This device is intended for automatic labelling, visualization and volumetric quantification of segmentable brain structures and lesions from a set of MR images. Volumetric data may be compared to reference percentile data.

Device Description

SwiftSight-Brain is a fully automated MR image analysis software that provides automatic labeling, visualization, and volumetric quantification of brain structures from a set of MR images and returns segmented images and morphometric reports. The resulting output is provided in morphometric reports that can be displayed on Picture Archive and Communications Systems (PACS). The high throughput capability makes the software suitable for use in routine patient care as a support tool for clinicians in assessment of structural MRIs.

SwiftSight-Brain provides morphometric measurements based on 3D T1 weighted MRI series. The output of the software includes volumes that have been annotated with color overlays, with each color representing a particular segmented region, and morphometric reports that provide comparison of measured volumes to age and gender-matched reference percentile data. In addition, the adjunctive use of the T2 weighted FLAIR MR series allows for improved identification of some brain abnormalities such as lesions, which are often associated with T2 weighted FLAIR hyperintensities.

SwiftSight-Brain processing architecture includes a proprietary automated internal pipeline that performs segmentation, volume calculation and report generation.

The results are displayed in a dedicated graphical user interface, allowing the user to:

Browse the segmentations and the measures
Compare the results of segmented brain structures to a reference healthy population
Read, download and print a PDF report

Additionally, automated safety measures include automated quality control functions, such as scan protocol verification, which validate that the imaging protocols adhere to system requirements.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study details for SwiftSight-Brain, extracted from the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance for SwiftSight-Brain

1. Table of Acceptance Criteria and Reported Device Performance

For 3D T1 Weighted Images (Brain Structures):

Acceptance Criterion (Metric)	Target Acceptance Value	Reported Device Performance
Segmentation Accuracy (Dice's Coefficient)
Major Subcortical Brain Structures	Undefined, but implied high	Above 80%
Major Cortical Structures	Undefined, but implied high	Above 75%
Brain Structural Reproducibility (Mean Percentage Absolute Volume Differences)
All Major Subcortical and Cortical Structures	Not explicitly stated, but implied low	5% or less

For T2 Weighted FLAIR Images (Brain Lesions):

Acceptance Criterion (Metric)	Target Acceptance Value	Reported Device Performance
Lesion Segmentation Accuracy (Dice's Coefficient)	Not explicitly stated, but implied high	Exceeds 0.80
Brain Lesion Segmentation Reproducibility (Mean Absolute Lesion Volume Difference)	Not explicitly stated, but implied low	Less than 0.25cc

2. Sample Sizes and Data Provenance

For 3D T1 Weighted Images:

Test Set Sample Size: 72 cases for accuracy, 72 cases for reproducibility.
Data Provenance: Subjects were collected from multiple countries, including the United States, the United Kingdom, China, and Germany. The data primarily sourced from U.S. hospitals. Retrospective.
Specifics: Test dataset included MR images from Philips (54 subjects), Siemens Healthineers (53 subjects), and GE (37 subjects) scanners, using both 1.5T and 3.0T field strengths. Ages ranged from 20s to 90s. Included healthy subjects, mild cognitive impairment patients, Alzheimer's disease patients, and small vessel disease patients.

For T2 Weighted FLAIR Images:

Test Set Sample Size: 160 cases for accuracy, 85 cases for reproducibility.
Data Provenance: Subjects were collected from multiple countries, including the United States, Brazil, and Republic of Korea. The data primarily sourced from U.S. hospitals. Retrospective.
Specifics: Test dataset included MR images from Philips (92 subjects), Siemens Healthineers (65 subjects), and GE (88 subjects) scanners, using both 1.5T and 3.0T field strengths. Ages ranged from 20s to 90s. Included healthy subjects, mild cognitive impairment patients, Alzheimer's disease patients, and small vessel disease patients.

3. Number of Experts and Qualifications for Ground Truth Establishment (Test Set)

Number of Experts: Two neuroradiologists and one neurologist.
Qualifications: U.S. based. No specific years of experience or board certifications are mentioned in the provided text, only their specialties.

