(100 days)
Not Found
Yes
The device description explicitly states that it uses a "deep learning algorithm" and "Deep Learning model" for image enhancement, which are forms of machine learning.
No
The device is described as software that enhances MR images for better visualization by reducing noise and increasing sharpness. It processes images for radiologists and does not directly provide therapy or therapeutic benefit to patients.
No
This device is not a diagnostic device. It enhances the quality of MR images by reducing noise and increasing sharpness, but it does not interpret those images or provide any medical diagnosis or prognosis. It is a tool for image processing, not diagnosis.
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...". It also details the software's functions and workflow without mentioning any accompanying hardware components that are part of the medical 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 device description clearly state that it processes MR images (DICOM format). It enhances the quality of these images by reducing noise and increasing sharpness. It does not interact with or analyze biological samples.
Therefore, SwiftMR falls under the category of a medical device that processes imaging data, not an In Vitro Diagnostic.
No
The letter does not mention that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
SwiftMR is a stand-alone software solution intended to be used for acceptance, enhancement and transfer of brain. spine, knee, ankle, shoulder and hip MR images in DICOM format. It can be used for noise reduction and increasing image sharpness for non-contrast enhanced MR images.
SwiftMR is not intended for use on mobile devices.
Product codes
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 deep learning algorithm performs the denoising function and sharpening filter performs the sharpening function with the ability of adjusting the sharpness level from level 0 to level 5.
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:
-. Receive MR images that are in DICOM format
-. Image quality enhancement using Deep Learning model and sharpening filter
- Transfer enhanced MR image as DICOM format to PACS .
There are four deep learning algorithms: three are for the general pulse sequences and the other one is for the TOF pulse sequences. The four deep learning algorithms share the same network architecture, input and label data generation method, training procedures. The only difference between the four 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 view the processing status. When loqged 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 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 to PACS.
If the user wishes to monitor this automated workflow to check on the status of image processing, he/she can check the main page of the client application or toast messages will appear on the bottom right corner upon completion of each processing. After using the software, they should log out for security reasons.
A settings menu is provided in the form of a user interface to enable 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, spine, knee, ankle, shoulder and hip
Indicated Patient Age Range
22~90
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
The validation dataset consists of data of the following conditions:
-
- Manufacturer: SIEMENS. GE. PHILIPS
-
- Field Strength: 1.5T / 3.0T
-
- Anatomical region: Brain, Spine, Knee, Ankle, Shoulder, Hip
-
- Protocol: T1, T2, T2 FS, GRE, FLAIR, PD, PD FS, TOF, SWI, MPRAGE
-
- Demographics
- age: 22~90
- gender: Male (48.7%), Female (51.3%)
To show that the performance of the device is not hindered by site variability, in the validation dataset, we included data from sources not included in the training dataset.
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 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.
For the sharpness increase performance, acceptance criteria were defined that the FWHM of a selected region of interest (ROI) is decreased by by 0.43% (level 1), 1.7% (level 2), 2.3% (level 3), 3.6% (level 4), 4.5% (level 5) or more for at least 90% of the dataset. This test passed.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
SNR increase by 40% or more for noise reduction.
FWHM decrease by 0.43% (level 1), 1.7% (level 2), 2.3% (level 3), 3.6% (level 4), 4.5% (level 5) or more for at least 90% of the dataset for sharpness increase.
Predicate Device(s)
Reference Device(s)
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
May 25, 2022
Image /page/0/Picture/1 description: The image shows the logo for the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo includes the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG" in blue, with the word "ADMINISTRATION" underneath.
AIRS Medical Inc. % Jihyeon Seo RA Manager 8-9F, CS Tower, 1838, Nambusunhwan-ro, Gwanak-gu Seoul. Seoul 08788 KOREA
Re: K220416
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: April 15, 2022 Received: April 15, 2022
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 DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Ouality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
510(k) Number (if known) K220416
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. spine, knee, ankle, shoulder and hip MR images in DICOM format. It can be used for noise reduction and increasing image sharpness for non-contrast enhanced MR images.
SwiftMR is not intended for use on mobile devices.
| 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) |
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3
Image /page/3/Picture/0 description: The image shows the logo for AIRS Medical. The logo consists of the word "AIRS" in bold, black letters, with the "A" stylized to resemble an upside-down "V". Below the word "AIRS" is the word "MEDICAL" in smaller, gray letters. The overall design is simple and modern.
