AI-Rad Companion (Cardiovascular) is image processing software that provides quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images to support radiologists and physicians from emergency medicine, specialty care, urgent care, and general practice in the evaluation and assessment of cardiovascular diseases.

It provides the following functionality:

· Segmentation and volume measurement of the heart
· Quantification of the total calcium volume in the coronary arteries
Segmentation of the aorta
· Measurement of maximum diameters of the aorta at typical landmarks
· Threshold-based highlighting of enlarged diameters

The software has been validated for non-cardiac chest CT data with filtered backprojection reconstruction from Siemens Healthineers, GE Healthcare, Philips, and Toshiba/Canon. Additionally, the calcium detection feature has been validated on non-cardiac chest CT data with iterative reconstruction from Siemens Healthineers.

Only DICOM images of adult patients are considered to be valid input.

Device Description

In general, AI-Rad Companion (Cardiovascular) is a software only image post-processing application that uses deep learning algorithms to post-process CT data of the thorax.

The subject device AI-Rad Companion (Cardiovascular) is an image processing software that utilizes deep learning algorithms to provide quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images to support radiologists and physicians from emergency medicine, specialty care, urgent care, and general practice in the evaluation and assessment of disease of the thorax. The subject device supports the following device specific functionality:

Segmentation and volume measurement of heart
. Identification and measurement of volume with high Hounsfield values -- related to coronary calcification
. Segmentation of the aorta and determination of 9 Landmarks
. Computation of cross-sectional MPRs at the 9 landmarks and their maximum diameter
Measurement of maximum diameters of the aorta at typical landmarks ●
Threshold-based classification of diameters into different categories ●

AI/ML Overview

Here's an analysis of the acceptance criteria and study details for the AI-Rad Companion (Cardiovascular) device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly present a formal "acceptance criteria" table with specific pass/fail thresholds for each metric. Instead, it reports performance results (sometimes implicitly as "equivalent" or "consistent") that are presumably within acceptable limits for FDA clearance given the substantial equivalence claim.

Feature / Metric	Acceptance Criteria (Implicitly Met)	Reported Device Performance and Confidence Intervals
Coronary Calcium Volume Quantification	Performance equivalent to predicate device	Logarithmic correlation coefficient of total coronary calcium volume between subject and predicate device was 0.96 (N=381).
Aorta Diameter Measurements (Average Absolute Error)	Performance consistent across critical subgroups and within acceptable limits	Average absolute error in aorta diameters was 1.6 mm (95% confidence interval: [1.5 mm, 1.7 mm]) across all nine measurement locations.
Aorta Diameter Measurements (Per Location)	Performance consistent across critical subgroups and within acceptable limits	Varied between 0.9 mm and 2.4 mm per location (N=193).
Consistency across Subgroups	Performance consistent for critical subgroups (vendors, slice thickness)	Performance was consistent for all critical subgroups, such as vendors or slice thickness.
Software Functionality	All software specifications met acceptance criteria	All testable requirements in the Engineering Requirements Specifications keys, Subsystem Requirements Specifications keys, and the Risk Management Hazard keys have been successfully verified and traced. Testing results support that all software specifications have met the acceptance criteria.
Risk Management	Identified hazards are mitigated	Risk analysis completed and risk control implemented to mitigate identified hazards.
Human Factors Usability	Human factors addressed and acceptable for safe and effective use	Human Factor Usability Validation showed that Human factors are addressed in the system test and in clinical use tests with customer reports and feedback.

2. Sample Sizes and Data Provenance

Test Set Sample Sizes:
- Coronary Calcium Volume: N = 381 data sets.
- Aorta Diameter Measurements: N = 193 data sets.
Data Provenance: Retrospective performance studies on non-cardiac chest CT data from multiple clinical sites across the United States.

3. Number of Experts and Qualifications

The document does not explicitly state the "number of experts" or their specific "qualifications" used to establish ground truth. However, for the training set, it mentions "Description of ground truth / annotations generation," implying expert involvement. For the validation set, the comparison is primarily against a "predicate device," suggesting that the ground truth for that comparison would have been established previously for the predicate, not necessarily by new experts for this study.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method (e.g., 2+1, 3+1) for the test set's ground truth. The comparison seems to be against the predicate device's output, which would have its own established ground truth based on its clearance.

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

No, an MRMC comparative effectiveness study was not explicitly described in the provided text. The study focuses on the standalone performance of the AI device in comparison to a predicate device, not on how human readers improve with or without AI assistance.

6. Standalone Performance Study (Algorithm Only)

Yes, a standalone performance study was done. The document states: "The performance of the AI-Rad Companion (Cardiovascular) device has been validated in retrospective performance studies..." and details the algorithm's performance metrics (correlation coefficient for calcium, absolute error for aorta diameters) against "predicate device" or implied ground truth, indicating algorithm-only performance.

