SwiftMR

K230854 · Airs Medical, Inc. · LLZ · Oct 27, 2023 · Radiology

Device Facts

• Record ID: K230854
• Device Name: SwiftMR
• Applicant: Airs Medical, Inc.
• Product Code: LLZ · Radiology
• Decision Date: Oct 27, 2023
• Decision: SESE
• Submission Type: Traditional
• Regulation: 21 CFR 892.2050
• Device Class: Class 2
• Attributes: AI/ML, Software as a Medical Device, Real-World Evidence, Pediatric

Real-World Evidence

Submission	Device	Sponsor	RWD Sources	RWE Use Summary	Key Tags
K230854 · Oct 27, 2023	SwiftMR	Airs Medical, Inc.	Retrospective clinical MR images	Retrospective clinical MR images were used to validate the performance of the device's noise reduction and sharpness increase functions across various manufacturers, field strengths, and anatomical regions.	Retrospective clinical images; Performance validation; Noise reduction; Image sharpness

Clinical Evidence

Study Design	Population	Comparator	Key Endpoints
Validation testing; Retrospective performance validation	Adults (22-93 yrs) and Pediatrics (0-21 yrs); Male (44.8%), Female (55.2%)	Not applicable for this study	Signal-to-noise ratio (SNR) increase; Full Width at Half Maximum (FWHM) decrease

AI Performance

Output	Algorithm	Acceptance	Observed	Dev DS	Dev Readers	Test DS	Test Readers
MR Image Noise Reduction	CNN with cascaded filter banks	SNR increase >= 40% for at least 90% of dataset (level 1, +1% per level)	Passed	—	—	Retrospective clinical images	—
MR Image Sharpness	CNN with cascaded filter banks	FWHM decrease >= 0.13% (model), 0.43% (level 1), 1.7% (level 2), 2.3% (level 3), 3.6% (level 4), 4.5% (level 5) for at least 90% of dataset	Passed	—	—	Retrospective clinical images	—

Indications for Use

SwiftMR is a stand-alone software solution intended to be used for acceptance, enhancement and transfer of all body parts MR images in DICOM format. It can be used for noise reduction and increasing image sharpness for MR images. SwiftMR is not intended for use on mobile devices.

Device Story

SwiftMR is a stand-alone software (SaMD) that enhances MRI images. It receives DICOM images from PACS or MRI scanners; processes them in the background using a deep learning model and sharpening filters; and outputs enhanced DICOM images back to PACS or the MRI device. Operated by radiology technologists in clinical settings, the device allows adjustment of denoising (levels 0-8) and sharpening (levels 0-5). It automates image quality enhancement, allowing for potential scan time reduction up to 50%. The system includes a user interface for monitoring processing status and system administration. By improving signal-to-noise ratio and image sharpness, the device assists clinicians in viewing diagnostic images, potentially improving diagnostic confidence.

Clinical Evidence

Bench testing only. Validation used retrospective clinical images across various manufacturers (Siemens, GE, Philips, etc.), field strengths (0.25T-3.0T), and anatomical regions. Noise reduction performance: SNR increased by ≥40% for ≥90% of the dataset at level 1. Sharpness performance: FWHM decreased by 0.13% (DL model) to 4.5% (filter level 5) for ≥90% of the dataset. Dataset included adults (22-93 yrs) and pediatrics (0-21 yrs).

Technological Characteristics

SaMD operating on off-the-shelf PC hardware. Uses a convolutional neural network-based filtering algorithm for noise reduction and sharpening. Processes DICOM-compliant images. Connectivity via PACS/MRI integration. No specific materials or energy sources as it is software-only.

Indications for Use

Indicated for patients of all ages (pediatric and adult) undergoing MRI scans of any body part (including neuro, musculoskeletal, cardiac, abdomen, pelvis, and breast) to enhance image quality via noise reduction and sharpness adjustment.

Regulatory Classification

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.

Special Controls

*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).

