AI-Rad Companion (Pulmonary)

K233753 · Siemens Healthcare GmbH · JAK · Mar 21, 2024 · Radiology

Device Facts

• Record ID: K233753
• Device Name: AI-Rad Companion (Pulmonary)
• Applicant: Siemens Healthcare GmbH
• Product Code: JAK · Radiology
• Decision Date: Mar 21, 2024
• Decision: SESE
• Submission Type: Abbreviated
• Regulation: 21 CFR 892.1750
• Device Class: Class 2
• Attributes: AI/ML, Software as a Medical Device

Intended Use

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 specialty 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 solid and sub-solid lung nodules - Dedication of found lung nodules to corresponding lung lobe - Correlation of segmented lung nodules of current scan with known priors and quantitative assessment of changes of the correlated data. - Identification of areas with elevated Hounsfield values, where areas with elevated versus high opacities are distinquished.

Device Story

Software processes previously acquired CT DICOM images; utilizes machine/deep learning to segment lungs, lung lobes, and lung nodules (solid/sub-solid). Performs parenchyma evaluation (density/Hounsfield thresholds) and lesion follow-up by correlating current scans with priors. Used in clinical settings by radiologists and physicians; provides quantitative metrics (diameters, volume, doubling time) and visualizations (MPR/VRT overlays) to assist in disease assessment. Acts as an adjunct tool; does not replace clinical judgment. Deployed via cloud or edge (on-premise) platforms. Benefits include improved clinical utility and reduced imaging workflow complexity.

Clinical Evidence

No clinical trials conducted. Performance validated via bench testing and scientific evaluation of the lesion segmentation algorithm using 527 subjects (273 US, 254 Germany). Metrics included DICE coefficient, Bias, RMSE, and Limits of Agreement (LoA). Results demonstrated superior performance for sub-solid nodules compared to predicate solid nodule performance. Failure rate <1%. Data independence maintained between training and test sets.

Technological Characteristics

Software-based image processing; utilizes machine/deep learning algorithms. Inputs: CT DICOM images (adults). Outputs: Segmented masks, quantitative measurements, visualizations. Deployment: Cloud and edge (on-premise). Conformance: IEC 62366-1, ISO 14971, IEC 62304, DICOM PS 3.1-3.20, ISO 15223-1.

Indications for Use

Indicated for adult patients (≥22 years) for quantitative and qualitative analysis of previously acquired CT DICOM images to support radiologists and physicians in evaluating lung disease. Not for use in detecting pathologies outside the specified list.

Regulatory Classification

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.

Predicate Devices

• AI-Rad Companion (Pulmonary) (K213713)

Submission Summary (Full Text)

{0}------------------------------------------------

Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.

March 21, 2024

Siemens Healthcare GmbH c/o Kira Morales Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard MALVERN, PA 19335

Re: K233753

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, QIH Dated: February 20, 2024 Received: February 20, 2024

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/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.

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).

{1}------------------------------------------------

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 (QS) 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.

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 ([e-mail]) or phone [phone] or 301-796-7100).

Sincerely.

Lu Jiang

Lu Jiang, Ph.D. Assistant Director Diagnostic X-Ray Systems Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

{2}------------------------------------------------

### Indications for Use

Submission Number (if known)

K233753

Device Name

Al-Rad Companion (Pulmonary)

Indications for Use (Describe)

Al-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 specialty 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 solid and sub-solid lung nodules
- Dedication of found lung nodules to corresponding lung lobe
- Correlation of segmented lung nodules of current scan with known priors and quantitative assessment of changes of the correlated data.
- Identification of areas with elevated Hounsfield values, where areas with elevated versus high opacities are distinquished.

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.

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)

### CONTINUE ON A SEPARATE PAGE IF NEEDED.

This section applies only to requirements of the Paperwork Reduction Act of 1995.

### *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.*

The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:

> Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov

"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."