7. Type of Ground Truth Used

The ground truth for the validation studies appears to be based on:

Comparison to a predicate device's output: For coronary calcium volume quantification, the performance is reported as a correlation between the subject device and the predicate device.
Likely expert-derived measurements previously established or derived from the predicate device's method: For aorta diameter measurements, the "average absolute error" suggests comparison against a reference "true" measurement, which would typically be derived by experts using the predicate's methodology, or a gold standard measurement. The mention of "AHA standard" for diameter categorization also points to established clinical guidelines as a reference.

8. Sample Size for the Training Set

The document states: "Training cohort: size and properties of data used for training O". However, it does not explicitly provide the numerical sample size for the training set.

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

The document briefly mentions under "Data" for each algorithm analysis: "Description of ground truth / annotations generation O". This implies that ground truth was established, likely through expert annotation or a similar process, but does not provide specific details on the methodology (e.g., number of annotators, their qualifications, consensus process) for the training set's ground truth.

Summary

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September 10, 2019

Siemens Medical Solutions USA, Inc. % Mrs. Kimberly Rendon Senior Manager, Regulatory Affairs 40 Liberty Blvd., Mail Code: 65-1A MALVERN PA 19355

Re: K183268

Trade/Device Name: AI-Rad Companion (Cardiovascular) Regulation Number: 21 CFR 892.1750 Regulation Name: Computed tomography x-ray System Regulatory Class: Class II Product Code: JAK, LLZ Dated: August 15, 2019 Received: August 19, 2019

Dear Mrs. Rendon:

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 mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely,

Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

510(k) Number (if known) K183268

Device Name

AI-Rad Companion (Cardiovascular)

Indications for Use (Describe)

It provides the following functionality:

· Segmentation and volume measurement of the heart
· Quantification of the total calcium volume in the coronary arteries
Segmentation of the aorta
· Measurement of maximum diameters of the aorta at typical landmarks
· Threshold-based highlighting of enlarged diameters

Only DICOM images of adult patients are considered to be valid input.

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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510(K) SUMMARY AI-RAD COMPANION (CARDIOVASCULAR) K183268

Date Prepared: August 14, 2019

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

I. Submitter

Importer/Distributor Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19355 Establishment Registration Number 2240869

Manufacturing Site

Siemens Healthcare GmbH Siemensstrasse 1 D-91301 Forchheim, Germany Establishment Registration Number 3004977335

Contact Person

Kimberly Rendon Sr. Manager, Regulatory Affairs

II. Device Name and Classification

Product/Proprietary Trade Name: Classification Name: Classification Panel: CFR Section: Device Class: Product Code: Secondary Product Code:

AI-Rad Companion (Cardiovascular) Computed Tomography X-ray System Radiology 21 CFR §892.1750 Class II JAK LLZ

III. Predicate Device Primary Predicate Device:

Product/Proprietary Trade Name: 510(k) Number: Clearance Date: Classification Name: Classification Panel: CFR Section: Device Class: Primary Product Code: Recall Information:

syngo.CT Calcium Scoring K990426 05/12/1999 Computed Tomography X-Ray System Radiology 21 CFR §892.1750 Class II JAK There are currently no recalls for this device

Secondary Predicate Device:

Product/Proprietary Trade Name: 510(k) Number: Clearance Date: Classification Name: Classification Panel:

syngo.CT Cardiac Function K123585 12/20/2012 Computed Tomography X-Ray System Radiology

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CFR Section:	21 CFR §892.1750
Device Class:	Class II
Primary Product Code:	JAK
Recall Information:	There are currently no recalls for this device

Secondary Predicate Device:

Product/Proprietary Trade Name: syngo aortic Valve Guide 510(k) Number: K113027 Clearance Date: 11/22/2011 Interventional Fluoroscopic X-Ray System Classification Name: Classification Panel: Radiology CFR Section: 21 CFR 8892.1650 Device Class: Class II Primary Product Code: OWB Secondary Product Code: JAA

IV. Device Description

In general, AI-Rad Companion (Cardiovascular) is a software only image post-processing application that uses deep learning algorithms to post-process CT data of the thorax. As an update to the previously cleared devices, the following modifications have been made:

1. Modified Indication for Use Statement
Support of software AI-Rad Companion CT VA10A 2)
- Heart segmentation including measurements (modified) a)
- Calcium detection based on deep learning algorithm (modiffied) b)
- Aorta segmentation (modified) c)
- AHA landmarks for labeling and diameter measurement of the aorta, including threshold-based d) aorta diameter classification (modified)
1. Subject device claims list

Segmentation and volume measurement of heart
. Identification and measurement of volume with high Hounsfield values -- related to coronary calcification
. Segmentation of the aorta and determination of 9 Landmarks
. Computation of cross-sectional MPRs at the 9 landmarks and their maximum diameter
Measurement of maximum diameters of the aorta at typical landmarks ●
Threshold-based classification of diameters into different categories ●

