(119 days)
Not Found
Yes
The device description clearly states the use of "deep learning model" for vessel selection, end-diastolic frame determination, and coronary vessel segmentation. While the description is titled "Does this device contain an AI model?", the inclusion of 'deep learning model' confirms the presence of an AI model.
No
Explanation: The device is a software package that analyzes X-ray angiographic images to assess coronary vessels and provides quantified results (QFR values). It does not directly provide therapy or treatment to patients. Instead, it is a tool to support clinicians in their assessment of coronary artery disease, which may inform therapeutic decisions, but it is not a therapeutic device itself.
Yes
The device is indicated for use in clinical settings to support the assessment of coronary vessels and provides "quantified results" which are "only intended for use by the responsible clinicians." It calculates a "QFR value" and generates a report with "analysis results (vessel sizing and QFR value)." These functions are consistent with a diagnostic device that aids in evaluating patient conditions.
Yes
The device is described as a "standalone software package" delivered as a "web-based application." It processes X-ray angiographic images to generate quantitative results for coronary vessel assessment and relies on a server system (software) without any mention of proprietary hardware. The hardware components involved (server, web browser, PACS, XA systems) are general-purpose computing and medical imaging hardware, not specific to the QFR device.
No.
The device analyzes X-ray angiographic images to assess coronary vessels and calculate a QFR value, which involves image processing and reconstruction, not in vitro examination of specimens derived from the human body.
No
The clearance letter does not explicitly state that the FDA has reviewed, approved, or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
QFR is indicated for use in clinical settings where validated and reproducible quantified results are needed to support the assessment of coronary vessels in X-ray angiographic images, for use on individual patients with coronary artery disease.
When the quantified results provided by QFR are used in a clinical setting on X-ray images of an individual patient. The results are only intended for use by the responsible clinicians.
Product codes
QHA, LLZ
Device Description
QFR is delivered as a standalone software package which is installed and running on a server system in the server room of the cathlab or the hospital. The server offers all functionalities that are required to work with the quantitative measurement in X-ray Angiographic (XA) patient studies supported by the QFR device.
QFR will be used by interventional cardiologists and researchers to obtain quantifications of lesions in coronary vessels. QFR has been developed as a web-based application to run in a web browser in the control room of the cathlab or in a hospital image review room. The import of images and the export of analysis results are via PACS.
The QFR device calculates the QFR value based on an anatomical model which is the result of a 3D reconstruction using the 2D contours obtained from two angiographic projections with angles >=25 degrees apart. These projections are acquired through monoplane or biplane XA systems. The algorithm involves three key steps: (1) Vessel Selection, (2) Contours Detection, and (3) QFR Analysis:
-
Vessel Selection: Angiograms are pre-classified by a deep learning model, identifying main epicardial vessels such as RCA, LAD, and LCx. The user then chooses the segment for analysis, and the software automatically selects end-diastolic image frames. The end-diastolic frame is determined as the angiogram frame with the vessel lumen adequately filled with contrast in both image sequences. This selection is either based on the patient's electrocardiogram when available or performed by the software using a deep learning model. It is essential for the user to verify this selection before proceeding with the analysis. The chosen end-diastolic frame serves as the projection view for the subsequent 3D reconstruction of the vessel.
-
Contours Detection. First, the system runs another deep learning model for coronary vessel segmentation as input to identify anatomical corresponding points on both projections for automatic correction of the system distortions introduced by the isocenter offset and the respiration-induced heart motion. Second, begins the automatic detection of start and end positions of the vessel segment to be reconstructed on the projection views, and extract its contours and centerline. Third, the position of the start and end point must be confirmed by the user.
-
QFR Analysis: The QFR value is computed from the arterial and reference diameter function calculated from the 3D reconstruction based on the contours detected on the cross-sections of the vessel segment, and the patient-specific volumetric flow rate calculated from the automated TIMI frame count. The reference diameter and bifurcations are used to determine the flow distribution at coronary bifurcations and calculate the reference diameter function. The reconstructed 3D model is used to calculate the QFR value.