{3}------------------------------------------------

K233753

Image /page/3/Picture/1 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 text is a cluster of orange dots.

# 510(k) SUMMARY FOR AI-RAD COMPANION (Pulmonary) SW version VA40

Submitted by: Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19355 Date Prepared: March 18, 2024

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.<br>40 Liberty Boulevard<br>Malvern, PA 19355<br>Registration Number: 2240869 |
|----------------------|------------------------------------------------------------------------------------------------------------------|
| Manufacturing Site | Siemens Healthcare GmbH<br>Henkestrasse 127<br>Erlangen, Germany 91052<br>Registration Number: 3002808157 |

# 2. Contact Person

Kira Morales Regulatory Affairs Manager Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19335 Phone: [phone] - 9471 Email: [e-mail]

# 3. Device Name and Classification

| Product Name: | AI-Rad Companion (Pulmonary) |
|----------------------|----------------------------------|
| Trade Name: | AI-Rad Companion (Pulmonary) |
| Classification Name: | Computed Tomography X-Ray System |

{4}------------------------------------------------

Image /page/4/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. To the right of the words is a graphic of orange dots arranged in a circular pattern.

| Classification Panel: | Radiology |
|-------------------------|------------------|
| CFR Section: | 21 CFR §892.1750 |
| Secondary CFR Section: | 21 CFR §892.2050 |
| Device Class: | Class II |
| Product Code: | JAK |
| Secondary Product Code: | QIH |

# 4. Predicate Device

| Product Name: | AI-Rad Companion (Pulmonary) |
|-------------------------|----------------------------------|
| Propriety Trade Name: | AI-Rad Companion (Pulmonary) |
| 510(k) Number: | K213713 |
| Clearance Date: | August 11, 2022 |
| Classification Name: | Computed Tomography X-Ray System |
| Classification Panel: | Radiology |
| CFR Section: | 21 CFR §892.1750 |
| Secondary CFR Section: | 21 CFR §892.2050 |
| Device Class: | Class II |
| Primary Product Code: | JAK |
| Secondary Product Code: | QIH |
| Recall Information: | N/A |

# 5. Indications for Use

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 specialty 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 solid and sub-solid lung nodules ●
- Dedication of found lung nodules to corresponding lung lobe ●
- . Correlation of segmented lung nodules of current scan with known priors and quantitative assessment of changes of the correlated data.
- Identification of areas with elevated Hounsfield values, where areas with elevated versus high opacities are distinguished.

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).

{5}------------------------------------------------

Image /page/5/Picture/0 description: The image contains 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.

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

### Modified Indications for Use Assessment

The Indications for Use for AI-Rad Companion (Pulmonary) VA40 was updated to introduce the sub-solid lung nodule segmentation feature compared to the predicate. Additionally, the removal of the intended users "emergency medicine" and "urgent care" physicians was a clarification to further align with the intended user profiles and the risk analysis. The Indications for Use statement has been modified to provide a more accurate description of the extension's functionality but does not represent a new intended use in comparison to the predicate.

# 6. Device Description

The subject device AI-Rad Companion (Pulmonary) is an image processing software that utilizes machine learning and deep learning algorithms to provide quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images to support qualified clinicians in the evaluation and assessment of disease of the thorax. AI-Rad Companion (Pulmonary) builds on platform functionality provided by the AI-Rad Companion Engine and cloud/edge functionality provided by the Siemens Healthineers teamplay digital platform. AI-Rad Companion (Pulmonary) is an adjunct tool and does not replace the role of a qualified medical professional. AI-Rad Companion (Pulmonary) is also not designed to detect the presence of radiographic findings other than the prespecified list. Qualified medical professionals should review original images for all suspected pathologies.

