(318 days)
Yes
The device description explicitly states that "segmentation is performed using specialized neuronal networks" and later clarifies this as "Segmentation by machine learning (supervised voxel classification by a Convolutional Neuronal Network)". It also mentions that "Both devices use the same machine learning procedures (supervised voxel classification by Convolutional Neural Networks)".
No.
The device is described as software used for automatic labeling, visualization, and volumetric quantification of brain structures from MR images, which aids in diagnosis and assessment but does not directly treat or prevent a disease or condition.
Yes
The device aids in the automatic labeling, visualization, and volumetric quantification of brain structures from MR images, which is a process used to assist in the diagnosis of medical conditions. The output (report with volume evaluation, segment size differences, and white matter lesion information) is intended to be evaluated by physicians in a professional healthcare setting for diagnostic purposes. Its function is to provide objective measurements and comparisons that can inform a clinical diagnosis.
Yes
The device description explicitly states "AIRAscore is a software" and details its functions and data processing without mentioning any accompanying hardware components that are part of the medical device itself. It processes existing MR images and outputs reports and data via DICOM, which are software-based interactions.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In vitro diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections. They are used to examine specimens outside of the body.
- Device Function: The description clearly states that AIRAscore processes MR images of the brain. This is an imaging modality, not a test performed on a biological sample.
- Intended Use: The intended use is for automatic labeling, visualization, and volumetric quantification of brain structures from MR images. This is a form of medical image analysis and processing, not an in vitro diagnostic test.
The device is a software tool for analyzing medical images, which falls under a different regulatory category than IVDs.
No
The provided input text does not contain any explicit statement that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device. The reference to "Control Plan Authorized (PCCP) and relevant text: Not Found" further confirms this.
Intended Use / Indications for Use
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.
Product codes
LLZ
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.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
T1-weighted and (optional) fluid-attenuated inversion recovery (FLAIR) MR images from a single time point
Anatomical Site
Brain
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Primary users of the system are physicians with finished course of studies, medical license and expert knowledge in neuroanatomy and MR-imaging of the head. The reports and control images are looked at and evaluated in a professional healthcare setting (diagnostic workstation or doctor's office).
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Not Found
Summary of Performance Studies
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.
Key Metrics
Not Found
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 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).
0
Image /page/0/Picture/0 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 blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
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
1
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
2
Indications for Use
510(k) Number (if known)
K223180
Device Name AIRAscore
Indications for Use (Describe)
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.
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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3
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 GmbH
Konrad-Adenauer-Str. 13
72072 Tübingen
Germany |
|-----------------|-------------------------------------------------------------------------------------------|
| Contact Person: | Dr. Maximilian Stalter
Email: maximilian.stalter@airamed.de
phone: +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
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 and Function
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.
4
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 |
| Regulation | ||
| Number | 892.2050 | 892.2050 |
| Product Code | LLZ | LLZ |
| Intended Use / | ||
| Indications for | ||
| Use | 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. | icobrain is intended for automatic | |
| labeling, visualization and volumetric | ||
| quantification of segmentable brain | ||
| structures from a set of MR or NCCT | ||
| 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 or NCCT | ||
| images. | ||
| icobrain consists of two distinct image | ||
| processing pipelines: icobrain cross | ||
| and icobrain long. | ||
| icobrain cross is intended to provide | ||
| volumes from images acquired at a | ||
| single timepoint. icobrain long is | ||
| intended to provide changes in | ||
| volumes between two images that | ||
| were acquired on the same scanner, | ||
| Technical | ||
| Characteristics | AIRAscore is a software as medical | |
| device (SaMD) that runs on | ||
| AIRAmed internal servers (Software | ||
| as a Service - SaaS). For sending and receiving DICOM | ||
| data dedicated interfaces are | ||
| supplied as accessory. Operates on off-the-shelf hardware | ||
| (multiple vendors) DICOM compatible Segmentation by machine learning | ||
| (supervised voxel classification by a | ||
| Convolutional Neuronal Network) Input: T1-weighted and (optional) fluid- | ||
| attenuated inversion recovery | ||
| (FLAIR) MR images from a single | ||
| time point Output: Multiple electronic report with | ||
| volumetric information of brain | ||
| structures (Encapsulated PDF | ||
| DICOM) Annotated DICOM images for visual | ||
| inspection by an expert (Secondary | ||
| Capture DICOM) | with the same image acquisition | |
| protocol and with the same contrast at | ||
| two different timepoints. The results of | ||
| icobrain cross cannot be compared | ||
| with the results of icobrain long. Software package Operates on off-the-shelf hardware | ||
| (multiple vendors) DICOM compatible Segmentation by classical machine | ||
| learning and deep learning | ||
| (supervised voxel classification by a | ||
| Convolutional Neuronal Network) Input: T1-weighted and fluid-attenuated | ||
| inversion recovery (FLAIR) MR | ||
| images from a single or multiple | ||
| time points Non-contrast CT from a single time | ||
| point Output: Multiple electronic reports with | ||
| volumetric information of brain | ||
| structures and midline shift Annotated DICOM images | ||
| Performance | ||
| Measurement | ||
| Testing | 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 | Accuracy Brain segmentable structure | |
| volumes / volume changes | ||
| compared to simulated and/or | ||
| manually labeled ground truth Reproducibility Brain segmentable structure | ||
| volumes / volume changes | ||
| compared on test-retest images | ||
| Environment of | ||
| Use | Primary users of the system are | |
| physicians with finished course of | ||
| studies, medical license and expert | ||
| knowledge in neuroanatomy and MR- | ||
| imaging of the head. The reports and | ||
| control images are looked at and | ||
| evaluated in a professional healthcare | ||
| setting (diagnostic workstation or | ||
| doctor's office). | icobrain is used by trained | |
| professionals in hospitals, imaging | ||
| centers 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
5
6
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
7
- 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:
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.