A report is generated by QFR that shows patient information, image acquisition information (both obtained from the DICOM input), analysis results (vessel sizing and QFR value) and snapshot images showing the vessel boundaries.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
X-ray Angiographic (XA)
Anatomical Site
coronary vessels / coronary arteries
Indicated Patient Age Range
Not Found
Intended User / Care Setting
interventional cardiologists and researchers / clinical settings, cathlab, hospital image review room
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Not Found
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Performance evaluation of vessel classification:
- Acceptance criterion: 80% for correct vessel classification.
- Results:
- RCA: 96% correct
- LAD: 88% correct
- LCx: 78% correct
- Key results: On average the 80% acceptance criterion was satisfied.
Performance evaluation of start and end point detection:
- Acceptance criterion: 80% for correct start and end point detection, with only 10% allowed to be completely wrong.
- Results (representative dataset):
- 77% correct result
- 11% small deviation (needed no correction)
- 8% wrong result (needed correction)
- 4% gave no result
- Key results: 88% satisfies the 80% criterion and 8% satisfies the 10% criterion.
Performance evaluation of the determination of the ED frame:
- Acceptance criterion: 80% for correct detection of the ED.
- Results (representative dataset):
- Analytical algorithm using ECG data: 83%
- AI/ML model using image data: 81%
Performance evaluation of the new flow velocity calculation:
- Acceptance criterion: Defined based on QFR results, stricter than reproducibility of FFR measurements.
- Key results: Ensured that the automatic flow calculation was not outperformed by manual indication.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Not Found
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 892.1600 Angiographic x-ray system.
(a)
Identification. An angiographic x-ray system is a device intended for radiologic visualization of the heart, blood vessels, or lymphatic system during or after injection of a contrast medium. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.
FDA 510(k) Clearance Letter - QFR (3.0)
Page 1
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
Doc ID # 04017.07.05
April 4, 2025
QFR Solutions bv
℅ Bob Goedhart
Head of QARA
Schuttersveld 9
LEIDEN, ZH 2316XG
NETHERLANDS
Re: K243769
Trade/Device Name: QFR (3.0)
Regulation Number: 21 CFR 892.1600
Regulation Name: Angiographic X-Ray System
Regulatory Class: Class II
Product Code: QHA, LLZ
Dated: December 6, 2024
Received: March 5, 2025
Dear Bob Goedhart:
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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K243769 - Bob Goedhart Page 2
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-
Page 3
K243769 - Bob Goedhart Page 3
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,
Lu Jiang
Lu Jiang, Ph.D.
Assistant Director
Diagnostic X-Ray Systems Team
DHT8B: Division of Radiological Imaging
Devices and Electronic Products
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
Page 4
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)
K243769
Device Name
QFR (3.0)
Indications for Use (Describe)
QFR is indicated for use in clinical settings where validated and reproducible quantified results are needed to support the assessment of coronary vessels in X-ray angiographic images, for use on individual patients with coronary artery disease.
When the quantified results provided by QFR are used in a clinical setting on X-ray images of an individual patient. The results are only intended for use by the responsible clinicians.
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
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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."
Page 5
QFR Solutions B.V.
Schuttersveld 9, 2316XG Leiden
P.O. Box 384. 2300 AJ Leiden, the Netherlands
P +31 71 522 32 44 | medisimaging.com | sales@medisimaging.com
Rabobank BIC: RABONL2U
IBAN: NL92RABO 031 5335 602
Chamber of Commerce: 67554172
510(k) Summary
Summary in accordance with the requirements of 21 CFR Part 807.92(a).
Submitter
Manufacturer: QFR Solutions bv
Address: Schuttersveld 9, 2316 XG Leiden, The Netherlands
Contact Person: B. Goedhart, Head of QARA
Date Prepared: April 4, 2025
Email: bgoedhart@qfrsolutionsglobal.com
Telephone: +31 71 522 3244
Fax: +31 71 521 5617
Device
Device Name: QFR (3.0)
Common Name: QFR (3.0)
Classification Name: Angiographic X-ray system
Regulatory Class: II
Regulation: 21 CFR 892.1600
Classification Product Code: QHA
Subsequent Product Code: LLZ
Predicate Device
Predicate Device: QANGIO XA 3D (K182611)
This predicate device has not been subject to a design-related recall.
Device Description
QFR is delivered as a standalone software package which is installed and running on a server system in the server room of the cathlab or the hospital. The server offers all functionalities that are required to work with the quantitative measurement in X-ray Angiographic (XA) patient studies supported by the QFR device.
