(195 days)
Not Found
Yes
The device description explicitly states that it uses a "deep learning algorithm" for image enhancement, which is a subset of machine learning and artificial intelligence.
No.
The device is described as a software solution for enhancing brain MRI images by reducing noise and increasing sharpness. Its primary functions are image processing, quality checks, and monitoring, not directly treating a medical condition or restoring a bodily function.
No
Explanation: The device enhances MRI images by reducing noise and increasing sharpness. It processes existing images to improve their quality for viewing, but it does not perform a medical diagnosis itself or provide information leading to a diagnosis. Its function is image manipulation, not diagnostic interpretation.
Yes
The device description explicitly states "SwiftMR, is software used as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners. The device only processes DICOM images for the end User..." and details its functions as image processing, quality check, and progress monitoring, all performed through software. There is no mention of accompanying hardware components included with the device itself.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs analyze biological samples: In Vitro Diagnostics are designed to examine specimens taken from the human body, such as blood, urine, or tissue, to provide information about a person's health.
- This device processes medical images: SwiftMR's intended use and description clearly state that it processes MRI images in DICOM format. It enhances the quality of these images for interpretation by radiology technologists.
- No mention of biological sample analysis: The document does not mention any interaction with or analysis of biological samples.
Therefore, SwiftMR falls under the category of medical imaging software, not an In Vitro Diagnostic device.
No
The input document does not contain any explicit statement that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device. The 'Control Plan Authorized (PCCP) and relevant text' section is explicitly marked as "Not Found".
Intended Use / Indications for Use
SwiftMR is a stand-alone software solution intended to be used for acceptance, enhancement and transfer of brain MRI images in DICOM format. It can be used for noise reduction and increasing image sharpness for non-contrast enhanced MRI images.
SwiftMR is not intended for use on mobile devices.
Product codes (comma separated list FDA assigned to the subject device)
LLZ
Device Description
SwiftMR, is software used as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners. The device only processes DICOM images for the end User and is intended to be used by radiology technologists in an imaging center, clinic, or hospital.
The device's inputs are standard of care MRI images in DICOM format. The deep learning algorithm produces enhanced images as outputs with reduced noise and increased sharpness in DICOM format. The device applies both denoising and sharpness increase functions simultaneously.
SwiftMR provides an automatic image quality enhancement function for MR images acquired in various environments. SwiftMR can only be used for professional purposes and is not intended for use on mobile devices.
SwiftMR 's automation procedure is as follows:
- Upload MR images that have been taken or converted to the DICOM format
- Image quality enhancement using Deep Learning model
- Download enhanced MR image as DICOM format
There are two deep learning algorithms that should be selected by users according to the pulse sequences. One is for the general pulse sequences and the other is for the TOF pulse sequences. The two deep learning algorithms share network architecture, input data generation method, training procedures. The only difference between the two deep learning algorithms is the input / label dataset used for training.
After integration with the facilities PACS, SwiftMR performs image processing in the background automatically. At the same time, SwiftMR allows logged-in users to use its functions and change product settings through the client application. When logged in as the System Admin, the function is available to the control automation procedure and system change settings. On the User side, the User can retrieve the results of image processing in the form of a worklist by login to the user account.
The software provides three main functions, which are image processing, quality check and progress monitoring.
The software is intended to run automatically in the background so that it does not interrupt the workflow of users. When the user executes MR scans and saves the images in PACS as he/she usually does, the newly acquired images are automatically uploaded to the server and registered in the database (DB) for image processing. Once image processing is complete, the images are sent back to PACS.
If the user wishes to monitor this automated workflow to check on the status of image processing, he/she can log in to the Client App, and notifications will be received upon each task completion. Detailed information is also available when the main worklist is opened, and MR Study and/or Series in concern is selected.
A settings menu is provided in the form of a user interface to enable users and system admin to modify software settings as required by the institution or respective user.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
MRI
Anatomical Site
brain
Indicated Patient Age Range
Not Found
Intended User / Care Setting
radiology technologists in an imaging center, clinic, or hospital.
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)
Validation testing: Performance test was conducted using retrospective clinical images for both noise reduction and sharpness increase functions.
For the noise reduction performance, acceptance criteria were defined that the A. average signal-to-noise ratio (SNR) of the SwiftMR-processed image series is increased by 40% or more compared to the value of the original image series. This test passed.
B. For the sharpness increase performance, acceptance criteria were defined that the FWHM of a selected region of interest (ROI) is decreased by 0.13% or more after applying SwiftMR for at least 90% of the test datasets. This test passed.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
average signal-to-noise ratio (SNR) of the SwiftMR-processed image series is increased by 40% or more compared to the value of the original image series.
