AI-Rad Companion Brain MR VA50 is an enhancement to the predicate, AI-Rad Companion Brain MR VA40 (K213706). Just as in the predicate, the brain morphometry feature of AI-Rad Companion Brain MR addresses the automatic quantification and visual assessment of the volumetric properties of various brain structures based on T1 MPRAGE datasets. From a predefined list of brain structures (e.g. Hippocampus, Caudate, Left Frontal Gray Matter, etc.) volumetric properties are calculated as absolute and normalized volumes with respect to the total intracranial volume. The normalized values are compared against age-matched mean and standard deviations obtained from a population of healthy reference subjects. The deviation from this reference population can be visualized as 3D overlay map or out-of-range flag next to the quantitative values.

Additionally, identical to the predicate, the white matter hyperintensities feature addresses the automatic quantification and visual assessment of white matter hyperintensities on the basis of T1 MPRAGE and T2 weighted FLAIR datasets. The detected WMH can be visualized as a 3D overlay map and the quantification in count and volume as per 4 brain regions in the report.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study details for the AI-Rad Companion Brain MR device, based on the provided FDA 510(k) summary:

Acceptance Criteria and Device Performance

Metric	Acceptance Criteria	Reported Performance (AVG)	95% CI	Standard Deviation (STD)
Volumetric Segmentation Accuracy	PCC >= 0.77	0.94 PCCC	[0.83, 0.98]	n.a.
Voxel-wise Segmentation Accuracy	Mean Dice score >= 0.47	0.50 Dice	[0.42, 0.57]	0.22
WMH Change Region-wise Segmentation Accuracy	Median F1-score >= 0.69	0.69 F1-score	[0.633, 0.733]	0.13

Study Details

Sample Size and Data Provenance:
- Test Set Sample Size: 75 subjects / 150 studies (2 scans per subject).
- Data Provenance: The data originate from a mix of retrospective and potentially prospective sources, from both the US and Europe:
  - UPenn (US): 15 subjects
  - ADNI (US): 15 subjects
  - Lausanne (EU): 22 subjects
  - Prague (EU): 23 subjects
  - Medical Indication: 60 Multiple Sclerosis (MS) patients, 15 Alzheimer's (AD) patients.
  - Age Range: 25-88 years.
  - Gender Distribution: 56 females, 19 males.
  - Scanner Info: Siemens 3.0T MR scanners, T1w MPRAGE and T2w FLAIR scan protocols.
Number of Experts and Qualifications for Ground Truth:
- The document states that for each dataset, three sets of ground truth were manually annotated. Each set was annotated by a "disjoint group of annotator, reviewer, and clinical expert."
- For the initial annotation and review, "in-house annotators" and "in-house reviewers" were used.
- For final review and correction, a "clinical expert" was used, randomly assigned per case to minimize bias.
- Specific qualifications (e.g., years of experience, board certification) for these experts are not explicitly stated in the provided text, beyond being "clinical experts."
Adjudication Method for Test Set:
- The ground truth process involved a multi-step adjudication. For each test dataset:
  1. Three initial annotations by three different in-house annotators.
  2. Each initial annotation was reviewed by an in-house reviewer.
  3. Each initial annotation (after in-house review) was reviewed by a reference clinical expert.
  4. If corrections by the clinical expert were "significant and time-consuming," they were communicated back to the annotator for correction and then re-reviewed.
- This resembles a form of iterative consensus building and expert adjudication, where multiple initial annotations are refined through reviewer and expert input, rather than a strict N+1 or N+M voting system for final ground truth, though the final decision appears to rest with the clinical expert.
Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
- No MRMC study was done. The document explicitly states: "The predicate (K213706) was not validated using clinical tests and therefore no clinical tests were conducted to test the performance and functionality of the modifications introduced within AI-Rad Companion Brain MR."
- The validation focused on standalone algorithmic performance compared to expert-established ground truth and comparison against a "reference device" (icobrain) using equivalent validation methodology for the WMH follow-up feature.
- Therefore, there's no reported effect size of human readers improving with AI vs. without AI assistance.
Standalone (Algorithm Only) Performance Study:
- Yes, a standalone performance study was conducted for the WMH follow-up feature. The acceptance criteria and reported performance metrics (PCC, Dice, F1-score) are for the algorithm's performance against the established ground truth.
Type of Ground Truth Used:
- The ground truth for the White Matter Hyperintensities (WMH) Follow-Up Feature was established through expert consensus and manual annotation. It involved a "disjoint group of annotator, reviewer, and clinical expert" for each ground truth dataset. The clinical expert performed the final review and correction.
Training Set Sample Size:
- The document states: "The training data used for the fine tuning the hyper parameters of WMH follow-up algorithm is independent of the data used to test the white matter hyperintensity algorithm follow up algorithm."
- However, the specific sample size for the training set is not provided in the given text. It mentions independent training data but does not quantify it.
How Ground Truth for Training Set was Established:
- The document mentions that training data was used for "fine tuning the hyper parameters." While it implies that the training data would also require ground truth, the method for establishing ground truth for the training set is not explicitly described in the provided text. It only states that the training data was "independent" of the test data. Given the "WMH follow-up algorithm does not include any machine learning component," the type of "training" might refer to calibration or rule optimization rather than machine learning model training in the conventional sense, and subsequently, how ground truth for that calibration was established is not detailed.