AI-Rad Companion (Pulmonary) offers:

- Segmentation of lungs, ●
- Segmentation of lung lobes.
- Parenchyma evaluation, ●
- Parenchyma ranges,
- Pulmonary density,
- Visualization of segmentation and parenchyma results,
- Interface to LungCAD,
- Lesion segmentation, ●
- Visualization of lesion segmentation results, ●
- Lesion follow-up

AI-Rad Companion (Pulmonary) requires images of patients of 22 years and older.

AI-Rad Companion (Pulmonary) SW version VA40 is an enhancement to the previously cleared device AI-Rad Companion (Pulmonary) (K213713) that utilizes machine and deep learning algorithms to provide quantitative and qualitative analysis to computed tomography DICOM images to support qualified clinicians in the evaluation and assessment of disease of the thorax.

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

- Sub-solid Lung Nodule Segmentation ●

{6}------------------------------------------------

Image /page/6/Picture/0 description: The image contains 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 word "Healthineers" is a graphic of orange dots arranged in a circular pattern.

This feature provides the ability to segment and measure all subtypes of lesions including solid and sub-solid lesions.

- . Modified Indications for Use Statement The indications for use statement was updated to include descriptive text for sub-solid lung nodule addition.
- Updated Subject Device Claims List The claims list was updated to reflect the new device functionality
- . Updated Limitations for Use Additional limitations for use has been added to the subject device.

# 7. Technological Characteristics

The comparison between the above referenced predicate device are listed at a high-level in the following table.

| Feature | Subject Device<br>AI-Rad Companion (Pulmonary)<br>VA40 | Predicate Device<br>AI-Rad Companion (Pulmonary)<br>(K213713) | Comparison<br>Results |
|---------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------|
| Modality | CT | CT | Identical |
| Segmentation of<br>lungs | Creation of a lung segmentation<br>mask by combining the<br>segmentation masks of 5 lung<br>lobes. | Creation of a lung segmentation<br>mask by combining the<br>segmentation masks of 5 lung<br>lobes. | Identical |
| Segmentation of<br>lung lobes | Computation of segmentation<br>masks of the five lung lobes (right<br>upper (RUL), right middle (RML),<br>right lower (RLL), left upper (LUL)<br>and left lower (LLL) lobe) for a<br>given CT data set of the chest. | Computation of segmentation<br>masks of the five lung lobes (right<br>upper (RUL), right middle (RML),<br>right lower (RLL), left upper (LUL)<br>and left lower (LLL) lobe) for a<br>given CT data set of the chest. | Identical |
| Parenchyma<br>evaluation | The parenchyma evaluation uses<br>the lobe mask, counts all voxels per<br>lobe, counts image voxels below -<br>950 HU, and calculates the<br>percentages of these voxels relative<br>to the total number of voxels.<br>Additionally, it sums the individual<br>lobe results and calculates the<br>percentage for the complete lung. | The parenchyma evaluation uses<br>the lobe mask, counts all voxels per<br>lobe, counts image voxels below -<br>950 HU, and calculates the<br>percentages of these voxels relative<br>to the total number of voxels.<br>Additionally, it sums the individual<br>lobe results and calculates the<br>percentage for the complete lung. | Identical |
| Parenchyma<br>Ranges | The percentages are likewise<br>dedicated to the 4 ranges. Name of<br>ranges and their ranges are<br>configurable by the user. | The percentages are likewise<br>dedicated to the 4 ranges. Name of<br>ranges and their ranges are<br>configurable by the user. | Identical |
| Pulmonary<br>Density | AI-based identification of areas<br>with elevated Hounsfield values.<br>Threshold-based identification of<br>highest elevated Hounsfield values<br>inside these elevated regions, by a<br>predefined threshold of -200 HU | AI-based identification of areas<br>with elevated Hounsfield values.<br>Threshold-based identification of<br>highest elevated Hounsfield values<br>inside these elevated regions, by a<br>predefined threshold of -200 HU | Identical |
| Visualization of<br>segmentation<br>and parenchyma<br>results | Color overlay of MPR and VRT<br>with evaluation results | Color overlay of MPR and VRT<br>with evaluation results | Identical |
| Interface to<br>LungCAD | Interface to syngo.CT LungCAD | Interface to syngo.CT LungCAD | Identical |
| Lesion<br>segmentation | Segmentation of solid and sub-solid<br>lung lesions including the following<br>data:<br>Relative change of<br>maximum 2D diameter [%] relative change of<br>maximum orthogonal 2D<br>diameter [%] relative change of mean 2D<br>diameter [%], relative change of<br>maximum 3D diameter [%] Relative change of volume<br>(volume doubling time [d],<br>negative growth [%]) | Segmentation of lung lesions<br>including the following data:<br>Relative change of<br>maximum 2D diameter [%] relative change of<br>maximum orthogonal 2D<br>diameter [%] relative change of mean 2D<br>diameter [%], relative change of<br>maximum 3D diameter [%] Relative change of volume<br>(volume doubling time [d],<br>negative growth [%]) | Enhanced -<br>addition of<br>sub-solid<br>lesion<br>segmentation |
| Visualization of<br>lesion<br>segmentation<br>results | Color overlay of MPR and VRT<br>with evaluation | Color overlay of MPR and VRT<br>with evaluation | Identical |
| Lesion follow-<br>up | Correlation of segmented lung<br>lesions with known priors using the<br>data from the lesion segmentation. | Correlation of segmented lung<br>lesions with known priors using the<br>data from the lesion segmentation. | Identical |
| Deployment | Cloud and Edge (on-premise)<br>deployments | Cloud and Edge (on-premise)<br>deployments | Identical |