Predicate Devices

• SwiftMR (K220416)

Submission Summary (Full Text)

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October 27, 2023

Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the FDA logo is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.

AIRS Medical Inc. % Jihyeon Seo Head of RA 13-14F, Keungil Tower, 223, Teheran-ro, Gangnam-gu Seoul, 06142, Republic of Korea

## Re: K230854

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: September 28, 2023 Received: September 28, 2023

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 (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/cdrb/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.

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).

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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 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-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 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.

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-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 ([e-mail]) or phone [phone] or 301-796-7100).

Sincerely,

Samul for

Daniel Krainak, Ph.D. Assistant Director Magnetic Resonance and Nuclear Medicine Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

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# Indications for Use

510(k) Number (if known) K230854

Device Name SwiftMR

Indications for Use (Describe)

SwiftMR is a stand-alone software solution intended to be used for acceptance, enhancement and transfer of all body parts MR images in DICOM format. It can be used for noise reduction and increasing image sharpness for MR images. SwiftMR is not intended for use on mobile devices.

| Type of Use (Select one or both, as applicable) | |
|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <span style="font-family: Arial, sans-serif;"> <span style="font-size: 10pt;"> <span style="color: black;"> <input checked="true" type="checkbox"/> Prescription Use (Part 21 CFR 801 Subpart D) </span> </span> </span> | <span style="font-family: Arial, sans-serif;"> <span style="font-size: 10pt;"> <span style="color: black;"> <input type="checkbox"/> Over-The-Counter Use (21 CFR 801 Subpart C) </span> </span> </span> |

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Image /page/3/Picture/1 description: The image shows the logo for AIRS Medical. The logo consists of a large, blue letter "A" with a horizontal line extending from the right side of the "A". To the right of the "A" are the words "AIRS" and "MEDICAL" stacked on top of each other. The words "AIRS" and "MEDICAL" are in black.

This 510(k) Summary of safety and effectiveness information is being submitted in accordance with the requirements of 21 CFR 807.92.

#### I. SUBMITTER

Mrs. Jihyeon Seo Head of RA AIRS Medical Inc. 13-14F, Keungil Tower, 223, Teheran-ro, Gangnam-gu, Seoul, 06142, Republic of Korea Phone: [phone] FAX: [phone] Email: [e-mail]

Date Prepared: March 28, 2023

### 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: SwiftMR - K220416 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 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 noise reduction with the ability of adjusting the denoising level 0 to level 8, 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 from PACS or from MRI
- Image quality enhancement using Deep Learning model and sharpening . filter
- Transfer enhanced MR image as DICOM format to PACS or to MRI .

There is one deep learning model that can be applied to images from any MRIs with field strength of 0.25T, 0.6T, 1.5T, 3.0T. After integration with the facilities PACS. SwiftMR performs image processing in the background automatically. At

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Image /page/4/Picture/0 description: The image shows the logo for AIRS Medical. The logo consists of a large blue letter "A" with a curved line extending from the middle of the left side of the "A". To the right of the "A" are the words "AIRS" and "MEDICAL" stacked on top of each other in black font. The logo is simple and modern, and the use of blue and black gives it a professional look.

the same time. SwiftMR allows logged-in users to use its functions and view the processing status. 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 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 or to MR device.

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 all body parts MR images in DICOM format. It can be used for noise reduction and increasing image sharpness for 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.