4. Adjudication Method for the Test Set

The adjudication method for establishing ground truth from the multiple experts is not explicitly stated in the provided text. It mentions that ground truth was "established by the U.S. based two neuroradiologists and one neurologist," but doesn't detail how their individual interpretations were combined (e.g., majority vote, consensus meeting, primary reader with adjudication by others).

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

No MRMC comparative effectiveness study was mentioned in the provided text. The study focused on standalone algorithm performance against expert ground truth. Therefore, no effect size for human readers improving with AI vs. without AI assistance is available from this document.

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone (algorithm only) performance study was done. The document describes the evaluation of SwiftSight-Brain's segmentation accuracy and reproducibility by comparing its output directly against expert manual segmentations (ground truth). It does not describe any human-in-the-loop performance evaluation for the clearance.

7. Type of Ground Truth Used

The ground truth for both 3D T1 weighted and T2 weighted FLAIR images was established by expert manual segmentations.

8. Sample Size for the Training Set

The sample size for the training set is not explicitly stated in the provided text. The document mentions that "The subjects upon whom the device was trained and tested include healthy subjects, mild cognitive impairment patients, Alzheimer's disease patients, and small vessle disease patients from young adults to elderlies," but only provides specific numbers for the test sets.

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

The method for establishing ground truth for the training set is not explicitly stated. The document only details how the ground truth for the test set was established (expert manual segmentations by two neuroradiologists and one neurologist).

Summary

FDA 510(k) Clearance Letter - SwiftSight-Brain

Page 1

U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov

Doc ID # 04017.08.00

September 23, 2025

AIRS Medical Inc.
HaeRi Lee
RA Manager
13-14F, Keungil Tower, 223, Teheran-ro, Gangnam-gu
Seoul, 06142
Republic of Korea

Re: K251483
Trade/Device Name: SwiftSight-Brain
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical image management and processing system
Regulatory Class: Class II
Product Code: QIH, LLZ
Dated: August 25, 2025
Received: August 25, 2025

Dear HaeRi Lee:

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/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.

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K251483 - Lee HaeRi
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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).

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 (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-reporting-combination-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.

All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.

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-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.

For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/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-devices/device-advice-comprehensive-regulatory-

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K251483 - Lee HaeRi
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assistance/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,

Daniel M. Krainak, Ph.D.
Assistant Director
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

Enclosure

Page 4

FORM FDA 3881 (8/23)
Page 1 of 1
PSC Publishing Services (301) 443-6740 EF

DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration

Indications for Use

Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.

510(k) Number (if known): K251483

Device Name: SwiftSight-Brain

Indications for Use (Describe):
This device is intended for automatic labelling, visualization and volumetric quantification of segmentable brain structures and lesions from a set of MR images. Volumetric data may be compared to reference percentile data.

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."

Page 5

AIRS Medical Inc.
13-14F, Keungil Tower, 223, Teheran-ro, Gangnam-gu, Seoul, 06142, Republic of Korea
www.airsmed.com

K251483

Date of the 510(k) summary preparation: September 18th, 2025

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.

1. Submitter

Name: AIRS Medical Inc.
Address: 13-14F, Keungil Tower, 223, Teheran-ro, Gangnam-gu, Seoul, 06142, Republic of Korea
Phone: +82-10-4820-5200
FAX: +82-2-6280-3185

2. Contact person

Name: HaeRi Lee
Position: RA Manager
Email: lee.haeri@airsmed.com

3. Device information

Device trade name: SwiftSight-Brain
Model name: SS25-BR-CL
Primary product specifications
- Regulation No. 21 CFR 892.2050
- Device class: Class II
- Product code: QIH
- Classification name: Automated radiological image processing software
Associated product specifications
- Regulation No. 21 CFR 892.2050
- Device class: Class II
- Product code: LLZ
- Classification name: System, image processing, radiological

4. Predicate device

Device trade name: Neurophet AQUA (V3.1)
510(k) number: K242215
Product specification
- Regulation No. 21 CFR 892.2050
- Classification: Class II
- Product code: QIH, LLZ

