AI-Rad Companion (Pulmonary) 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 disease of the lungs. It provides the following functionality:

Segmentation and measurements of complete lung and lung lobes
Identification of areas with lower Hounsfield values in comparison to a predefined threshold for complete lung and lung lobes
Providing an interface to external Medical Device syngo.CT Lung CAD
Segmentation and measurements of found lung lesions and dedication to corresponding lung lobe.
The software has been validated for data from Siemens (filtered backprojection and iterative reconstruction), GE Healthcare (filtered backprojection reconstruction), and Philips (filtered backprojection reconstruction).
Only DICOM images of adult patients are considered to be valid input.

Device Description

AI-Rad Companion (Pulmonary) is a software only image processing application that supports quantitative and qualitative analysis of previously acquired CT DICOM Images to support radiologists and physicians from emergency medicine, specialty care, and general practice in the evaluation of and assessment of disease of the thorax.

AI/ML Overview

Here is a summary of the acceptance criteria and the study that proves the device meets them, based on the provided FDA document for Siemens AI-Rad Companion (Pulmonary):

1. Table of Acceptance Criteria & Reported Device Performance

The document doesn't explicitly state "acceptance criteria" as clear pass/fail thresholds for each metric. Instead, it describes validated performance results and claims they are "superior" to the predicate device, thereby supporting substantial equivalence. The key performance metrics are for lung lobe segmentation.

Feature/Metric	Acceptance Criteria (Implied/Compared)	Reported Device Performance
Lung Lobe Segmentation	Performance must be "superior" to the predicate device (syngo.CT Pulmo 3D). The specific quantitative thresholds for "superior" are not explicitly defined as acceptance criteria but are demonstrated by the comparative results below.	DICE Coefficients for individual lung lobes: - Ranged between 0.95 and 0.98. - Standard Deviation (SD) <= 0.07. Mean Surface Distance: - Ranged between 0.5 and 1.0 mm. - SD <= 1.5 mm. 95th percentile of Hausdorff Distance: - Ranged between 2.6 and 5.2 mm. - SD <= 6.7 mm. Volume Error: - Between 1.5 and 3.5 %. - SD <= 7.3 %. Overall: "All performance results were superior to the ones achieved using the predicate device."
Parenchyma Evaluation	Accurate calculation and visualization of lung tissue below a predefined threshold (-950 HU).	The algorithm receives a 3D CT data set and binary masks of the segmented lung lobes. It computes the ratio of voxels below -950 HU (%LAV950) for each lobe and for the complete lung. (No specific quantitative performance metrics provided for this function beyond its operation.)
Lesion Segmentation	Accurate segmentation and dedication to corresponding lung lobe.	Segmentation and measurements of identified lung lesions and dedication to corresponding lung lobe. (No specific quantitative performance metrics provided for this function beyond its operation.)

2. Sample Size and Data Provenance

Test Set Sample Size: "n > 4,500 CT data sets"
Data Provenance: Retrospective performance study from "multiple clinical sites from within and outside United States."

3. Number of Experts and Qualifications for Ground Truth

The document does not explicitly state the "number of experts" or their specific "qualifications" beyond mentioning "manually established ground truth." It implies that the ground truth was established by qualified professionals, likely radiologists or trained medical personnel, given the nature of the task (segmentation).

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1). It states "manually established ground truth," which typically implies consensus among multiple readers or a single highly experienced reader whose work is considered the gold standard within the study's context.

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

No evidence of a MRMC study. The document describes a standalone (algorithm only) performance study directly comparing the AI algorithm's output to "manually established ground truth" and claiming superiority over a predicate device's algorithm, not an AI-assisted human reader study. The purpose of this submission is to demonstrate substantial equivalence of the new AI-Rad Companion to existing predicate devices, not improvement in human reader performance.

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone study was done. The performance metrics (DICE coefficients, surface distance, Hausdorff distance, volume error) were computed by comparing the output of the algorithm to the manually established ground truth. This confirms it was an algorithm-only performance evaluation without human-in-the-loop.

7. Type of Ground Truth Used

Expert Consensus/Manual Establishment: The ground truth for the test set was "manually established ground truth." This typically refers to annotations or segmentations performed by human experts (e.g., radiologists) and potentially reviewed for consensus.

8. Sample Size for the Training Set

The document mentions a "Training cohort: size and properties of data used for training" as a structural element of their analysis but does not provide the specific sample size for the training set.

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

The document mentions "Description of ground truth / annotations generation" as a structural element for their algorithm analysis but does not detail how the ground truth for the training set was established. It can be inferred that it was likely generated through expert annotations, potentially similar to the test set, but specific methods (e.g., single expert, multiple experts, consensus, specific tools) are not described.

Summary

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July 26, 2019

Siemens Medical Solutions USA, Inc. Kimberly Rendon 40 Liberty Blvd. MALVERN, PA 19355

Re: K183271

Trade/Device Name: AI-Rad Companion (Pulmonary) Regulation Number: 21 CFR 892.1750 Regulation Name: Computed Tomography X-Ray System Regulatory Class: Class II Product Code: JAK, LLZ Dated: June 14, 2019 Received: June 17, 2019

Dear Kimberly 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 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-reporting

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combination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 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).