V. Indications for Use

It provides the following functionality:

. Segmentation and volume measurement of the heart
Quantification of the total calcium volume in the coronary arteries ●
. Segmentation of the aorta

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Measurement of maximum diameters of the aorta at typical landmarks
Threshold-based highlighting of enlarged diameters

The software has been validated for non-cardiac chest CT data with filtered backprojection reconstruction from Siemens Healthineers, GE Healthcare, Philips, and ToshibalCanon. Additionally, the calcium detection feature has been validated on non-cardiac chest CT data with iterative reconstruction from Siemens Healthineers.

Only DICOM images of adult patients are considered to be valid input.

VI. Comparison of Technological Characteristics with the Predicate Device

In comparison the predicate device, the subject devices provide outputs in terms of heartVaorta segmentation, coronary calcium visualization and grading, heart/aorta measurement, and labeling. A tabular comparison of the subject device and predicate devices is provided as Table 1 below.

Subject Device	Predicate Device	Comparison Results
SiemensAI-Rad Companion(Cardiovascular)	Siemenssyngo.CT Cardiac Function(K123585)	Modifiedsubject device: deep learning-based algorithmpredicate device: model-based segmentationalgorithm
AI-based Heart Segmentation	Model-based Heart Isolation
Color overlay of MPR andVRT with evaluation results	Basic Reading Functionality	Same
SiemensAI-Rad Companion(Cardiovascular)	Siemenssyngo CaScoring (K990426)	Modifiedsubject device: deep learning-based algorithmpredicate device: model-based segmentationalgorithm
Calcium Detection	Automatic detection of coronaryvessels and coronary calcium
SiemensAI-Rad Companion(Cardiovascular)	SiemensValveGuide (K113027)	Modifiedsubject device: deep learning-based algorithmpredicate device: model-based segmentationalgorithm
Aorta Segmentation	Aortic root segmentation
Landmark Detection	Landmark detection	Modifiedsubject device: deep learning-basedalgorithm, 9 AHA positionspredicate device: model-based segmentationalgorithm, aortic root plane
Aorta diameter measurements	Aorta diameter measurements	Same
Aorta categories	N/A	Newmeasurements are compared with results froma standard population, deviations aresignalized to the user
Color overlay of MPR andVRT with evaluation results	Basic Reading Functionality	Same

Table 1: Predicate Device Comparable Properties

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VII. Performance Data Non-Clinical Testing Summary

Performance tests were conducted to test the functionality of AI-Rad Companion (Cardiovascular). Software validations, bench testing, and clinical data-based software validations have been conducted to the performance claims as well as the claim of substantial equivalence to the predicate devices.

AI-Rad Companion has been tested to meet the requirements of conformity to multiple industry standards. Non-clinical performance testing demonstrated that Al-Rad Companion complies with the following voluntary FDA recognized Consensus Standards listed in Table 2 below.

RecognitionNumber	ProductArea	Title of Standard	PublicationDate	StandardsDevelopmentOrganization
12-300	Radiology	Digital Imaging and Communications inMedicine (DICOM) Set; PS 3.1 - 3.20	06/27/2016	NEMA
13-32	Software	Medical Device Software -Software LifeCycle Processes 62304:2006 (1st Edition)	08/20/2012	AAMI, ANSI, IEC
5-40	Software/Informatics	Medical devices - Application of riskmanagement to medical devices; 14971Second Edition 2007-03-01	08/20/2012	ISO

Table 2: Voluntary Conformance 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, and "Off-The-Shelf Software Use in Medical Devices" 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 AI-Rad Companion (Cardiovascular) software version VA10 during product development.

The Risk analysis was completed, and risk control implemented to mitigate identified hazards. The testing results support that all the software specifications have met the acceptance criteria. Testing for verification and validation for the device was found acceptable to support the claims of substantial equivalence.

Bench testing in the form of Unit, Subsystem and System Integration testing were performed to evaluate the performance and functionality of the new features and software updates. All testable requirements in the Engineering Requirements Specifications keys, Subsystem Requirements Specifications keys, and the Risk Management Hazard keys have been successfully verified and traced in accordance with the Siemens product development (lifecycle) process. The software verification and regression testing have been performed successfully to meet their previously determined acceptance criteria as stated in the test plans. Electrical safety and EMC testing requirements are addressed as part of the host system (CT device or PACS system) to ensure compliance with the application IEC standards.

Siemens conforms to the cybersecurity requirementing 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. Provided in this submission is a cybersecurity statement that considers IEC 80001-1:2010. The responsibility for compliance with IEC 80001-1-2010 is the hospital.