QFR will be used by interventional cardiologists and researchers to obtain quantifications of lesions in coronary vessels. QFR has been developed as a web-based
Page 6
application to run in a web browser in the control room of the cathlab or in a hospital image review room. The import of images and the export of analysis results are via PACS.
The QFR device calculates the QFR value based on an anatomical model which is the result of a 3D reconstruction using the 2D contours obtained from two angiographic projections with angles ≥25º apart. These projections are acquired through monoplane or biplane XA systems. The algorithm involves three key steps: (1) Vessel Selection, (2) Contours Detection, and (3) QFR Analysis:
1. Vessel Selection: Angiograms are pre-classified by a deep learning model, identifying main epicardial vessels such as RCA, LAD, and LCx. The user then chooses the segment for analysis, and the software automatically selects end-diastolic image frames. The end-diastolic frame is determined as the angiogram frame with the vessel lumen adequately filled with contrast in both image sequences. This selection is either based on the patient's electrocardiogram when available or performed by the software using a deep learning model. It is essential for the user to verify this selection before proceeding with the analysis. The chosen end-diastolic frame serves as the projection view for the subsequent 3D reconstruction of the vessel.
2. Contours Detection. First, the system runs another deep learning model for coronary vessel segmentation as input to identify anatomical corresponding points on both projections for automatic correction of the system distortions introduced by the isocenter offset and the respiration-induced heart motion. Second, begins the automatic detection of start and end positions of the vessel segment to be reconstructed on the projection views, and extract its contours and centerline. Third, the position of the start and end point must be confirmed by the user.
3. QFR Analysis: The QFR value is computed from the arterial and reference diameter function calculated from the 3D reconstruction based on the contours detected on the cross-sections of the vessel segment, and the patient-specific volumetric flow rate calculated from the automated TIMI frame count. The reference diameter and bifurcations are used to determine the flow distribution at coronary bifurcations and calculate the reference diameter function. The reconstructed 3D model is used to calculate the QFR value.
A report is generated by QFR that shows patient information, image acquisition information (both obtained from the DICOM input), analysis results (vessel sizing and QFR value) and snapshot images showing the vessel boundaries.
Intended Use
QFR is software intended to be used for performing calculations in X-ray angiographic images of the coronary arteries. QFR enables interventional cardiologists and researchers to obtain quantifications of one or more lesions in the analyzed coronary vessel segment. In particular, QFR provides:
- Quantitative results of coronary vessel segments based on a 3D reconstructed model;
- Dimensions of the cardiovascular vessels and lesions;
- Quantification of the pressure drop in coronary vessels.
Page 7
Indications for Use
QFR is indicated for use in clinical settings where validated and reproducible quantified results are needed to support the assessment of coronary vessels in X-ray angiographic images, for use on individual patients with coronary artery disease.
When the quantified results provided by QFR are used in a clinical setting on X-ray images of an individual patient, the results are only intended for use by the responsible clinicians.
Comparison of Technological Characteristics with the Predicate Device
Substantial Equivalence Information
The predicate device is QANGIO XA 3D (K182611).
Intended Use and Indications for Use of the QFR device are identical to those of the predicate device.