| Item | Predicate Device<br>(SwiftMR (K220416)) | Subject Device<br>(SwiftMR) | Differences |
|--------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------|
| Physical<br>Characteristics | Software device that<br>operates on off-the-<br>shelf computer<br>hardware | Same as predicate | No Difference |
| Computer | PC Compatible | Same as predicate | No Difference |
| DICOM<br>Standard<br>Compliance | The software processes<br>DICOM-compliant<br>image data | Same as predicate | No Difference |
| Item | Predicate Device<br>(SwiftMR (K220416)) | Subject Device<br>(SwiftMR) | Differences |
| Modalities | MRI | Same as predicate | No Difference |
| Image<br>Enhancement<br>Algorithm<br>Description | SwiftMR implements an<br>image enhancement<br>algorithm using<br>convolutional<br>neural network-based<br>filtering. Original<br>images are enhanced<br>by running through a<br>cascade of<br>filter banks, where<br>thresholding and<br>scaling operations are<br>applied. Neural<br>network-based filters<br>that perform noise<br>reduction are obtained.<br>The parameters of the<br>filters were obtained<br>through an image<br>guided optimization<br>process.<br>Sharpening filter is<br>additionally applied to<br>the deep learning<br>processed image. | SwiftMR implements an<br>image enhancement<br>algorithm using<br>convolutional<br>neural network-based<br>filtering. Original<br>images are enhanced<br>by running through a<br>cascade of<br>filter banks, where<br>thresholding and<br>scaling operations are<br>applied. Neural<br>network-based filters<br>that perform noise<br>reduction and/or<br>sharpening are<br>obtained. The<br>parameters of the filters<br>were obtained through<br>an image guided<br>optimization process.<br>Sharpening filter is<br>additionally applied to<br>the deep learning<br>processed image. | The deep learning model also<br>performs sharpening function. |
| Deep learning<br>models | 3 General sequence<br>models<br>1 TOF sequence model | 1 model | The deep learning model is applied<br>for all types of images (including but<br>not limited to T1, T2, T2*, PD,<br>FLAIR, DWI, MRA). |
| Supported body<br>parts | Brain, Spine, knee,<br>ankle, shoulder, and hip | All body parts | It is expanded to all body parts. |
| Workflow | The software operates<br>on DICOM files,<br>enhances the images,<br>and stores the<br>enhanced images on<br>PACS. Enhanced<br>images co-exist with the<br>original images. | The software operates<br>on DICOM files,<br>enhances the images,<br>and stores the<br>enhanced images on<br>PACS or on MR device.<br>Enhanced images co-<br>exist with the original<br>images. | The enhanced images can also be<br>sent to MR device not only to<br>PACS. |

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Image /page/5/Picture/0 description: The image contains the logo for AIRS MEDICAL. The logo consists of a large, stylized letter "A" in blue, with a curved line extending from the right side of the "A". To the right of the "A" are the words "AIRS" and "MEDICAL" stacked vertically, both in black. The font used for "AIRS MEDICAL" is bold and sans-serif.

## 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.

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Image /page/6/Picture/0 description: The image contains the logo for AIRS Medical. The logo consists of a large, stylized letter "A" in blue, with a curved line extending from the left side of the "A". To the right of the "A" are the words "AIRS" and "MEDICAL" stacked vertically, both in black, sans-serif font. The logo is simple and modern in design.

The following tests were conducted for SwiftMR:

- 1) Verification testing: Unit test. Integration/system test conducted. These tests passed.
- 2) 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 signal-to-noise ratio (SNR) of the SwiftMR-processed image series is increased by 40% or more for at least 90% of the dataset for level 1 with an incremental 1% increase per each level. 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 bv 0.13% (deep learning model). 0.43% (filter level 1). 1.7% (filter level 2). 2.3% (filter level 3), 3.6% (filter level 4), 4.5% (filter 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, CANON, ESAOTE, FONAR, FUJIFILM

2. Field Strength: 0.25T, 0.6T, 1.5T, 3.0T

3. Anatomical region: Body (breast, abdomen, and pelvis), Cardiac, Neuro (head, neck, and spine), Musculoskeletal (shoulder, wrist, hip, knee, and ankle) 4. Protocol: T1, T2, T2*, FLAIR, PD, DWI, MRA

- 5. Demographics
- age: Adults (22~93 yrs, 88.4%), Pediatrics (0~21 yrs, 11.6%)
- gender: Male (44.8%), Female (55.2%)
- 6. Time reduction range for reduced scan time images: up to 50%

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.

### 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.