Clinical Data Based Software Validation

To validate the AI-Rad Companion (Cardiovascular) software from clinical perspective, the following algorithms underwent a scientific evaluation:

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● Segmentation of heart

The heart segmentation algorithm computes a segmentation mask of the heart for a given noncardiac chest CT data set. The output segmentation mask is then used to compute of the heart volume and to define a region of interest for the coronary calcium detection algorithm.

. Detection of coronary calcium

The algorithm detects calcifications in the coronary arteries for a given non-cardiac chest CT data set (without contrast, maximum slice thickness is 3 mm). The output is the total volume of the detected coronary calcium.

● Segmentation of aorta

The algorithm computes segmentation masks of the aorta for a given non-cardiac chest CT data set.

. Detection of aortic landmarks

Detection of anatomical landmarks (salient 3D points) is used to find out if and where a particular structure is located within the 3D image data. In particular the aortic landmarks are used to identify locations for diameter measurements.

. Aorta diameter measurements

Based on a segmented mask of the aorta and a series of detected aortic landmarks, the algorithm computes aortic landmarks according to guidelines of the American Heart Association (AHA)} provides the corresponding maximum aortic diameters.

. Threshold-based categorization of diameter measurements

The algorithm receives maximum aortic diameters at AHA-locations and applies the thresholds given in the AHA standard. The results are then labelled accordingly.

For each algorithm of AI-Rad Companion the analysis is structured as follows:

Algorithm Description: purpose, functionality, technical description
. Data
- Training cohort: size and properties of data used for training O
- Description of ground truth / annotations generation O
- Validation cohort: size and properties of data used for testing/validation O
Performance
- Choice of performance metric O
- Actual performance results O
- Assessment of clinical relevance of achieved performance O
Related clinical research, e.g. publications (if applicable)

The results of clinical data-based software validation for the subject device Al-Rad Companion (Cardiovascular) demonstrated equivalent performance in comparison to the primary predicate device. A complete scientific evaluation report is provided in support of the device modifications.

The performance of the AI-Rad Companion (Cardiovascular) device has been validated in retrospective performance studies on non-cardiac chest CT data from multiple clinical sites across the United States. With respect to the cardiac function, logarithmic correlation coefficient of total coronary calcium volume between subject and predicate device was 0.96 (N=381). With respect to the average absolute error in aorta diameters was 1.6 mm (95% confidence interval: [1.5 mm, 1.7 mm]) across all nine measurement locations and varied between 0.9 mm and 2.4 mm per location (N=193). Performance was consistent for all critical subgroups, such as vendors or slice thickness.

Summary

AI-Rad Companion (Cardiovascular) was tested and found to be safe and effective for intended users, uses and use environments through the design control verification process and clinical data-based software validation. The Human Factor Usability Validation showed that Human factors are addressed in

4 LF Hiratzka, GL Bakris, JA Beckman, et al., "2010 ACCF/AHA/AATS/ACR/SCA/SCA/SCA/SCA/SIR/STS/SVM guidelines for the diagnosis and management of patients with thoracic aortic disease", Circulation. 2010; 121:e266-e369

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the system test according to the operator's manual and in clinical use tests with customer report and feedback form. Customer employees are adequately trained in the use of this equipment.

VIII. General Safety and Effectiveness Concerns

The device labeling contains instructions for use as well as necessary cautions and warnings to provide for safe and effective use of the device. Risk management is ensured via a system related Risk analysis, which is used to identify potential hazards. These potential hazards are controlled during development, verification and validation testing according to the Risk Management process. In order to minimize electrical, mechanical, and radiation hazards, Siemens adheres to recognized and established industry practice and standards.

IX. Conclusions

AI-Rad Companion (Cardiovascular) has the same intended use as the predicate devices. The indication for use has been modified to include a more succinct summary of device specific performance but is still within the scope of the intended use and regulatory classification as the predicate devices. The fundamental technological characteristics such as image visualization and image manipulation are the same as the predicate device. The result of all testing conducted was found acceptable to support the claim of substantial equivalence. The predicate devices were cleared based on non-clinical supportive information including bench testing and software validations. The results of these tests demonstrate that the predicate devices are adequate for the intended use. The comparison of technological characteristics, non-clinical performance data, and software validation demonstrates that the subject device is as safe and effective when compared to the predicate device that is currently marketed for the same intended use. For the subject device, AI-Rad Companion (Cardiovascular), Siemens used the same testing with the same workflows as used to clear the predicate device to demonstrate safety and performance of the technical workflow. Clinical applicability was demonstrated via software-data based validations that were derived in the same intended environment as the predicate devices. Since both devices were tested using the same methods, Siemens believes that the data generated from the AI-Rad Companion (Cardiovascular) software testing supports a finding of substantial equivalence

Regulation Number and Section

§ 892.1750 Computed tomography x-ray system.

(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.