Summary

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October 23, 2023

Siemens Medical Solutions U.S.A. % Kira Morales Regulatory Affairs Manager 40 Liberty Blvd. Malvern, PA 19355

Re: K232305

Trade/Device Name: AI-Rad Companion Brain MR Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: July 31, 2023 Received: August 1, 2023

Dear Kira Morales:

We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrb/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

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Page 2

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products): good manufacturing practice requirements as set forth in the quality systems (OS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

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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).

Sincerely,

Signature

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

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Indications for Use

510(k) Number (if known) K232305

Device Name AI-RAD Companion Brain MR

Indications for Use (Describe)

AI-Rad Companion Brain MR is a post-processing image analysis software that assists clinicians in viewing, analyzing, and evaluating MR brain images.

Al-Rad Companion Brain MR provides the following functionalities:

Automated segmentation and quantitative analysis of individual brain structures and white matter hyperintensities
Quantitative comparison of brain structure with normative data from a healthy population
Presentation of results of reporting that includes all numerical values as well as visualization of these results

X Prescription Use (Part 21 CFR 801 Subpart D)

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

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510(k) SUMMARY FOR AI-Rad Companion Brain MR

Submitted by: Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19355 Date Prepared: July 31, 2023

This summary of 510(k) safety and effectiveness information is being submitted in accordance with the requirements of Safe Medical Devices Act of 1990 and 21 CFR §807.92.

1. Submitter

Importer/Distributor	Siemens Medical Solutions USA, Inc.40 Liberty BoulevardMalvern, PA 193552Registration Number: 2240869
Manufacturing Site	Siemens Healthcare GmbHHenkestrasse 127Erlangen, Germany 91052Registration Number: 3002808157

2. Contact Person

Kira Morales Regulatory Affairs Manager Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19335 Phone: +1 (484) 901 - 9471 Email: kira.morales@siemens-healthineers.com

3. Device Name and Classification

Product Name:	AI-Rad Companion Brain MR
Trade Name:	AI-Rad Companion Brain MR
Classification Name:	Medical Image Management and Processing System
Classification Panel:	Radiology
CFR Section:	21 CFR §892.2050

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Device Class:	Class II
Product Code:	QIH

4. Predicate Device

Product Name:	AI-Rad Companion Brain MR
Propriety Trade Name:	AI-Rad Companion Brain MR
510(k) Number:	K213706
Clearance Date:	April 15, 2022
Classification Name:	Picture Archiving and Communication System
Classification Panel:	Radiology
CFR Section:	21 CFR §892.2050
Secondary CFR Section:	21 CFR §892.1000
Device Class:	Class II
Primary Product Code:	QIH
Recall Information:	N/A

5. Reference Device

Product Name:	icobrain
510(k) Number:	K192130
Clearance Date:	December 13, 2019
Classification Name:	Picture Archiving and Communication System
Classification Panel:	Radiology
CFR Section:	21 CFR §892.2050
Device Class:	Class II
Primary Product Code:	LLZ
Recall Information:	N/A

6. Indications for Use

AI-Rad Companion Brain MR is a post-processing image analysis software that assists clinicians in viewing, analyzing, and evaluating MR brain images.