FWHM of a selected region of interest (ROI) is decreased by 0.13% or more after applying SwiftMR for at least 90% of the test datasets.
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
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
0
October 14, 2021
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services seal on the left and the FDA acronym and name on the right. The FDA acronym and name are in blue, with the acronym in a square and the name in a sans-serif font.
AIRS Medical Inc. % Jihyeon Seo RA Manager 8-9F, CS Tower, 1838, Nambusunhwan-ro, Gwanak-gu Seoul. Seoul 08788 SOUTH KOREA
Re: K210999
Trade/Device Name: SwiftMR Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: LLZ Dated: August 31, 2021 Received: September 2, 2021
Dear Jihyeon Seo:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. 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 located 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.
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 of medical device-related adverse events) (21 CFR 803) for
1
devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-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 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 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-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/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-device-advice-comprehensive-regulatoryassistance/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.
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
510(k) Number (if known) K210999
Device Name SwiftMR
Indications for Use (Describe)
SwiftMR is a stand-alone software solution intended to be used for acceptance, enhancement and transfer of brain MRI images in DICOM format. It can be used for noise reduction and increasing image sharpness for non-contrast enhanced MRI images.
SwiftMR is not intended for use on mobile devices.
| Type of Use (Select one or both, as applicable) | |
|---|---|
| X Prescription Use (Part 21 CFR 801 Subpart D) | Over-The-Counter Use (21 CFR 801 Subpart C) |
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K210999
Image /page/3/Picture/1 description: The image shows the logo for AIRS Medical. The logo consists of the word "AIRS" in large, bold, black letters. Below the word "AIRS" is the word "MEDICAL" in smaller, gray letters. The font is sans-serif and modern.
510(k) Summary
This 510(k) Summary of safety and effectiveness information is being submitted in accordance with the requirements of 21 CFR 807.92.
I. SUBMITTER
Ms. Jihyeon Seo RA Manager AIRS Medical Inc. 8-9F, CS Tower, 1838, Nambusunhwan-ro Gwanak-gu, Seoul, 08788, Republic of Korea Phone: +82-70-777-5061 FAX: +82-2-6280-3185 Email: seo.kate@airsmed.com
Date Prepared: March 29, 2021
II. DEVICE
Name of Device: SwiftMR Common or Usual Name: Medical Image Management and Processing System Classification Name: system, image processing, radiological (21 CFR 892.2050) Requlatory Class: II Product Code: LLZ
III. PREDICATE DEVICE
Primary Predicate Device: SubtleMR – K191688 by Subtle Medical, Inc., Class II, CFR 892.2050, classification with product code LLZ.
IV. DEVICE DESCRIPTION
SwiftMR, is software used as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners. The device only processes DICOM images for the end User and is intended to be used by radiology technologists in an imaging center, clinic, or hospital.
The device's inputs are standard of care MRI images in DICOM format. The deep learning algorithm produces enhanced images as outputs with reduced noise and increased sharpness in DICOM format. The device applies both denoising and sharpness increase functions simultaneously.
SwiftMR provides an automatic image quality enhancement function for MR images acquired in various environments. SwiftMR can only be used for professional purposes and is not intended for use on mobile devices.
SwiftMR 's automation procedure is as follows:
- . Upload MR images that have been taken or converted to the DICOM format
- . Image quality enhancement using Deep Learning model
- . Download enhanced MR image as DICOM format
There are two deep learning algorithms that should be selected by users according to the pulse sequences. One is for the general pulse sequences and the other is for the TOF pulse sequences. The two deep learning algorithms share network architecture, input data generation method, training procedures. The only difference between the two deep learning algorithms is the input / label dataset used for training.
4
Image /page/4/Picture/0 description: The image shows the logo for AIRS Medical. The logo consists of the word "AIRS" in bold, black letters on the top line, and the word "MEDICAL" in gray letters on the bottom line. The "A" in AIRS is stylized to look like a triangle.
510(k) Summary
After integration with the facilities PACS, SwiftMR performs image processing in the background automatically. At the same time, SwiftMR allows logged-in users to use its functions and change product settings through the client application. When logged in as the System Admin, the function is available to the control automation procedure and system change settings. On the User side, the User can retrieve the results of image processing in the form of a worklist by login to the user account.
The software provides three main functions, which are image processing, quality check and progress monitoring.
The software is intended to run automatically in the background so that it does not interrupt the workflow of users. When the user executes MR scans and saves the images in PACS as he/she usually does, the newly acquired images are automatically uploaded to the server and registered in the database (DB) for image processing. Once image processing is complete, the images are sent back to PACS.
If the user wishes to monitor this automated workflow to check on the status of image processing, he/she can log in to the Client App, and notifications will be received upon each task completion. Detailed information is also available when the main worklist is opened, and MR Study and/or Series in concern is selected.
