AIRAscore is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR images. This software is intended to automate the current manual process of identifying, labeling and quantifying the volume of segmentable brain structures identified on MR images.

Device Description

AIRAscore is a software that offers automatic, fast and reliable segmentation of brain volumes into gray matter, white matter, cerebrospinal fluid and, if present, white matter lesions with an additional classification of tissue anatomy. The AIRAscore software comprises two functions, referred to as "AIRAscore structure" and "AIRAscore MS". The report created using the AIRAscore structure function contains the volume evaluation for each seqmented anatomical area with the raw value, the relative value with respect to the total intracranial volume, and the percentile for the patient compared to a reference set. It furthermore provides a quick overview of potential segment size differences based on the reference set comparison. If the AIRAscore MS report is requested, it is provided with additional information about the number and the volume of white matter lesions and their categorization (i.e., juxtacortical, periventricular or infratentorial). For analysis with AIRAscore, incoming MRI data need to comply with the DICOM standard and are checked to fulfill the technical requirements. After successful verification, segmentation is performed using specialized neuronal networks that remain static during the lifetime of a software version. The results are then corrected for head size and compared to an age- and sex adjusted reference collective including a statistical classification. A report is generated and transmitted via a DICOM storage SCU (sender) to a defined DICOM storage SCP (usually the picture archive of the referring physician) using the DICOM format.

AI/ML Overview

The provided FDA 510(k) summary for AIRAscore does not contain a detailed description of the acceptance criteria and the study that rigorously proves the device meets those criteria, specifically regarding its clinical performance or accuracy for volumetric quantification. The document focuses on general software verification and validation, comparison to a predicate device, and compliance with standards, but it lacks specific performance testing results (e.g., accuracy, precision, sensitivity, specificity, Dice scores) against a defined ground truth.

The "Performance Testing" section states: "The validation confirmed that AIRAscore performs well across target patient population and scanner manufacturers." However, it does not provide what performance metrics were used, what the acceptance criteria for "performing well" were, or what the actual results were.

Therefore, I cannot populate all the requested information. Below is what can be inferred or stated as missing based solely on the provided text.

Acceptance Criteria and Study to Prove Device Meets Acceptance Criteria

The provided 510(k) summary for AIRAscore does not explicitly define specific quantitative acceptance criteria for its performance (e.g., accuracy of volumetric quantification) or present a detailed study proving these criteria were met. The document focuses on general software verification and validation, comparison to a predicate device, and compliance with general software/medical device standards.

The "Performance Testing" section broadly states that "The validation confirmed that AIRAscore performs well across target patient population and scanner manufacturers." However, it does not specify the metrics, thresholds for "performing well," or the results of this validation.

1. Table of Acceptance Criteria and Reported Device Performance

Based on the provided document, specific quantitative acceptance criteria and corresponding reported device performance metrics (e.g., accuracy, precision, correlation coefficients, Dice scores for segmentation) are NOT detailed.

The document states:

Performance Measurement Testing (for New Device - AIRAscore):
- Accuracy: "Brain segmentable structure volumes / volume changes compared to manually labeled ground truth"
- Reproducibility: "Brain segmentable structure volumes / volume changes compared on test-retest images"

However, the specific acceptance thresholds for these measurements (e.g., "accuracy > X%", "Dice coefficient > Y") and the actual numerical results that demonstrate the device met these criteria are not included in this summary.

2. Sample Size and Data Provenance for Test Set

Sample Size for Test Set: Not specified in the provided document.
Data Provenance: The document states "The validation confirmed that AIRAscore performs well across target patient population and scanner manufacturers." This broadly implies use of diverse data, but specific details on country of origin or whether the data was retrospective or prospective are not provided.

3. Number of Experts and Qualifications for Ground Truth Establishment

Number of Experts: Not specified in the provided document.
Qualifications of Experts: The type of study described (comparison to "manually labeled ground truth") implies expert involvement, but their qualifications (e.g., specific medical specialties, years of experience, board certification) are not detailed.

4. Adjudication Method for the Test Set

Adjudication Method: Not specified in the provided document.

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

MRMC Study: The document describes "performance measurement testing" including "Accuracy" and "Reproducibility" comparing to "manually labeled ground truth." However, it does not mention a multi-reader multi-case (MRMC) comparative effectiveness study evaluating how much human readers improve with AI vs. without AI assistance. The device's intended use is to "automate the current manual process," suggesting a focus on automation rather than AI-assisted human reading improvement.

6. Standalone (Algorithm Only) Performance

Standalone Performance: The description of "Performance Measurement Testing" (Accuracy relative to manually labeled ground truth, Reproducibility) suggests that the device's standalone performance (algorithm only without human-in-the-loop) was assessed. However, the specific metrics and results of this standalone assessment are not provided.

7. Type of Ground Truth Used

Type of Ground Truth: "Manually labeled ground truth" is explicitly mentioned for accuracy measurement. The specific methodology for this manual labeling (e.g., expert consensus, pathology, long-term outcomes data) is not further detailed. Given the context of "segmentable brain structures" and "volumetric quantification," it is highly probable that this refers to expert-driven manual segmentation or volumetric measurements on the MR images.