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: February 11, 2022
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) Regulatory Class: II Product Code: LLZ
III. PREDICATE DEVICE
Primary Predicate Device: SwiftMR - K210999 by AIRS 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 deep learning algorithm performs the denoising function and sharpening filter performs the sharpening function with the ability of adjusting the sharpness level from level 0 to level 5.
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:
- . Receive MR images that are in DICOM format
- . Image quality enhancement using Deep Learning model and sharpening filter
- Transfer enhanced MR image as DICOM format to PACS .
There are four deep learning algorithms: three are for the general pulse sequences and the other one is for the TOF pulse sequences. The four deep learning
4
algorithms share the same network architecture, input and label data generation method, training procedures. The only difference between the four 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 view the processing status. When loqged 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 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 to PACS.
If the user wishes to monitor this automated workflow to check on the status of image processing, he/she can check the main page of the client application or toast messages will appear on the bottom right corner upon completion of each processing. After using the software, they should log out for security reasons.
A settings menu is provided in the form of a user interface to enable 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, spine, knee, ankle, shoulder and hip MR images in DICOM format. It can be used for noise reduction and increasing image sharpness for non-contrast enhanced MR images.
SwiftMR is not intended for use on mobile devices.
VI. COMPARISON OF TECHNOLOGICAL CHARACTERISTICS WITH THE PREDICATE DEVICES
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.
5
| Item | Predicate Device
(SwiftMR (K210999)) | Subject Device
(SwiftMR) | Differences |
|--------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Physical
Characteristics | Software device that
operates on off-the-
shelf computer
hardware | Same as predicate | No Difference |
| Computer | PC Compatible | Same as predicate | No Difference |
| DICOM
Standard
Compliance | The software processes
DICOM-compliant
image data | Same as predicate | No Difference |
| Modalities | MRI | Same as predicate | No Difference |
| Image
Enhancement
Algorithm
Description | 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. | 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 perform noise
reduction are obtained.
The parameters of the
filters were obtained
through an image
guided optimization
process.
Sharpening filter is
additionally applied to
the deep learning
processed image. | The deep learning algorithm using
convolutional neural network-based
filtering performs denoising function
and newly added sharpness filter
performs sharpening function in the
subject device. |
| Deep learning
models | 1 General sequence
model
1 TOF sequence model | 3 General sequence
models
1 TOF sequence model | The general sequence model was
divided into three separate models
for each MRI manufacturer.
The TOF sequence model remains
the same as the predicate device. |
| Supported body
parts | Brain | Brain, Spine, knee,
ankle, shoulder, and
hip | Supported body parts have been
expanded to spine and MSK (knee,
ankle, shoulder, and hip) in addition
to brain. |
| 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 | SwiftMR operates on
DICOM files, enhances
the images, and stores
the enhanced images
on PACS. | The subject device can receive
DICOM files either from PACS or
from MR device.
It is possible to store only the
processed images. |
| Item | Predicate Device
(SwiftMR (K210999)) | Subject Device
(SwiftMR) | Differences |
| | 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. | Enhanced images can
be stored with the
original images or only
the enhanced images
can be stored. | |
6
VII. PERFORMANCE DATA
SwiftMR. has been assessed and tested 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, Integration/system test conducted. These tests passed.
-
- Validation testing: Performance test was conducted using retrospective clinical images for both noise reduction and sharpness increase functions.
- A. For the noise reduction performance, acceptance criteria were defined that the 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 by 0.43% (level 1), 1.7% (level 2), 2.3% (level 3), 3.6% (level 4), 4.5% (level 5) or more for at least 90% of the dataset. This test passed.
The validation dataset consists of data of the following conditions:
-
- Manufacturer: SIEMENS. GE. PHILIPS
-
- Field Strength: 1.5T / 3.0T
-
- Anatomical region: Brain, Spine, Knee, Ankle, Shoulder, Hip
-
- Protocol: T1, T2, T2 FS, GRE, FLAIR, PD, PD FS, TOF, SWI, MPRAGE
-
- Demographics
- age: 22~90
- gender: Male (48.7%), Female (51.3%)
To show that the performance of the device is not hindered by site variability, in the validation dataset, we included data from sources not included in the training dataset.
Therefore, it was demonstrated that SwiftMR performance was shown to be substantially equivalent to the predicate device.
7
Image /page/7/Picture/0 description: The image shows the logo for AIRS Medical. The logo consists of the word "AIRS" in bold, black capital letters, with the "A" stylized to resemble an inverted "V". Below "AIRS" is the word "MEDICAL" in smaller, gray capital letters. The overall design is clean and modern.
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.