{7}------------------------------------------------

Image /page/7/Picture/0 description: The image contains 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 cluster of orange dots.

Table 1: Technological Comparisons

# 8. Nonclinical Tests

Non-clinical tests were conducted to test the functionality of AI-Rad Companion (Pulmonary). 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.

{8}------------------------------------------------

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

AI-Rad Companion has been tested to meet the requirements of conformity to multiple industry standards. Non-clinical performance testing demonstrates that AI-Rad Companion (Pulmonary) complies with the FDA guidance document, "Guidance for the Content of Premarket Submissions for Device Software Functions" (June 14, 2023) as well as with the following voluntary FDA recognized Consensus Standards listed in Table 1 below.

| Recognition<br>Number | Product<br>Area | Title of Standard | Reference<br>Number and<br>Date | Standards<br>Development<br>Organization |
|-----------------------|--------------------------|---------------------------------------------------------------------------------------------------------------------|----------------------------------------------|------------------------------------------|
| 5-129 | General | Medical Devices – Application<br>of usability engineering to<br>medical devices [including<br>Corrigendum 1 (2016)] | 62366-1:<br>2020-06 | IEC |
| 5-125 | General | Medical Devices – application<br>of risk management to medical<br>devices | 14971 Third<br>Edition 2019-<br>12 | ISO |
| 13-79 | Software/<br>Informatics | Medical device software -<br>software life cycle processes<br>[Including Amendment 1<br>(2016)] | 62304 Edition<br>1.1 2015-06<br>Consolidated | IEC |
| 12-349 | Radiology | Digital Imaging and<br>Communications in Medicine<br>(DICOM) Set | PS 3.1 - 3.20<br>2022d | NEMA |
| 5-134 | Radiology | Medical devices – symbols to<br>be used with information to be<br>supplied by the manufacturer | 15223-1<br>Fourth<br>Edition 2021-<br>07 | ISO |

Table 2: Voluntary Conformance Standards

### Verification and Validation

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 "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions" (September 27, 2023) 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.

{9}------------------------------------------------

Image /page/9/Picture/0 description: The image shows the logo for Siemens Healthineers. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. To the right of the words is a graphic of orange dots.