8. Sample Size for the Training Set

Sample Size for Training Set: Not specified in the provided document. The document mentions "specialized neuronal networks" and "machine learning (supervised voxel classification by a Convolutional Neuronal Network)" for segmentation, which implies a training set was used, but its size is not given.

9. How Ground Truth for Training Set Was Established

Ground Truth for Training Set: The document mentions "supervised voxel classification by a Convolutional Neuronal Network." For supervised learning, the ground truth for the training set would typically be established through expert annotations (e.g., manual segmentation/labeling of brain structures on MR images). However, the specific methodology and expert involvement for establishing the training set ground truth are not described in the provided text.

Summary

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AIRAmed GmbH % Katharina Keutgen Official Correspondent Johner Institut GmbH Niddastr. 91 Frankfurt, 60329 GERMANY

August 25th, 2023

Re: K223180

Trade/Device Name: AIRAscore Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: LLZ Dated: July 26, 2023 Received: July 26, 2023

Dear Katharina Keutgen:

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

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801); medical device reporting of medical device-related adverse events) (21 CFR 803) for 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 medical devices and radiation-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).

Sincerelv.

Ningzhi Li

For

Daniel M. 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

Enclosure

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Indications for Use

510(k) Number (if known)

K223180

Device Name AIRAscore

Indications for Use (Describe)

Type of Use (Select one or both, as applicable)

Prescription Use (Part 21 CFR 801 Subpart D)

Over-The-Counter Use (21 CFR 801 Subpart C)

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Image /page/3/Picture/0 description: The image shows the logo for AIRAmed, a company focused on artificial intelligence in radiology. The logo consists of the company name in two lines, with "AIRA" in red and "med" in gray. Below the company name, the tagline "artificial intelligence in radiology" is written in a smaller, gray font.

510(k) Summary

for

AIRAscore

This summary of 510(k) safety and effectiveness information is being submitted in accordance with the requirement of 21 CFR 807.92:

Sponsor

Sponsor:	AIRAmed GmbHKonrad-Adenauer-Str. 1372072 TübingenGermany
Contact Person:	Dr. Maximilian StalterEmail: maximilian.stalter@airamed.dephone: +49 7071 5393366
Date Prepared:	September 21, 2022
510(k) Number:	K223180

Device Name and Classification

Proprietary Name:	AIRAscore
Device:	System, Image Processing, Radiological
Classification Name:	Medical image management and processing system(21 CFR 892.2050, Product Code LLZ)

Predicate Device

Predicate Device: icobrain, K192130

Intended Use

Device Description and Function

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The AIRAscore software comprises two functions, referred to as "AIRAscore structure" and "AIRAscore MS". The report created using the AIRAscore structure function contains the volume evaluation for each seqmented anatomical area with the raw value, the relative value with respect to the total intracranial volume, and the percentile for the patient compared to a reference set. It furthermore provides a quick overview of potential segment size differences based on the reference set comparison.

If the AIRAscore MS report is requested, it is provided with additional information about the number and the volume of white matter lesions and their categorization (i.e., juxtacortical, periventricular or infratentorial).

For analysis with AIRAscore, incoming MRI data need to comply with the DICOM standard and are checked to fulfill the technical requirements. After successful verification, segmentation is performed using specialized neuronal networks that remain static during the lifetime of a software version. The results are then corrected for head size and compared to an age- and sex adjusted reference collective including a statistical classification. A report is generated and transmitted via a DICOM storage SCU (sender) to a defined DICOM storage SCP (usually the picture archive of the referring physician) using the DICOM format.

