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.

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.

AI/ML Overview

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) submission:

1. Table of Acceptance Criteria and Reported Device Performance

Performance Metric	Acceptance Criteria	Reported Device Performance
Noise Reduction (SNR)	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." (Explicitly stated that the acceptance criteria for noise reduction were met.)
Sharpness Increase (FWHM)	The Full Width at Half Maximum (FWHM) of a selected region of interest (ROI) is decreased by: - 0.43% or more for level 1 - 1.7% or more for level 2 - 2.3% or more for level 3 - 3.6% or more for level 4 - 4.5% or more for level 5 for at least 90% of the dataset.	"This test passed." (Explicitly stated that the acceptance criteria for sharpness increase were met.)

2. Sample Size Used for the Test Set and Data Provenance

Sample Size: The document does not explicitly state the exact numerical sample size (number of images or patients) for the validation test set. It mentions the dataset includes various manufacturers (SIEMENS, GE, PHILIPS), field strengths (1.5T/3.0T), anatomical regions (Brain, Spine, Knee, Ankle, Shoulder, Hip), protocols (T1, T2, T2 FS, GRE, FLAIR, PD, PD FS, TOF, SWI, MPRAGE), and demographics (age 22-90, gender Male 48.7%/Female 51.3%). It also states that the validation dataset included data from sources not included in the training dataset to demonstrate performance is not hindered by site variability.
Data Provenance:
- Country of Origin: Not specified in the provided text.
- Retrospective or Prospective: The study used "retrospective clinical images" for performance testing.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

The document does not specify the number of experts or their qualifications for establishing ground truth for the test set. The acceptance criteria for noise reduction (SNR) and sharpness (FWHM) are quantitative, suggesting these metrics were likely derived through automated or semi-automated image analysis rather than direct expert labeling for "ground truth" in the sense of pathological diagnosis. While experts may have been involved in defining the methodologies for SNR and FWHM calculation or in qualitative assessment, this is not detailed.

4. Adjudication Method for the Test Set

Not applicable/Not described. The assessment relied on quantitative measures (SNR, FWHM) against predefined thresholds, not on expert consensus or adjudication of subjective interpretations.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No. The document describes a "Performance test" using retrospective clinical images focused on quantitative metrics (SNR, FWHM) to demonstrate improvements in image quality (noise reduction and sharpness increase). It does not describe an MRMC study comparing human reader performance with or without AI assistance.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was Done

Yes, the performance tests described are standalone evaluations of the algorithm's output. The criteria (SNR increase, FWHM decrease) directly measure the image processing capabilities of SwiftMR without human intervention in the loop for assessment. The device is described as "SwiftMR, is software used as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners." It operates automatically in the background.

7. The Type of Ground Truth Used

The "ground truth" for the performance evaluation appears to be quantitative, intrinsic image quality metrics:

Signal-to-Noise Ratio (SNR): A measure of image quality directly related to noise levels.
Full Width at Half Maximum (FWHM): A measure of image sharpness/resolution.

These metrics are calculated from the images themselves, both original and processed, to quantify the device's effect, rather than based on a diagnostic "ground truth" like pathology outcomes or expert consensus on diagnosis.

8. The Sample Size for the Training Set

The document states, "The only difference between the four deep learning algorithms is the input / label dataset used for training." However, the specific sample size (number of images or patients) for the training set is not provided.

9. How the Ground Truth for the Training Set was Established

The document states that the deep learning algorithms were trained using "input and label data generation method." The "labels" in this context for image enhancement typically refer to reference or target images. It specifies that "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." For deep learning-based image enhancement, the "ground truth" or "labels" for training often involve:

Synthetically generated data: Creating pairs of noisy/blurry images and their corresponding "clean/sharp" counterparts.
Paired clinical data: Acquiring both lower quality (e.g., fast scan) and higher quality (e.g., long scan, high-resolution) images of the same anatomy, with the higher quality images serving as the "ground truth" or target for the lower quality input.

The document does not provide explicit details on the specific method of "label data generation" for training, beyond mentioning an "image guided optimization process."

Summary

{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 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)

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."

{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.

K220416

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 '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.

The software provides three main functions, which are image processing, quality check and progress monitoring.

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
PhysicalCharacteristics	Software device thatoperates on off-the-shelf computerhardware	Same as predicate	No Difference
Computer	PC Compatible	Same as predicate	No Difference
DICOMStandardCompliance	The software processesDICOM-compliantimage data	Same as predicate	No Difference
Modalities	MRI	Same as predicate	No Difference
ImageEnhancementAlgorithmDescription	SwiftMR implements animage enhancementalgorithm usingconvolutionalneural network-basedfiltering. Originalimages are enhancedby running through acascade offilter banks, wherethresholding andscaling operations areapplied. Neuralnetwork-based filtersthat simultaneouslyperform noise reductionand sharpness increasefunctions are obtained.The parameters of thefilters were obtainedthrough an imageguided optimizationprocess.	SwiftMR implements animage enhancementalgorithm usingconvolutionalneural network-basedfiltering. Originalimages are enhancedby running through acascade offilter banks, wherethresholding andscaling operations areapplied. Neuralnetwork-based filtersthat perform noisereduction are obtained.The parameters of thefilters were obtainedthrough an imageguided optimizationprocess.Sharpening filter isadditionally applied tothe deep learningprocessed image.	The deep learning algorithm usingconvolutional neural network-basedfiltering performs denoising functionand newly added sharpness filterperforms sharpening function in thesubject device.
Deep learningmodels	1 General sequencemodel1 TOF sequence model	3 General sequencemodels1 TOF sequence model	The general sequence model wasdivided into three separate modelsfor each MRI manufacturer.The TOF sequence model remainsthe same as the predicate device.
Supported bodyparts	Brain	Brain, Spine, knee,ankle, shoulder, andhip	Supported body parts have beenexpanded to spine and MSK (knee,ankle, shoulder, and hip) in additionto brain.
Workflow	The predicate softwareoperates on DICOMfiles on the file system,enhances the images,and stores theenhanced images onthe file system. The	SwiftMR operates onDICOM files, enhancesthe images, and storesthe enhanced imageson PACS.	The subject device can receiveDICOM files either from PACS orfrom MR device.It is possible to store only theprocessed images.
Item	Predicate Device(SwiftMR (K210999))	Subject Device(SwiftMR)	Differences
	receipt of originalDICOM image files anddelivery of enhancedimages as DICOM filesdepends on othersoftware systems.Enhanced images co-exist with the originalimages.	Enhanced images canbe stored with theoriginal images or onlythe enhanced imagescan 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:

1. Verification testing: Unit test, Integration/system test conducted. These tests passed.
1. 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:

1. Manufacturer: SIEMENS. GE. PHILIPS
1. Field Strength: 1.5T / 3.0T
1. Anatomical region: Brain, Spine, Knee, Ankle, Shoulder, Hip
1. Protocol: T1, T2, T2 FS, GRE, FLAIR, PD, PD FS, TOF, SWI, MPRAGE
1. 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.

Regulation Number and Section

§ 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).