AI-Rad Companion Brain MR provides the following functionalities.

. Automated segmentation and quantitative analysis of individual brain structures and white matter hyperintensities
. Quantitative comparison of each brain structure with normative data from a healthy population
Presentation of results for reporting that includes all numerical values as well as . visualization of these results

7. Device Description

AI-Rad Companion Brain MR VA50 is an enhancement to the predicate, AI-Rad Companion Brain MR VA40 (K213706). Just as in the predicate, the brain morphometry feature of AI-Rad

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Companion Brain MR addresses the automatic quantification and visual assessment of the volumetric properties of various brain structures based on T1 MPRAGE datasets. From a predefined list of brain structures (e.g. Hippocampus, Caudate, Left Frontal Gray Matter, etc.) volumetric properties are calculated as absolute and normalized volumes with respect to the total intracranial volume. The normalized values are compared against age-matched mean and standard deviations obtained from a population of healthy reference subjects. The deviation from this reference population can be visualized as 3D overlay map or out-of-range flag next to the quantitative values.

8. Substantially Equivalent (SE) and Technological Characteristics

The intended use of the predicate device and the subject device are equivalent. The main difference is that AI-Rad Companion Brain MR VA50 adds the additional features of the Brain Morphometry follow-up feature and White Matter Hyperintensities Follow-up as compared to the predicate, AI-Rad Companion Brain MR VA40.

The subject device. AI-Rad Companion Brain MR VA50 is substantially equivalent with regard to the intended use and technical characteristics compared to the predicate device, AI-Rad Companion Brain MR VA50 (K213706) with respect to the software features, functionalities, and core algorithms. The additional features, enhancements and improvements provided in AI-Rad Companion Brain MR VA50 increase the usability and reduce the complexity of the imaging workflow for the clinical user.

Icobrain serves as a reference device within this submission and a dedicated comparison of technological characteristics is provided. Siemens Healthineers has determined that AI-Rad Companion Brain MR VA50 is comparable to icobrain (K192130) as it has similar technological and performance characteristics with respect to the white matter hyperintensities feature (cleared in predicate K213706) and white matter hyperintensities follow-up feature. Comparable to the subject device, icobrain produces reports for the segmentation and volumetric analysis of FLAIR white matter hyperintensities in the peri-ventricular, juxta-cortical, infra-tentorial, and deep white matter hyperintensity regions. Comparable to the white matter hyperintensities follow-up feature, Icobrain identifies volume changes (new or enlarging) of white matter hyperintensities between two images at two different time points within the 4 brain regions and produces a progression map indicating the location of the new or enlarged area, a total count of new or enlarged areas and a total volume of new or enlarged areas. AI-Rad Companion Brain MR VA50 used equivalent validation methodology to analyze the performance of the white matter hyperintensities follow-up feature compared to icobrain (K192130).

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The risk analysis and non-clinical data support that both devices perform equivalently and do not raise different questions of the safety and effectiveness.

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DICOMSR/SC/PDF	AI-Rad CompanionBrain MR exports thenumerical reports(DICOM SR, DICOMSC and PDF) in bothmanual and automaticconfirmation.	Data transfer is handledby the teamplay Imagesinfrastructure and usesthe DICOM standard.The results are sent backto a configurable targetnode via the teamplaydigital health platforminfrastructure inaccordance with theDICOM standards.	DICOM structuredreport
--------------------	---------------------------------------------------------------------------------------------------------------------------------------------------	-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------	----------------------------

Table 1: Comparison table for AI-Rad Companion Brain MR VA50, predicate device AI-Rad Companion Brain MR VA40 (K213706) and reference device icobrain (K192130)

The conclusions from all verification and validation data suggest that these enhancements are equivalent with respect to safety and effectiveness of the predicate device. These modifications do not change the intended use of the product. Siemens is of the opinion that AI-Rad Companion Brain MR VA50 is substantially equivalent to the currently marketed device, AI-Rad Companion Brain MR VA40

9. Nonclinical Tests

Non-clinical tests were conducted to test the functionality of AI-Rad Companion Brain MR. Software validation and bench testing have been conducted to assess the performance claims as well as the claim of substantial equivalence to the predicate device. Non-clinical performance testing demonstrates that AI-Rad Companion Brain MR complies with appropriate FDA guidance documents as well as with the following voluntary FDA recognized Consensus Standards (Table 2).