5. Device description

SwiftSight-Brain is a fully automated MR image analysis software that provides automatic labeling, visualization, and volumetric quantification of brain structures from a set of MR images and

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AIRS Medical Inc.
13-14F, Keungil Tower, 223, Teheran-ro, Gangnam-gu, Seoul, 06142, Republic of Korea
www.airsmed.com

returns segmented images and morphometric reports. The resulting output is provided in morphometric reports that can be displayed on Picture Archive and Communications Systems (PACS). The high throughput capability makes the software suitable for use in routine patient care as a support tool for clinicians in assessment of structural MRIs.

SwiftSight-Brain processing architecture includes a proprietary automated internal pipeline that performs segmentation, volume calculation and report generation.

The results are displayed in a dedicated graphical user interface, allowing the user to:

Browse the segmentations and the measures
Compare the results of segmented brain structures to a reference healthy population
Read, download and print a PDF report

Additionally, automated safety measures include automated quality control functions, such as scan protocol verification, which validate that the imaging protocols adhere to system requirements.

6. Indications for Use

7. Comparison of technological characteristics with the predicate device

The table below compares SwiftSight-Brain (subject device) with Neurophet AQUA V3.1 (predicate device, K242215).

Comparison table

	Subject device	Predicate device	Substantial Equivalence
Device name	SwiftSight-Brain	Neurophet AQUA V3.1	-
510(k)	-	K242215	-
Manufacturer	AIRS Medical Inc.	NEUROPHET, Inc	-
Product code	QIH, LLZ	QIH, LLZ	Same
Indications for use	This device is intended for automatic labelling, visualization and volumetric quantification of segmentable brain structures and lesions from a set of MR images. Volumetric data may be	Neurophet AQUA is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures and lesions from a set of MR images. Volumetric data may be compared to reference	Same

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AIRS Medical Inc.
13-14F, Keungil Tower, 223, Teheran-ro, Gangnam-gu, Seoul, 06142, Republic of Korea
www.airsmed.com

	Subject device	Predicate device	Substantial Equivalence
	compared to reference percentile data.	percentile data.
Target anatomical sites	Brain	Brain	Same
Design and incorporated technology	• Automated measurement of brain tissue volumes, structures, and lesions• Automatic segmentation and quantification of brain structures using deep learning	• Automated measurement of brain tissue volumes, structures, and lesions• Automatic segmentation and quantification of brain structures using deep learning	Same
Physical characteristics	• Software package• Operates on off-the shelf hardware (multiple vendors)	• Software package• Operates on off-the shelf hardware (multiple vendors)	Same
Operation system	Windows	Windows	Same
Processing architecture	Automated internal pipeline that performs:• segmentation• volume calculation• report generation	Automated internal pipeline that performs:• segmentation• volume calculation• report generation	Same
Data source	• MRI scanner: 3D T1 and FLAIR MRI scans acquired with specified protocols• Supports DICOM format as input	• MRI scanner: 3D T1 and FLAIR MRI scans acquired with specified protocols• Supports DICOM format as input	Same
Output	• Provides volumetric measurements of brain structures and lesions• Includes segmented color overlays and morphometric reports• Automatically compares results to reference percentile data and to prior scans when available• Supports DICOM format as output of results that can be displayed on DICOM workstations and Picture Archive and Communications Systems	• Provides volumetric measurements of brain structures and lesions• Includes segmented color overlays and morphometric reports• Automatically compares results to reference percentile data and to prior scans when available• Supports DICOM format as output of results that can be displayed on DICOM workstations and Picture Archive and Communications Systems	Same
Safety	• Automated quality control functions	• Automated quality control functions	Substantially Equivalent

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AIRS Medical Inc.
13-14F, Keungil Tower, 223, Teheran-ro, Gangnam-gu, Seoul, 06142, Republic of Korea
www.airsmed.com

	Subject device	Predicate device	Substantial Equivalence
	- Tissue contrast check- Scan protocol verification• Results must be reviewed by a medical professional	- Image artifact check- Scan protocol verification• Results must be reviewed by a trained physician

SwiftSight-Brain and the predicate device are identical in most aspects, including intended use, target anatomical site, physical characteristics, processing architecture, data source, and output. While there are differences in terminology related to the automated quality control function and the user who reviews the results, these differences do not affect safety or effectiveness, do not introduce new or increased safety risks, and demonstrate equivalent performance to existing legally marketed devices.