Sincerely,

For

Thalia 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) K183271

Device Name AI-Rad Companion (Pulmonary)

Indications for Use (Describe)

· Segmentation and measurements of complete lung and lung lobes
· Identification of areas with lower Hounsfield values in comparison to a predefined threshold for complete lung and lung lobes
· Providing an interface to external Medical Device syngo.CT Lung CAD
· Segmentation and measurements of found lung lesions and dedication to corresponding lung lobe.

The software has been validated for data from Siemens (filtered backprojection and iterative reconstruction), GE Healthcare (filtered backprojection reconstruction), and Philips (filtered backprojection reconstruction).

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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Image /page/3/Picture/0 description: The image shows the Siemens Healthineers logo. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. To the right of the word "Healthineers" is a cluster of orange dots.

510(K) SUMMARY AI-RAD COMPANION (PULMONARY) K183271

Submitted by: Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19355 Date Prepared: July 19, 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 Henkestrasse 127 91052 Erlangen, Germany Establishment Registration Number 3004977335

Contact Person

Kimberly Rendon Sr. Manager Regulatory Affairs (610) 448-6480 kimberly.rendon@siemens-healthineers.com

II. Device Name and Classification

Product Name:	AI-Rad Companion (Pulmonary)
Trade Name:	AI-Rad Companion (Pulmonary)
Classification Name:	Computed Tomography X-ray System
Secondary Classification Name:	Picture Archiving and Communication System
Classification Panel:	Radiology
CFR Section:	21 CFR §892.1750
Device Class:	Class II
Product Code:	JAK
Secondary Product Code:	LLZ
III. Predicate Device
Primary Predicate Device:
Product Name:	syngo.CT Pulmo 3D
Propriety Trade Name:	syngo CT Pulmo 3D

510(k) Number: Clearance Date: Classification Name: Secondary Classification Name: Classification Panel: CFR Section:

K123540 August 29, 2013 Computed Tomography X-Ray System Picture Archiving and Communications System Radiology 21 CFR §892.1750

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Health

Device Class: Class II Primary Product Code: JAK Secondary Product Code: LLZ Recall Information: There are currently no recalls for this device

Secondary Predicate Device:

Product Name:	syngo.PET&CT Oncology
Propriety Trade Name:	syngo.PET&CT Oncology
510(k) Number:	K093621
Clearance Date:	February 23, 2010
Classification Name:	Picture Archiving and Communications System
Classification Panel:	Radiology
CFR Section:	21 CFR §892.2050
Device Class:	Class II
Primary Product Code:	LLZ
Recall Information:	There are currently no recalls for this device

IV. Device Description

This section described the technical features and workflow for subject device Al-Rad Companion (Pulmonary). Al-Rad Companion (Pulmonary) is a software only image processing application that supports quantitative and qualitative analysis of previously acquired CT DICOM Images to support radiologists and physicians from emergency medicine, specialty care, and general practice in the evaluation of and assessment of disease of the thorax.

As an update to the previously cleared predicate devices, the following modifications have been made:

Software version VA10A, including the following features: 2)
- Segmentation of the lung (modified) a)
- Segmentation of lung lobes based on deep learning algorithm (modified) b)
- Parenchyma evaluation (modified) c)
- Lesion segmentation (modified) d)
1. Subject device claims list

The subject device AI-Rad Companion (Pulmonary) is an image processing software that provides quantitative and qualitative analysis from previously acquired Tomography DICOM images to support qualified clinicians in the evaluation and assessment of disease of the thorax. The subject device supports the following device specific functionality:

Segmentation and volume measurements of complete lung and lung lobes ●
. Identification of areas with lower Hounsfield values in comparison to a predefined threshold for complete lung and lung lobes
Detection of solid pulmonary nodules with the assistance of LungCAD (K143196, clearance date 05/12/2015) and dedication to lung lobe
. Segmentation and measurements of identified lung lesions

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Image /page/5/Picture/0 description: The image shows the logo for Siemens Healthineers. The word "SIEMENS" is written in teal, and the word "Healthineers" is written in orange below it. To the right of the words is a graphic of orange dots arranged in a circular pattern.

V. Indications for Use

AI-Rad Companion (Pulmonary) is image processing software that provides quantitative and qualitative analysis from previously acquired 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 lungs.

It provides the following functionality:

. Segmentation and measurements of complete lung and lung lobes
Identification of areas with lower Hounsfield values in comparison to a predefined threshold for complete lung and lung lobes
Providing an interface to external Medical Device syngo.CT Lung CAD
. Segmentation and measurements of found lung lesions and dedication to corresponding lung lobe.

The software has been validated for data from Siemens Healthineers (filtered backprojection and iterative reconstruction), GE Healthcare (filtered backprojection reconstruction), and Philips (filtered backprojection reconstruction).

