unit 2560
Chicago, Illinois 60606
Re: K252204
Trade/Device Name: prolaio eVO2peak Module (Version 1.0)
Regulation Number: 21 CFR 870.2200
Regulation Name: Adjunctive Cardiovascular Status Indicator
Regulatory Class: Class II
Product Code: PPW
Dated: December 1, 2025
Received: December 1, 2025
Dear George Allen Hides:
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.
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K252204 - George Allen Hides
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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.
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-
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K252204 - George Allen Hides
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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,
Stephen C. Browning -S
LCDR Stephen Browning
Assistant Director
Division of Cardiac Electrophysiology,
Diagnostics, and Monitoring Devices
Office of Cardiovascular Devices
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
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FORM FDA 3881 (6/20)
Page 1 of 1
PSC Publishing Services (301) 443-6740
EF
| DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration Indications for Use | Form Approved: OMB No. 0910-0120 Expiration Date: 06/30/2023 See PRA Statement below. |
| --- | --- |
| 510(k) Number (if known) K252204 | |
| Device Name prolaio eVO2max Module (Version 1.0) | |
| Indications for Use (Describe) | |
| The prolaio eVO2peak Module (Version 1.0) is an all-software medical device intended to be used for estimating peak oxygen consumption (eVO2peak (mL/kg/min)) in adult patients for whom such VO2 Peak cardiopulmonary functional testing is desired. The prolaio eVO2peak Module uses as inputs: demographic data and single-lead electrocardiogram (sampled at least 125 Hz) and triaxial accelerometry data (sampled at 50 Hz) from qualified third-party FDA-cleared wearable medical sensors from patients carrying out activities of daily living. The prolaio eVO2peak Module is intended to provide information as an adjunctive aid in the clinical evaluation of patients by qualified clinicians who have the responsibility for interpreting its significance in connection with other standard of care clinical findings. The prolaio eVO2peak Module is not intended to be used as a diagnostic, for active patient monitoring, to be solely relied upon for determining patient status, or as an alarm device. | |
| 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. | |
| This section applies only to requirements of the Paperwork Reduction Act of 1995. *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.* | |
| The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to: Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff@fda.hhs.gov | |
| "An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number." | |
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K252204
# 510(k) Summary
## DATE PREPARED
December 15, 2025
## SUBMITTER
prolaio, Inc.
230 W Monroe
Suite 2560
Chicago IL 60606 USA
Phone: (855) 869-9054
## CONTACT
George Allen Hides
Vice President Regulatory, Quality and Clinical Affairs at prolaio, Inc.
Email: george.hides@prolaio.com
Phone: (312) 654-1010
This 510(k) Summary has been prepared in accordance with CFR 21 Part 807.92.
## DEVICE
| Category | Description |
| --- | --- |
| Device Trade Name | prolaio eVO2peak Module (Version 1.0) |
| Common Name | Adjunctive Cardiovascular Status Indicator |
| Classification Name | Adjunctive Cardiovascular Status Indicator |
| Class | 2 |
| Regulation Number | 21 CFR§870.2200 |
| Product Code | PPW |
## PREDICATE DEVICE
| Category | Description |
| --- | --- |
| Device Trade Name | CipherOx CRI M1 (K173929) |
| Common Name | Adjunctive Cardiovascular Status Indicator |
| Classification Name | Adjunctive Cardiovascular Status Indicator |
| Class | 2 |
| Regulation Number | 21 CFR§870.2200 |
| Product Code | PPW |
| Note | The predicate has not been subject to a design-related recall. |
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# DEVICE DESCRIPTION
The prolaio eVO2peak Module is a software-only Class II SaMD Python library. It processes stored physiologic data—single-lead ECG and triaxial accelerometry. The module applies a locked deep-learning model trained against reference VO2 Peak from cardiopulmonary exercise test (CPET). The module returns mean eVO2peak and contextual information allowing clinicians to use eVO2peak in concert with other clinical signs and parameters.
# INDICATIONS FOR USE
The prolaio eVO2peak Module (Version 1.0) is an all-software medical device intended to be used for estimating peak oxygen consumption (eVO2peak (mL/kg/min)) in adult patients for whom such VO2 Peak cardiopulmonary functional testing is desired.
The prolaio eVO2peak Module uses as inputs: demographic data and single-lead electrocardiogram (sampled at least 125 Hz) and triaxial accelerometry data (sampled at 50 Hz) from qualified third-party FDA-cleared wearable medical sensors from patients carrying out activities of daily living.
The prolaio eVO2peak Module is intended to provide information as an adjunctive aid in the clinical evaluation of patients by qualified clinicians who have the responsibility for interpreting its significance in connection with other standard of care clinical findings.
The prolaio eVO2peak Module is not intended to be used as a diagnostic, for active patient monitoring, to be solely relied upon for determining patient status, or as an alarm device.