RecognitionNumber	ProductArea	Title of Standard	ReferenceNumber andDate	StandardsDevelopmentOrganization
5-129	General	Medical Devices – Applicationof usability engineering tomedical devices [includingCorrigendum 1 (2016)]	IEC 62366-1Edition 1.12020-06CONSOLIDATEDVERSION	IEC
5-125	General	Medical Devices – applicationof risk management tomedical devices	ISO 14971 ThirdEdition 2019-12	ISO
13-79	Software/Informatics	Medical device software –software life cycle processes[Including Amendment 1(2016)]	IEC 62304Edition 1.12015-06	AAMIANSIIEC

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			CONSOLIDATEDVERSION
12-349	Radiology	Digital Imaging andCommunications in Medicine(DICOM) Set	PS 3.1 - 3.202021e	NEMA
5-134	General	Medical devices – symbols tobe used with information tobe supplied by themanufacturer – Part 1:General Requirements	15223-1Fourth edition2021-07	ISOIEC
13-97	Software/Informatics	Health software - Part 1:General requirements forproduct safety	82304-1Edition 1.02016-10	IEC

Table 2: List of recognized standards

Verification and Validation

Software documentation for a Moderate Level of Concern software, per FDA's Guidance Document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" issued on May 11, 2005, is also included as part of this submission. The performance data demonstrates continued conformance with special controls for medical devices containing software. Non-clinical tests were conducted on the subject device during product development.

Software "bench" testing in the form of Unit. System and Integration tests were performed to evaluate the performance and functionality of the new features and software updates. All testable requirements in the Requirement Specifications and the Risk Analysis have been successfully verified and traced in accordance with the Siemens Healthineers DH product development (lifecycle) process. Human factor usability validation is addressed in system testing and usability validation test records. Software verification and regression testing have been performed successfully to meet their previously determined acceptance criteria as stated in the test plans.

Siemens Healthineers adheres to the cybersecurity requirements as defined the FDA Guidance "Content of Premarket Submissions for Management for Cybersecurity in Medical Devices," issued October 2, 2014 by implementing a process of preventing unauthorized access, modifications, misuse or denial of use, or the unauthorized use of information that is stored, accessed, or transferred from a medical device to an external recipient.

10. Performance Software Validation

AI-Rad Companion Brain MR VA50A brain morphometry feature is identical to the predicate device AI-Rad Companion Brain MR VA20A.

AI-Rad Companion Brain MR VA50A White Matter Hyperintensities (WMH) segmentation feature is identical to the predicate device AI-Rad Companion Brain MR VA40A

In AI-Rad Companion Brain MR VA50A 2 features were added as below:

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SIEME Healthineers

The Brain Morphometry follow-up feature ●
White Matter Hyperintensities Follow-up ●

Brain Morphometry Follow-Up Feature

The Brain Morphometry follow-up feature of the subject device automatically calculates the atrophy range in percentage for each segmented brain structure using the Brain Morphometry feature. The Brain Morphometry follow-up feature takes the two MPRAGE scans from two timepoints of the same patient as input and calculates atrophy rates between timepoints. Brain morphometry follow-up consists of atrophy rate calculation Morphometry follow-up does not include any machine learning or deep learning component therefore it is verified by V&V testing, and no additional evaluation is provided in this document.

White Matter Hyperintensities Follow-Up Feature

To validation AI-Rad Companion Brain MR software from a clinical perspective, the white matter hyperintensities follow-up feature underwent a scientific evaluation. The results of clinical data-based software validation for the subject device AI-Rad Companion Brain demonstrated equivalent performance in comparison to the reference device and literature. A complete scientific evaluation report is provided in support of the device modifications. The brain morphometry & white matter hyperintensities algorithms, unchanged from the predicate. did not undergo a new scientific evaluation.