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 comparable outputs in terms of lung and lung lobe visualization/segmentation and lung lesion segmentation and labeling. A tabular comparison of the subject device and predicate devices is provided as Table 1 below.

Feature	Subject Device	Predicate Device	Comparison Results
	SiemensAI-Rad Companion(Pulmonary)	Siemenssyngo.CT Pulmo 3D(K123540, clearancedate 8/29/2013)
Segmentation oflungs	Segmentation of lungs	Segmentation of left /right lung	Modifiedsubject device: segmentation ofcomplete lungspredicate device: dedicatedalgorithm for segmentation ofboth lungs
Segmentation oflung lobes	Segmentation of lunglobes	Segmentation of lungthirds, lung core/peel,lung lobes	Modifiedsubject device: deep learning-based algorithm for long lobessegmentationpredicate device: Model-basedsegmentation algorithm
ParenchymaEvaluation	Calculation andvisualization of lungtissue below -950 HU	Calculation andvisualization of lungtissue below threshold	ModifiedSubject device: fixed thresholdfor segmentationPredicate device: Configurablethreshold for segmentation

Table 1: Predicate Device Comparable Properties

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Image /page/6/Picture/0 description: The image shows the Siemens Healthineers logo. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. To the right of the word "Healthineers" is a graphic of orange dots arranged in a circular pattern.

Feature	Subject Device	Predicate Device	Comparison Results
Visualization of segmentation and parenchyma results	Siemens AI-Rad Companion (Pulmonary)	Siemens syngo.CT Pulmo 3D (K123540, clearance date 8/29/2013)	Same
Interface to LungCAD	Siemens AI-Rad Companion (Pulmonary)	Siemens syngo.PET&CT Oncology (K093621, clearance date 02/23/2010)	Same
Lesion Segmentation	Segmentation of lung lesions	Segmentation of lesions of the lung, liver, and lymph nodes	Modifiedsubject device: segmentation of lung lesions and localization of found lesion to lung lobepredicate device: segmentation of lesions of lung, liver, lymph nodes, and general anatomies
Visualization of lesion segmentation results	Color overlay of MPR and VRT with evaluation results	Color overlay of MPR and VRT with evaluation results	Same

The subject device modifications referenced above do not raise different questions of safety or effectiveness in comparison to the predicate devices.

VII. Performance Data

Non-Clinical Testing Summary

Performance tests were conducted to test the functionality of AI-Rad Companion (Pulmonary). 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. Al-Rad Companion has been tested to meet the requirements of conformity to multiple industry standards. Nonclinical performance testing demonstrated that AI-Rad Companion complies with the following voluntary FDA recognized Consensus Standards listed in Table 2 on the next page:

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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
5-95	General I(QS/RM)	Medical devices - Part 1: Application ofusability engineering to medical devicesIEC 62366-1:2015	06/27/2016	IEC

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 (Pulmonary) 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 (Pulmonary) clinical workflow, the following algorithms underwent a scientific evaluation:

Segmentation of lung lobes
- The lung lobe segmentation algorithm computes segmentation masks of the five lung lobes (right upper (RUL), right middle (RML), right lower (RLL), left upper (LUL) and left lower lobe (LLL) for a given CT data set of the chest.
. Evaluation of the lung parenchyma

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Image /page/8/Picture/0 description: The image shows the Siemens Healthineers logo. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. To the right of the word "Healthineers" is a cluster of orange dots.

The algorithm receives a 3D CT data set and binary masks of the segmented lung lobes. It computes, the ratio of voxels below -950 HU (%LAV950) each lobe as well as for the complete lung.

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 (Pulmonary) demonstrated superior performance in comparison to the primary predicate device for segmentation. A complete scientific evaluation report is provided in support of the device modifications.

Performance of lung lobe segmentation of AI-Rad Companion.Pulmonary device has been validated in a retrospective performance study (n>4,500 CT data sets from multiple clinical sites from within and outside United States). In this study DICE coefficients, surface metrics and volume error have been computed by comparing the output of the algorithm to the manually established ground truth. The average DICE coefficients for the individual lung lobes ranged between 0.95 and 0.98 with a standard deviation (SD) <= 0.07. Mean surface distance ranged between 0.5 and 1.0 mm with SD <=1.5 mm. The 95th quantile of the Hausdorff distance ranged between 2.6 and 5.2 mm with SD <=6.7 mm. Volume error was between 1.5 and 3.5 % with SD <= 7.3 %.

All performance results were superior to the ones achieved using the predicate device supporting substantial equivalence.

Additional analysis was performed for both population-specific subgroups and various technical parameters and consistent performance has been found across all subgroups.

Summary

AI-Rad Companion (Pulmonary) 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 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. Conclusion

AI-Rad Companion (Pulmonary) has the same intended use as the primary predicate device. 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 general intended use and regulatory classification as the predicate devices. The

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fundamental technological characteristics such as image visualization and image manipulation are the same as the predicate devices. The result of all testing conducted was found acceptable to support the claim of substantial equivalence. The predicate devices were cleared 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 (Pulmonary), 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 (Pulmonary) 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.