# INTENDED USE
Intended Use and Indications for Use are the same.
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PREDICATE DEVICE COMPARISON
| Device Functionality | Flashback Technologies CipherOx CRI M1 | prolaio eVO2peak Module (Version 1.0) | Comments |
| --- | --- | --- | --- |
| Comparison | Traditional 510(k) Predicate | Traditional 510(k) Candidate Device | — |
| Manufacturer | Flashback Technologies | prolaio Inc. | — |
| 510(k) Number | K173929 | K252204 | — |
| Classification | Class 2 21CFR§2200 Adjunctive Cardiovascular Status Indicator | Class 2 21CFR§2200 Adjunctive Cardiovascular Status Indicator | Same |
| Product Code | PPW | PPW | Same |
| Intended Use | The CipherOx™ CRI M1 is indicated for continuous noninvasive monitoring of functional oxygen saturation of arterial hemoglobin (SpO2), pulse rate (measured by an SpO2 sensor), and the Compensatory Reserve Index (CRI), which trends changes in intravascular volume relative to the individual patient's response to hypovolemia.
For patients with a finger thickness of 0.3” to 1” in hospital and pre-hospital settings.
CRI trends with changes in intravascular volume relative to the individual patient's response to hypovolemia, and should only be used by qualified medical providers as an adjunct to rather than as a replacement for traditional hemodynamic measures. CRI is indicated for adults (19-36 years old) in the supine position under non-motion conditions and without cardiovascular disease. CRI has not been studied in trauma patients. | The prolaio eVO2peak Module (Version 1.0) is an all-software medical device intended to be used for estimating peak oxygen consumption (eVO2peak (mL/kg/min)) in adult patients for whom such VO2Max cardiopulmonary functional testing is desired.
The prolaio eVO2peak Module uses as inputs: demographic data and single-lead electrocardiogram (sampled at least 125 Hz) and triaxial accelerometry data (sampled at 50 Hz) from qualified third-party FDA-cleared wearable medical sensors from patients carrying out activities of daily living.
The prolaio eVO2peak Module is intended to provide information as an adjunctive aid in the clinical evaluation of patients by qualified clinicians who have the responsibility for interpreting its significance in connection with other standard of care clinical findings.
The prolaio eVO2peak Module is not intended to be used as a diagnostic, for active patient monitoring, to be solely relied upon for determining patient status, or as an alarm device. | Similar
Estimates cardiovascular parameter of clinical interest
Adult population
Used by qualified medical professionals
Adjunctive aid for retrospective analysis
Not a diagnostic, alarm device, not for active patient monitoring, not to be solely relied upon to make clinical decisions |
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| Device Functionality | Flashback Technologies CipherOx CRI M1 | prolaio eVO2peak Module (Version 1.0) | Comments |
| --- | --- | --- | --- |
| Level of Concern | Moderate^{1} | Basic Documentation Level^{1} | Same
Same software documentation level |
| Environment of Use | Hospital and prehospital settings | Ambulatory Settings | Same |
| Biosensor Requirements | Nonin non-invasive, finger-worn, pulse ox sensor | Non-invasive chest-worn, single-lead ECG biosensor with ECG 125 Hz or higher and triaxial accelerometer 50 Hz or higher | Similar
Non-invasive commercial medical biosensor devices provide input to computational algorithm for analysis |
| Cardiovascular Parameter | SpO2
Pulse Rate
CRI™ Algorithm | eVO2peak | Similar
Different metrics but both intended to estimate clinical physiologic cardiovascular parameters |
| Software | Yes – CRI™ Algorithm
The CRI™ algorithm trends intravascular volume using non-invasive arterial pulsatile waveform signals by continuously comparing extracted waveforms to a reference model. CipherOx™ CRI system operates on the photoplethysmograph (PPG) waveform used in pulse oximetry to estimate CRI. | Yes – eVO2peak
eVO2peak estimates peak oxygen consumption. | Same |
| Display | Yes.
M1 User Interface module is a small, portable, battery-powered multiparameter monitor | No | Similar
eVO2peak Module does not include an interface or display but its outputs can be integrated into such an external system for display |
| Patient Population | Adult (19 – 36 yrs) | Adult | Same |
| Alarm | No | No | Same |
| Active Patient Monitoring | No | No | Same |
1 "Minor", "Moderate", "Major" Levels of Concern has been replaced with "Basic" or "Enhanced" Documentation Levels in Guidance for Industry - Content of Premarket Submissions for Device Software Function (2023).
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A detailed Substantial Equivalence Table is available in the submission, comparing device characteristics including algorithmic methodology, input/output data types, performance metrics, system integration pathways, and risk control measures.