| SUBSTANTIAL EQUIVALENCE COMPARISON TABLE | SUBJECT DEVICE: QFR (K243769) | PREDICATE DEVICE: QANGIO XA 3D (K182611) |
|---|---|---|
| Regulation | 892.1600 – Angiographic X-ray System | 892.1600 – Angiographic X-ray System |
| Product Code | QHA (Class 2) – X-ray Angiographic Imaging Based Coronary Vascular Simulation Software | QHA (Class 2) – X-ray Angiographic Imaging Based Coronary Vascular Simulation Software |
| Associated Product Code | LLZ | LLZ |
| Image modality | XA | XA |
| Input format | DICOM | DICOM |
| Patient study browser (used to select the patient study of interest) | Yes | Yes |
| Import study from PACS | Yes | Yes |
| Automated series loading | Yes | Yes |
| Interactive image handling (incl. windowing, zooming, panning, filtering, windowlevel) | Yes | Yes |
Page 8
| SUBSTANTIAL EQUIVALENCE COMPARISON TABLE | SUBJECT DEVICE: QFR (K243769) | PREDICATE DEVICE: QANGIO XA 3D (K182611) |
|---|---|---|
| Cine loop review with controls for speed, and frame-by-frame review. | Yes | Yes |
| ECG display (used for ECG signal display in synchrony with the review of the XA images) | Yes | Yes |
| ISO center calibration (provided by the manufacturer of the X-ray acquisition equipment) | Yes | Yes |
| Possibility to edit or delete an existing analysis | Yes | Yes |
| Multiple analyses possible on a single image | Yes | Yes |
| Report facility | Yes | Yes |
| Export results to PACS | Yes | Yes |
| Audit Trail | Yes | Yes |
| Guided workflow supporting step-by-step analysis | Yes | Yes |
| Selection of the angiographic series to be used in the analysis | Selection is now supported by an AI/ML model and manual correction | Only supported manual selection |
| Selection of the determination of the start and end points of the vessel to be analyzed | Selection is now supported by an AI/ML model and manual correction | Only supported manual selection |
Page 9
| SUBSTANTIAL EQUIVALENCE COMPARISON TABLE | SUBJECT DEVICE: QFR (K243769) | PREDICATE DEVICE: QANGIO XA 3D (K182611) |
|---|---|---|
| Determination of the ED frame within the series to be used | Selection is now supported by a combination of an AI/ML model and an analytical algorithm using the ECG data and manual correction | Only supported manual selection |
| Determination of start and end frame for flow velocity calculation | Automatic determination of the start and end frame is now supported by an analytical algorithm based on traditional image processing techniques or manual indication | Only supported manual indication of the start and end frame |
Performance Data
QFR algorithm improvements
Performance evaluation of vessel classification: Since this functionality aims at supporting the user and not to completely automate the functionality, an acceptance criterion of 80% for correct vessel classification was defined. For the vessel classification the following performance measures were obtained: 96% of the patient studies showing the RCA were selected correctly, 88% of the patient studies showing the LAD were selected correctly and 78% of the patient studies showing the LCX were selected correctly. On average the 80% acceptance criterion was satisfied.
Performance evaluation of start and end point detection: Since this functionality aims at supporting the user and not to completely automate the functionality, an acceptance criterion of 80% for correct start and end point detection was defined, where only 10% is allowed to be completely wrong. The performance of the AI/ML model using the image data performed 77% correct result, 11% showed a small deviation (needed no correction), 8% showed a wrong result (needed correction) and 4% gave no result in a representative dataset. In conclusion, 88% satisfies the 80% criterion and 8% satisfies the 10% criterion.
Performance evaluation: The same acceptance criteria were used for both the analytical algorithm as well as the trained AI/ML model: Since this functionality aims at
Page 10
supporting the user and not to completely automate the functionality, an acceptance criterion of 80% for correct detection of the ED was defined. The performance of the analytical algorithm using the ECG data performed 83% on a representative dataset. The performance of the AI/ML model using the image data performed 81% on a representative dataset.
Performance evaluation of the new flow velocity calculation: Since this functionality is directly related to the QFR outcome, an acceptance criterion was defined based on the QFR results. A validation process established acceptance criteria that were significantly stricter for the resulting QFR measurement than the reproducibility of FFR measurements. This ensured that, the automatic flow calculation, was not outperformed by manual indication.
For all of these algorithmic improvements the user is able to review and correct the results before the QFR value is calculated.
Conclusions
The QFR device has the same intended use and indications for use as the predicate device QANGIO XA 3D.
The QFR device complies with international process standards (ISO 13485, ISO 14971, IEC 62304, IEC 62366 and ISO 15223).
Testing and validation have produced results consistent with design input requirements.
The non-clinical performance data demonstrates that the QFR device performs as intended in the specified use conditions and thereby performs comparable to its predicate device QANGIO XA 3D.
Based on the clinical performance data as documented above, the QFR device was found to perform as intended in the specified use conditions and thereby performs comparable to its predicate device QANGIO XA 3D.
During the development, potential hazards were controlled by a risk management plan, including risk analysis, risk mitigation, verification and evaluation. Results have been summarized in the risk management report and cybersecurity assessment report.
QFR Solutions bv concludes that QFR is a safe and effective medical device and performs comparable to its predicate device QANGIO XA 3D.