Predicate Device Comparison

Characteristic	New Device	Predicate Device
510(k) Number	K223180	K192130
Device Name,Model	AIRAscore	icobrain
Manufacturer	AIRAmed GmbH	icometrix NV
RegulationNumber	892.2050	892.2050
Product Code	LLZ	LLZ
Intended Use /Indications forUse	AIRAscore is intended for automaticlabeling, visualization and volumetricquantification of segmentable brainstructures from a set of MR images. Thissoftware is intended to automate thecurrent manual process of identifying,labeling and quantifying the volume ofsegmentable brain structures identifiedon MR images.	icobrain is intended for automaticlabeling, visualization and volumetricquantification of segmentable brainstructures from a set of MR or NCCTimages. This software is intended toautomate the current manual processof identifying, labeling and quantifyingthe volume of segmentable brainstructures identified on MR or NCCTimages.icobrain consists of two distinct imageprocessing pipelines: icobrain crossand icobrain long.icobrain cross is intended to providevolumes from images acquired at asingle timepoint. icobrain long isintended to provide changes involumes between two images thatwere acquired on the same scanner,
TechnicalCharacteristics	AIRAscore is a software as medicaldevice (SaMD) that runs onAIRAmed internal servers (Softwareas a Service - SaaS). For sending and receiving DICOMdata dedicated interfaces aresupplied as accessory. Operates on off-the-shelf hardware(multiple vendors) DICOM compatible Segmentation by machine learning(supervised voxel classification by aConvolutional Neuronal Network) Input: T1-weighted and (optional) fluid-attenuated inversion recovery(FLAIR) MR images from a singletime point Output: Multiple electronic report withvolumetric information of brainstructures (Encapsulated PDFDICOM) Annotated DICOM images for visualinspection by an expert (SecondaryCapture DICOM)	with the same image acquisitionprotocol and with the same contrast attwo different timepoints. The results oficobrain cross cannot be comparedwith the results of icobrain long. Software package Operates on off-the-shelf hardware(multiple vendors) DICOM compatible Segmentation by classical machinelearning and deep learning(supervised voxel classification by aConvolutional Neuronal Network) Input: T1-weighted and fluid-attenuatedinversion recovery (FLAIR) MRimages from a single or multipletime points Non-contrast CT from a single timepoint Output: Multiple electronic reports withvolumetric information of brainstructures and midline shift Annotated DICOM images
PerformanceMeasurementTesting	Accuracy Brain segmentable structure volumes/ volume changes compared tomanually labeled ground truth Reproducibility Brain segmentable structure volumes/ volume changes compared on test-retest images	Accuracy Brain segmentable structurevolumes / volume changescompared to simulated and/ormanually labeled ground truth Reproducibility Brain segmentable structurevolumes / volume changescompared on test-retest images
Environment ofUse	Primary users of the system arephysicians with finished course ofstudies, medical license and expertknowledge in neuroanatomy and MR-imaging of the head. The reports andcontrol images are looked at andevaluated in a professional healthcaresetting (diagnostic workstation ordoctor's office).	icobrain is used by trainedprofessionals in hospitals, imagingcenters or in image processing labs.
Testing	Product Risk assessment Software verification tests Software validation tests	Product Risk assessment Software verification tests Software validation tests
Compliance with Standards	ISO 14971:2019 Medical devices - Application of risk management to medical devices IEC 62304 Edition 1.1 2015-06 Medical device software - Software life-cycle processes IEC 62366-1 Edition 1.0 2015-02 Medical devices - Application of usability engineering to medical devices CFR 21 part 820 Quality System Regulation for Medical Devices ISO 13485:2016 Medical devices - Quality management systems NEMA PS 3.1 - 3.20 (2016) Digital imaging and communication in medicine (DICOM) Set	ISO 14971:2007 Medical devices - Application of risk management to medical devices IEC 62304:2006 Medical device software - Software life-cycle processes IEC 62366:2014 Medical devices - Application of usability engineering to medical devices CFR 21 part 820 Quality System Regulation for Medical Devices ISO 13485:2016 Medical devices - Quality management systems ISO 12052:2006 Digital imaging and communication in medicine (DICOM)

Table 1: Predicate Device Comparison

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Both the subject- and the predicate device have the same intended use and comparable technical features. Both devices use the same machine learning procedures (supervised voxel classification by Convolutional Neural Networks) to perform segmentation tasks. Since the technique is the same in both devices and is known to perform well on segmentation tasks, no different questions regarding safety and effectiveness are raised and both devices are deemed to be substantially equivalent.

Performance Testing

The software verification of AIRAscore included continuous automatic unit testing, integration testing and end-to-end testing during the product realization phase according to IEC 62304. During the verification phase, the components were tested separately to verify the conformance of the development result with the defined software requirements. The verification included the check of the implementation of risk mitiqation measures. The efficiency of these measures was either tested during the verification or during the course of the validation.

Afterwards, integration testing was performed to verify that the components work together as specified in the software.

The validation confirmed that AIRAscore performs well across tarqet patient population and scanner manufacturers.

Software verification and validation demonstrated that AIRAscore meets the software requirements.

Performance Standards

AIRAscore complies with the applicable requirements of the following international and national standards:

ISO 14971 Third Edition 2019-12 Medical Devices Application Of Risk Management To . Medical Devices
IEC 62304 Edition 1.1 2015-06 CONSOLIDATED VERSION Medical Device Software -. Software Life Cycle Processes

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IEC 62366-1 Edition 1.0 2015-02 Medical devices Part 1: Application of usability . engineering to medical devices [Including CORRIGENDUM 1 (2016)]
NEMA PS 3.1 - 3.20 2021e Digital Imaging and Communications in Medicine (DICOM) Set

The following FDA Guidance Documents have been applied:

Format for Traditional and Abbreviated 510(k)s, 2019 ●
. Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices, 2005
Content of Premarket Submissions for Management of Cybersecurity in Medical . Devices, 2014
Cybersecurity for Networked Medical Devices Containing Off-the-Shelf (OTS) . Software, 2005
Off-The-Shelf Software Use in Medical Devices, 2019 ●
Applying Human Factors and Usability Engineering to Medical Devices, 2016 ●
Design Considerations and Premarket Submission Recommendations for Interoperable Medical Devices, 2017

Conclusion of Substantial Equivalence Discussion:

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).