Performance testing for AI-Rad Companion Brain MR WMH follow-up was performed on Siemens Healthineers test data from 75 subjects, which included Multiple Sclerosis patients (MS) and Alzheimer's patients (AD). Testing data had more female subjects as Multiple Sclerosis occurs in females more as compare to male subjects. and a balanced distribution with respect to age of the patient according to target patient population and field strength of the MR scanner used. For each dataset, three sets of ground truth of white matter hyperintensity changes between two time points are annotated manually. Each set is annotated by a disjoin group of annotator, reviewer and clinical expert, with the expert randomly assigned per case. For each test dataset, the three initial annotations are annotated by three different in-house annotators, then each initial annotation is reviewed by the in-house reviewer. Afterwards, each initial annotation is reviewed by the referred clinical expert.

Validation Type	Acceptance Criteria
Volumetric Segmentation Accuracy	A PCC >= 0.77 is considered as a passed casefor volumetric segmentation accuracy
Voxel-wise Segmentation Accuracy	A mean Dice score >=0.47 is considered as apassed case for segmentation quality
WMH Change Region-wise SegmentationAccuracy	A median F1-score >=0.69 is considered apassed case

Acceptance Criteria:

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Summary Performance data, Standard Deviations & CIs:

	VolumetricSegmentation	Voxel-wise Segmentation	WMH Lesion-wiseSegmentation
	PCC	Dice	F1-score
AVG	0.94	0.50	0.69
STD	n.a.	0.22	0.13
95% CI	[0.83,0.98]	[0.42,0.57]	[0.633,0.733]

Testing Data Information:

Testing Cohort
# Subjects	75
# Studies	150 (2 scans per subject)
# of Females	56
# of Males	19
Age Range	25-88
Medical Indication	MS: 60Alzheimer's: 15
Scan Protocol	T1w MPRAGET2w FLAIR
Field Strength	3.0T
Manufacturer	Siemens
Data Origin	UPenn: (US): 15ADNI (US): 15Lausanne (EU): 22Prague (EU): 23

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Standard Annotation Process:

For each dataset, three sets of ground truth of white matter hyperintensity changes between two time points are annotated manually. Each set is annotated by a disjoint group of annotator, reviewer, and clinical expert, with the expert randomly assigned per case to minimize annotation bias. For each test dataset, the three initial annotations are annotated by three different in-house annotators. Then, each initial annotation is reviewed by the in-house reviewer. Afterwards, each initial annotation is reviewed by the referred clinical expert. The clinical expert reviews and corrects the initial annotation of the changed WMH areas according to the annotation protocol. If the corrections are significant and time-consuming, the corrections are communicated to the annotator for correction and then re-reviewed.

Testing & Training Data Independence:

WMH follow-up algorithm does not include any machine learning component. The training data used for the tine tuning the hyper parameters of WMH follow-up algorithm is independent of the data used to test the white matter hyperintensity algorithm follow up algorithm.

11. Summary of Nonclinical Tests

Based on the nonclinical performance documented within the Scientific Evaluation, AI-Rad Companion Brain MR VA50 was found to have a safety and effectiveness profile that is similar to the predicate. Since the predicate device was cleared based on the results of the prior conducted scientific evaluation, the same methodology was required to support the substantial equivalence. The nonclinical data and verification and validation results supports the safety and effectiveness of the subject device in that it should performs comparable to the predicate device that is currently marketed.

12. Summary of Clinical Tests

The predicate (K213706) was not validated using clinical tests and therefore no clinical tests were conducted to test the performance and functionality of the modifications introduced within AI-Rad Companion Brain MR. Verification and validation of the enhancements and improvements have been performed and these modifications have been validated for their intended use. No animal testing has been performed on the subject device.

13. Safety and Effectiveness

The device labeling contains instructions for use and any necessary cautions and warnings to ensure safe and effective use of the device.

Risk management is ensured via ISO 14971:2019 compliance to identify and provide mitigation of potential hazards in a risk analysis early in the design phase and continuously throughout the development of the product. These risks are controlled via measures realized during software development, testing and product labeling.

Furthermore, the device is intended for healthcare professionals familiar with the post processing of magnetic resonance images.

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).