# SPECIAL CONTROLS
Adjunctive cardiovascular status indicator devices include several special controls, as follows:
| Control | Evidence |
| --- | --- |
| Software description, verification, and validation based on comprehensive hazard analysis must be provided. | |
| Full characterization of technical parameters of the software, including any proprietary algorithm(s) | Software Description |
| Description of the expected impact of all applicable sensor acquisition hardware characteristics on performance and any associated hardware specifications | Software Description |
| Specification of acceptable incoming sensor data quality control measures | Software Description |
| Mitigation of impact of user error or failure of any subsystem components (signal detection and analysis, data display, and storage) on accuracy of patient reports | Risk Analysis
Usability Assessment |
| Scientific justification for the validity of the status indicator algorithm(s) must be provided. Verification of algorithm calculations and validation testing of the algorithm using a data set separate from the training data must demonstrate the validity of modeling. | Device Description |
| Usability assessment must be provided to demonstrate that risk of misinterpretation of the status indicator is appropriately mitigated. | Usability Assessment |
| Clinical data must be provided in support of the intended use. | |
| Output measure(s) must be compared to an acceptable reference method to demonstrate that the output measure(s) represent(s) the predictive measure(s) that the device provides in an accurate and reproducible manner | Validation Report |
| The data set must be representative of the intended use population for the device. Any selection criteria or limitations of the samples must be fully described and justified | Validation Report |
| Agreement of the measure(s) with the reference measure(s) must be assessed across the full measurement range; | Validation Report |
| Data must be provided within the clinical validation study or using equivalent datasets to demonstrate the consistency of the output and be representative of the range of data sources and data quality likely to be encountered in the intended use population and relevant use conditions in the intended use environment | Validation Report |
| Labeling | |
| The type of sensor data used, including specification of compatible sensors for data acquisition | User Guide and Installation Manual & User About Page |
| A description of what the device measures and outputs to the user | User Guide and Installation Manual & User About Page |
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| Control | Evidence |
| --- | --- |
| Warnings identifying sensor reading acquisition factors that may impact measurement results | User Guide and Installation Manual & User About Page |
| Guidance for interpretation of the measurements, including warning(s) specifying adjunctive use of the measurements | User Guide and Installation Manual & User About Page |
| Key assumptions made in the calculation and determination of measurements | User Guide and Installation Manual & User About Page |
| The measurement performance of the device for all presented parameters, with appropriate confidence intervals, and the supporting evidence for this performance | User Guide and Installation Manual |
| A detailed description of the patients studied in the clinical validation (e.g., age, gender, race/ethnicity, clinical stability) as well as procedural details of the clinical study | User About Page |
# PERFORMANCE DATA
Risk Management:
The prolaio peak Module underwent risk analysis per ISO 14971. All residual risks post-mitigation were evaluated as acceptable.
Cybersecurity:
Cybersecurity testing was conducted under the principles in FDA's 2023 Cybersecurity Premarket Guidance. The module includes encryption, access control, input validation, and logging as part of its secure product development framework. All residual risks post-mitigation were evaluated as acceptable.
Human Factors Engineering (HFE):
Although the device lacks a user interface, HFE principles under IEC 62366 and FDA's 2016 Applying Human Factors and Usability Engineering to Medical Devices were applied to ensure safe integration and interpretation of outputs by clinical users. Documentation includes instructions for integration and intended context of use. Evaluation supported that device users could safely use the device.
AI/ML Compliance:
The module was developed in accordance with FDA's Good Machine Learning Practice (GMLP) principles. Training and validation datasets were diverse and clinically representative of the target population. The algorithm is locked and does not perform autonomous updates.
Software V&V
Verification and validation activities per IEC 62304 confirmed functional performance, scalability, and compatibility to ensure that the use of the software fulfils the intended use without causing any unacceptable risks.
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Clinical Validation:
Validation of prolaio eVO2peak was performed using data from patients undergoing cardiopulmonary exercise tests (CPET) in a clinical study including multiple centers in the United States. Overall, 228 patients were enrolled in multiple regions and were demographically diverse and representative of the intended population. Performance of prolaio eVO2peak met the target accuracy, reliability/consistency, and bias criteria.
| Results | Overall [95%CI] | Bike Modality [95%CI] |
| --- | --- | --- |
| Mean Error (Bias) (mL/kg/min) | 1.47 [0.55, 2.27] | 0.97 [0.01, 1.96] |
| Mean Absolute Error (mL/kg/min) | 4.39 [3.82, 4.91] | 4.07 [3.52, 4.69] |
## CONCLUSIONS
The results from risk management, design control, and clinical testing support that the prolaio eVO2peak Module is substantially equivalent to the predicate device. The proposed device does not raise new questions of safety or effectiveness compared to the predicate, or from differences to the predicate, for the proposed indications for use.
K252204
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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.