A settings menu is provided in the form of a user interface to enable users and system admin to modify software settings as required by the institution or respective user.
V. INDICATIONS FOR USE
SwiftMR is a stand-alone software solution intended to be used for acceptance, enhancement and transfer of brain MRI images in DICOM format. It can be used for noise reduction and increasing image sharpness for non-contrast enhanced MRI images.
SwiftMR is not intended for use on mobile devices.
VI. COMPARISON OF TECHNOLOGICAL CHARACTERISTICS WITH THE PREDICATE DEVICES
The subject device and predicate are both software applications that are loaded into PACS. Both systems have been developed for image enhancement on DICOM images generated by an MRI. The subject device and the predicate device are substantially equivalent in the areas of general function, application, and intended use.
Any differences between the predicate and the subject device have no negative impact on the device safety or efficacy and does not raise any new potential or increased safety risks and is equivalent in performance to existing legally marketed devices.
| Item | Predicate Device
(K191688) | Subject Device
(SwiftMR) | Differences |
|--------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Physical
Characteristics | Software device that
operates on off-the-
shelf computer
hardware | Same as predicate | No Difference |
| Computer | Linux Compatible | PC Compatible | Differences are basically in the
computer operating system but also
may have some differences in the
processor speeds, amount of RAM
memory, monitors, and hard drive
space requirements. However, the
subject device and the predicate
device are substantially equivalent |
| | | | |
| | | | in the areas of technical
characteristics, general function,
application, and intended use and
the computer platform differences
do not raise any new potential
safety risks. Therefore, it is our
determination that there is "No
impact on safety or efficacy" and
there are no new potential or
increased safety risks. |
| DICOM
Standard | The software processes | Same as predicate | No Difference |
| Compliance | compliant image data | | |
| Modalities | MRI | Same as predicate | No Difference |
| Image
Enhancement
Algorithm
Description | The predicate software
implements an image
enhancement algorithm
using convolutional
neural network-based
filtering. Original
images are enhanced
by running through a
cascade of
filter banks, where
thresholding and
scaling operations are
applied. Separate
neural network-based
filters are obtained for
noise reduction and
sharpness increase.
The parameters of the
filters were obtained
through an image
guided optimization
process. | SwiftMR implements an
image enhancement
algorithm using
convolutional
neural network-based
filtering. Original
images are enhanced
by running through a
cascade of
filter banks, where
thresholding and
scaling operations are
applied. Neural
network-based filters
that simultaneously
perform noise reduction
and sharpness
increase functions are
obtained. The
parameters of the filters
were obtained through
an image guided
optimization process. | The only difference is in how the
neural network-based filter exists.
As for the predicate device, there
are separate filters for noise
reduction and sharpness increase.
On the other hand, there are neural
network-based filters that
simultaneously perform both
functions for the subject device.
However, the same functions, which
are noise reduction and sharpness
increase, are applied by the filters.
Therefore, the difference does not
raise new questions of safety or
effectiveness. |
| Workflow | The predicate software
operates on DICOM
files on the file system,
enhances the images,
and stores the
enhanced images on
the file system. The
receipt of original
DICOM image files and
delivery of enhanced
images as DICOM files
depends on other
software systems.
Enhanced images co-
exist with the original
images. | Same as predicate | No Difference |
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MEDICA
510(k) Summary
6
510(k) Summary
VII. PERFORMANCE DATA
SwiftMR, has been assessed and has passed all predetermined testing criteria. The Validation Test Plan was designed to evaluate output functions.
Validation testing indicated that as required by the risk analysis, designated individuals performed all verification and validation activities and that the results demonstrated that the predetermined acceptance criteria were met.
The following tests were conducted for SwiftMR:
-
- Verification testing: Unit test, Intearation/system test conducted. These tests passed.
-
- Validation testing: Performance test was conducted using retrospective clinical images for both noise reduction and sharpness increase functions.
- For the noise reduction performance, acceptance criteria were defined that the A. average signal-to-noise ratio (SNR) of the SwiftMR-processed image series is increased by 40% or more compared to the value of the original image series. This test passed.
- B. For the sharpness increase performance, acceptance criteria were defined that the FWHM of a selected region of interest (ROI) is decreased by 0.13% or more after applying SwiftMR for at least 90% of the test datasets. This test passed.
Therefore, it was demonstrated that SwiftMR performance was shown to be substantially equivalent to the predicate device.
VIII. CONCLUSION
The information presented in the 510(k) for SwiftMR contains adequate information, data, and nonclinical test results to demonstrate substantial equivalence to the predicate device. SwiftMR was shown to be substantially equivalent to the predicate device in the areas of technical characteristics, general function, application, and does not raise different questions of safety and effectiveness.