	Subject Device:AI-Rad CompanionBrain MR VA50A	Predicate Device:AI-Rad CompanionBrain MR VA40(K213706)	Reference Device:icobrain (K192130)
Indications for Use	AI-Rad CompanionBrain MR is a post-processing imageanalysis software thatassists clinicians inviewing, analyzing, andevaluating MR brainimages.AI-Rad CompanionBrain MR provides thefollowingfunctionalities:• Automaticsegmentation andquantitative analysisof individual brainstructures andwhite matterhyperintensities• Quantitativecomparison of eachbrain structure withnormative data froma healthypopulation• Presentation ofresults for reportingthat includes allnumerical values aswell as visualizationof these results	AI-Rad CompanionBrain MR is a post-processing imageanalysis software thatassists clinicians inviewing, analyzing, andevaluating MR brainimages.AI-Rad CompanionBrain MR provides thefollowingfunctionalities:• Automaticsegmentation andquantitative analysisof individual brainstructures and whitematterhyperintensities• Quantitativecomparison of eachbrain structure withnormative data froma healthy population• Presentation ofresults for reportingthat includes allnumerical values aswell as visualizationof these results	icobrain is intendedfor automaticlabeling,visualization andvolumetricquantification ofsegmentable brainstructuresfrom a set of MR orNCCT images. Thissoftware is intendedto automate thecurrent manualprocess ofidentifying,labeling andquantifying thevolume ofsegmentable brainstructures identifiedon MR or NCCTimages. icobrainconsists of twodistinct imageprocessing pipelines:icobrain cross andicobrain long.icobrain cross isintended to providevolumes from MR orNCCT imagesacquired at a singletime point. icobrainlong is intended toprovide changes involumes between twoMR images that wereacquired on the samescanner
			with the same imageacquisition protocoland with samecontrast at twodifferent timepoints.The results oficobrain cross cannotbe compared with theresults of icobrainlong.
BrainMorphometry	Pre-processingfunctionality forautomatic segmentationand volumetry ofMPRAGE data.	Pre-processingfunctionality forautomatic segmentationand volumetry ofMPRAGE data.	Image processing forautomaticsegmentation andvolumetry ofMPRAGE data.
Brain WhiteMatterHyperintensitiesSegmentation	AI-Rad CompanionBrain MR White MatterHyperintensities(WMH) includessegmentation andquantification of WhiteMatter Hyperintensitieson the basis of T1-weightedMPRAGE and T2-weighted FLAIRdatasets as input.	Pre-processingfunctionality forautomatic segmentationand volumetry ofMPRAGE and FLAIRdata.	Image processing forautomaticsegmentation andvolumetry of FLAIRdata.
Brain WhiteMatterHyperintensitiesQuantification	The WMH reportcontains visualization ofWMH (3D overlay ofWMH map) andnumeric results of countand volume of WMH asper four brain regionsperiventricular,juxtacortial,infratentorial and deepwhite matter.	The WMH reportcontains visualization ofWMH (3D overlay ofWMH map) andnumeric results of countand volume of WMH asper four brain regionsperiventricular,juxtacortial,infratentorial and deepwhite matter.	Unnormalizedvolume and volumechanges of FLAIRwhite matterhyperintensities asper 4 brain regions
Follow-up	The longitudinalassessment of Brain MRimages from twotimepoints provides therate of change ofvolumes of brainstructures and the countand volume of new orenlarged WMH.	The follow-up feature isnot available in theequivalent device (AI-Rad Companion BrainMR VA40)	Assessment ofNew/Enlarging lesioncount
Brain WhiteMatterHyperintensitiesMap	Calculation of whitematter hyperintensitiesmap fused with theprocessed FLAIR dataUser customizable colorlabels for the overlaymap.	Calculation of whitematter hyperintensitiesmap fused with theprocessed FLAIR dataUser customizable colorlabels for the overlaymap.	Calculation of whitematterhyperintensities mapoverlaid with theFLAIR data
BrainMorphometry:Quantification	Calculation of labelmaps (display of brainsegmentation) andpartially combined labelmaps (fused with theprocessed MPRAGEdata).	Calculation of labelmaps (display of brainsegmentation) andpartially combined labelmaps (fused with theprocessed MPRAGEdata).	Normalized andunnormalized volumeand volume changesof different brainstructures.
