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.

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.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study details for SwiftMR, based on the provided FDA 510(k) summary:

1. Acceptance Criteria and Reported Device Performance

Acceptance Criteria	Reported Device Performance
Noise Reduction: Average Signal-to-Noise Ratio (SNR) of SwiftMR-processed image series is increased by 40% or more compared to the original image series.	Test passed. The average SNR of SwiftMR-processed images was increased by 40% or more.
Sharpness Increase: FWHM (Full Width at Half Maximum) 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.	Test passed. The FWHM of selected ROIs was decreased by 0.13% or more for at least 90% of the test datasets.

2. Sample Size and Data Provenance for the Test Set

Sample Size for Test Set: Not explicitly stated. The document mentions "retrospective clinical images" and "at least 90% of the test datasets," but the absolute number of cases or images is not provided.
Data Provenance: Retrospective clinical images. The country of origin is not specified, but the applicant (AIRS Medical Inc.) is based in South Korea, suggesting potential involvement of data from that region.

3. Number of Experts and Qualifications for Ground Truth

Number of Experts: Not specified.
Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

Adjudication Method: Not specified. The document only states that "predetermined acceptance criteria were met," implying internal validation, but does not detail how potential disagreements in ground truth were resolved if multiple experts were used.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

MRMC Study Conducted: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not reported. The performance data focuses on technical metrics (SNR and FWHM) of the algorithm's output, not on human reader improvement with or without AI assistance.
Effect Size of Human Reader Improvement: Not applicable, as no MRMC study was performed.

6. Standalone Algorithm Performance Study

Standalone Performance Study: Yes, a standalone (algorithm only without human-in-the-loop performance) study was conducted. The performance data presented (SNR increase and FWHM decrease) directly quantifies the technical output of the SwiftMR algorithm.

7. Type of Ground Truth Used

Type of Ground Truth: The ground truth for the performance test appears to be based on quantitative technical metrics of image quality (SNR and FWHM) of the original images, which are then compared to the processed images. It is implicitly assumed that higher SNR and lower FWHM indicate improved image quality. It's not based on expert consensus for clinical diagnosis, pathology, or outcomes data in this summary.

8. Sample Size for the Training Set

Sample Size for Training Set: Not explicitly stated. The document mentions "input / label dataset used for training" for two deep learning algorithms (general pulse sequences and TOF pulse sequences), but the size of these datasets is not provided.

9. How Ground Truth for the Training Set Was Established

Ground Truth Establishment for Training Set: The document states that 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." However, it does not explicitly describe how the "label dataset" (ground truth) for training was established. It can be inferred that the labels would correspond to an idealized "enhanced" version of the input, likely generated through a process that defines optimal noise reduction and sharpness, but the specifics of this generation are not detailed.

Summary

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October 14, 2021

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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

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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

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Indications for Use

510(k) Number (if known) K210999

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)
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 '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

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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

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 users and system admin to modify software settings as required by the institution or respective user.

V. INDICATIONS FOR USE

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
PhysicalCharacteristics	Software device thatoperates on off-the-shelf computerhardware	Same as predicate	No Difference
Computer	Linux Compatible	PC Compatible	Differences are basically in thecomputer operating system but alsomay have some differences in theprocessor speeds, amount of RAMmemory, monitors, and hard drivespace requirements. However, thesubject device and the predicatedevice are substantially equivalent

			in the areas of technicalcharacteristics, general function,application, and intended use andthe computer platform differencesdo not raise any new potentialsafety risks. Therefore, it is ourdetermination that there is "Noimpact on safety or efficacy" andthere are no new potential orincreased safety risks.
DICOMStandard	The software processes	Same as predicate	No Difference
Compliance	compliant image data
Modalities	MRI	Same as predicate	No Difference
ImageEnhancementAlgorithmDescription	The predicate softwareimplements an imageenhancement algorithmusing convolutionalneural network-basedfiltering. Originalimages are enhancedby running through acascade offilter banks, wherethresholding andscaling operations areapplied. Separateneural network-basedfilters are obtained fornoise reduction andsharpness increase.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 simultaneouslyperform noise reductionand sharpnessincrease functions areobtained. Theparameters of the filterswere obtained throughan image guidedoptimization process.	The only difference is in how theneural network-based filter exists.As for the predicate device, thereare separate filters for noisereduction and sharpness increase.On the other hand, there are neuralnetwork-based filters thatsimultaneously perform bothfunctions for the subject device.However, the same functions, whichare noise reduction and sharpnessincrease, are applied by the filters.Therefore, the difference does notraise new questions of safety oreffectiveness.
Workflow	The predicate softwareoperates on DICOMfiles on the file system,enhances the images,and stores theenhanced images onthe file system. Thereceipt of originalDICOM image files anddelivery of enhancedimages as DICOM filesdepends on othersoftware systems.Enhanced images co-exist with the originalimages.	Same as predicate	No Difference

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MEDICA

510(k) Summary

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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:

1. Verification testing: Unit test, Intearation/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.
- 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.

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).