8. Summary of non-clinical test

The software verification and validation tests were conducted to establish the performance, functionality and reliability characteristics of the subject devices. The device passed all the tests based on pre-determined Pass/Fail criteria.

To demonstrate the 3D T1 weighted and T2 weighted FLAIR analysis performance of the SwiftSight-Brain, the data primarily sourced from U.S. hospitals were utilized. The data presents a diverse mix of clinical, demographic, and technical variables, providing a foundation for both reliable and reproducible testing, as well as comprehensive accuracy assessment. The ground truth was established by the U.S. based two neuroradiologists and one neurologist. The multi-site data collection enhances statistical independence, while the inclusion of varied demographic groups, clinical conditions, and MR field strength addresses potential confounders.

The subjects upon whom the device was trained and tested include healthy subjects, mild cognitive impairment patients, Alzheimer's disease patients, and small vessle disease patients from young adults to elderlies. The multicenter study was adapted to collect scans from various vendors including Philips, Siemens Healthineers, and GE Healthcare and MR scans using general clinical protocols were collected.

For the 3D T1 weighted images, a total of 72 3D T1 weighted scan cases were used for accuracy, and 72 cases were used for reproducibility. The test dataset included MR images from Philips (54 subjects), Siemens Healthineers (53 subjects), and GE (37 subjects) scanners, using both 1.5T and 3.0T field strengths. Subjects were collected from multiple countries, including the United States, the United Kingdom, China, and Germany, with ages ranging from young adults to the elderly (20s to 90s).

As a result, the segmentation accuracy compared to expert manual segmentations of 3D T1 MRI scans was evaluated using Dice's coefficient metric. For major subcortical brain structures Dice's coefficients were above 80% and for major cortical were above 75%.

Brain structural reproducibility of repeated 3D T1 MRI scans for same subjects was evaluated by using the percentage absolute volume differences. The mean percentage absolute volume differences for all major subcortical and cortical structures were 5% or less.

For the T2 weighted FLAIR images, the accuracy test dataset comprised 160 T2 weighted FLAIR scan

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AIRS Medical Inc.
13-14F, Keungil Tower, 223, Teheran-ro, Gangnam-gu, Seoul, 06142, Republic of Korea
www.airsmed.com

cases, and the reproducibility test dataset comprised 85 cases. The sample size supports robust performance validation. The test dataset included MR images from Philips (92 subjects), Siemens Healthineers (65 subjects), and GE (88 subjects) scanners, using both 1.5T and 3.0T field strengths. Subjects were collected from multiple countries, including the United States, Brazil and Republic of Korea with ages ranging from young adults to the elderly (20s to 90s).

SwiftSight-Brain performance was then evaluated by comparing segmentation accuracy with expert manual segmentations and by measuring segmentation reproducibility between same subject scans. The system yields reproducible results that are well correlated with expert manual segmentation.

SwiftSight-Brain's lesion segmentation accuracy compared to expert manual segmentations of T2 weighted FLAIR scan was evaluated using Dice's coefficient metric, which exceeds 0.80. The brain lesion segmentation reproducibility was evaluated using repeated T2 weighted FLAIR scan pairs of subjects with brain lesions. The mean absolute lesion volume difference was less than 0.25cc.

9. Conclusion

The subject device is substantially equivalent in the areas of technical characteristics, general function, application, and indications for use. The new device does not introduce fundamentally new scientific technology, and the device has been validated through the system level test. Therefore, we conclude that the subject device described in this submission is substantially equivalent to the predicate device (K242215, Neurophet AQUA V3.1).

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).