BrainMorphometry:Deviation Mapand Label map	• Deviation resultsinclude deviation map,which presents differentbrain regions, color-coded to indicate thedegree of deviation fromthe average age- andgender-matchednormative volume.• Label results includethe label map, whichshows different brainregions using differentcolors.	• Deviation resultsinclude deviation map,which presents differentbrain regions, color-coded to indicate thedegree of deviation fromthe average age- andgender-matchednormative volume.• Label results includethe label map, whichshows different brainregions using differentcolors.	Not available
Distribution &Archiving	Creation of an imageseries for morphometryand WMH reports.Automatic transfer ofgenerated maps and	Creation of an imageseries for morphometryand WMH reports.Automatic transfer ofgenerated maps and	Automatic transfer ofgenerated imageseries and report to aPACS system.
	reports to a PACSsystem.	reports to a PACSsystem.
Architecture	Cloud-based and on-edge deployment in the institution premise. For Edge deployment, the data processing is performed within the institution whereas logging, institution management, and audit logging are performed in the cloud.	Cloud-based and on-edge deployment in the institution premise. For Edge deployment, the data processing is performed within the institution whereas logging, institution management, and audit logging are performed in the cloud.	Cloud only solution with no components deployed on customer premise.
Communication	PACS (DICOM compatible)	PACS (DICOM compatible)	PACS (DICOM compatible)
User interface	Configuration UIUser can activate or deactivate the processing of brain MR cases in AI-Rad Companion Engine. If you activate AI-Rad Companion Brain MR, then the brain MR cases uploaded to AI-Rad Companion Engine are processed and is displayed in the Patient List. You can manually adjust the settings available in the General Settings screen. If you deactivate AI-Rad Companion Brain MR, then no brain MR cases are processed in AI-Rad Companion Engine. The settings available in the General Settings screen are not displayed. To activate or deactivate AI-Rad Companion Brain MR, see Configuring AI-Rad	Configuration UIUser can activate or deactivate the processing of brain MR cases in AI-Rad Companion Engine. If you activate AI-Rad Companion Brain MR, then the brain MR cases uploaded to AI-Rad Companion Engine are processed and is displayed in the Patient List. You can manually adjust the settings available in the General Settings screen. If you deactivate AI-Rad Companion Brain MR, then no brain MR cases are processed in AI-Rad Companion Engine. The settings available in the General Settings screen are not displayed. To activate or deactivate AI-Rad Companion Brain MR, see Configuring AI-Rad	Not available
	Companion in the AI-Rad Companion EngineInstructions for Use.Longitudinal (Follow-up) configurations andSupport for output indifferent orientations areadded.Conformation UIOn the Results Preview,user can confirm ordecline the results andsend them to the PACS.All changes aretemporarily saved untilthe case is sent to thePACS.	Companion in the AI-Rad Companion EngineInstructions for Use.Conformation UIOn the Results Preview,user can confirm ordecline the results andsend them to the PACS.All changes aretemporarily saved untilthe case is sent to thePACS.
Softwarerequirement/Operating system	AI-Rad CompanionBrain MR was tested onMicrosoft Windows 10.AI-Rad CompanionBrain MR is notvalidated on any otheroperating system, forexample, MAC.AI-Rad CompanionBrain MR is notvalidated for use withtouch screen or mobiledevices.AI-Rad CompanionNotifier requires a 64-bitWindows OperatingSystem (Windows 10recommended). It isrecommended to useGoogle Chrome as apreferred web browserfor use with AI-RadCompanion Brain MR.	AI-Rad CompanionBrain MR was tested onMicrosoft Windows 10.AI-Rad CompanionBrain MR is notvalidated on any otheroperating system, forexample, MAC.AI-Rad CompanionBrain MR is notvalidated for use withtouch screen or mobiledevices.AI-Rad CompanionNotifier requires a 64-bitWindows OperatingSystem (Windows 10recommended). It isrecommended to useGoogle Chrome as apreferred web browserfor use with AI-RadCompanion Brain MR.	Not available
Systemdeployment	Cloud based deploymentEdge deploymentPlatform	Cloud based deploymentEdge deploymentPlatform	Cloud Only Solution