# 9. Performance Software Validation

To validate AI-Rad Companion (Pulmonary) VA40 software from a clinical perspective, the lesion segmentation algorithm underwent a scientific evaluation. The results of clinical databased software validation for the subject device, AI-Rad Companion (Pulmonary)VA40, demonstrated substantially equivalent performance in comparison to the predicate device. The performance of the sub-solid lesion segmentation algorithm was analyzed against the predicate's performance of solid nodule segmentation. The additional features of AI-Rad Companion (Pulmonary) were unchanged from the predicate and did not undergo a new scientific evaluation.

Performance testing for AI-Rad Companion (Pulmonary) lesion segmentation algorithm was performed on multiple vendor test data from 273 subjects in the United States and 254 subjects in Germany.

| Validation Type | Target |
|-------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Failure Rate | < 1% |
| Accuracy for Solid & Calcified<br>nodules | Average DICE ≥ predicate<br>Bias & RMSE ≤ predicate<br>LoA in line with predicate LoA |
| Accuracy for sub-solid nodules | Performance was analyzed in relation to the predicate's<br>solid nodule segmentation performance<br><br>Median nodule size range for sub-solid is 10mm-20mm<br><br>LoA for 10mm-20mm ≥ 95% for all three diameter<br>metrics |
| DICE score | Average DICE score for sub-solid nodules > average<br>DICE for predicate solid nodules |
| Consistency of Subgroup results | Average DICE not smaller than DICE of overall cohort<br>minus 1 STD<br>Bias of three metrics not exceed ±1 STD<br>RMSE of three metrics not exceed RMSE of overall<br>cohort +1 STD each |

#### Acceptance Criteria:

Table 3: Acceptance Criteria for Subject Device Performance

#### Testing Data Information:

| Category | Frequency |
|--------------|-----------------------------------------------------------|
| Manufacturer | Canon/Toshiba: 18<br>GE: 35<br>Philips: 15<br>Siemens: 32 |

{10}------------------------------------------------

Image /page/10/Picture/0 description: The image shows the logo for Siemens Healthineers. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. To the right of the words is a graphic of orange dots arranged in a circular pattern.

| Data Origin | US: 69<br>Germany: 31 |
|------------------------|---------------------------------------------------------------------|
| Dose | Low: 31<br>Conventional: 69 |
| Contrast Enhancement | Contrasted: 47<br>Native: 53 |
| Slice Thickness [mm] | ≤1.25: 37<br>(1.25-2]: 25<br>(2-3]: 38 |
| Age group [years] | [21-40): 17<br>[40-60): 28<br>[60-70): 24<br>[70-80): 21<br>≥80: 10 |
| Nodule type | Solid: 90<br>Calcified: 12<br>Sub-solid: 98 |
| Nodule Size Range [mm] | [3-6): 25<br>[6-10): 22<br>[10-20): 33<br>[20-30]: 7 |
| Patient Sex | Male: 47<br>Female: 53 |

Table 4: Testing Data Characteristics for subject device

### Testing Summary

AI-Rad Companion (Pulmonary) VA40 performed significantly better than the predicate device (K213713). For the existing solid and calcified nodule types and sizes, the average DICE coefficient was greater than that of the predicate. For sub-solid nodules, the average DICE coefficient of the subject device was superior to the average DICE coefficient of the predicate device for solid nodules. The subject device also met its individual subgroup analysis acceptance criterion for all subgroups.

### Standard Annotation Process:

For the testing data the ground truth annotations were established independently by two board certified radiologists (10 and 7 years of experience, respectively). In case of disagreement a third radiologist (9 years of experience) served as an adjudicator.

### Testing & Training Data Independence:

None of the clinical sites providing the test data provided data for training of any of the algorithms. Therefore there is a clear independence on site level between training and test data.

# 10. Summary of Nonclinical Tests

{11}------------------------------------------------

### SIEME Healthineers

Based on the nonclinical performance documented within the Scientific Evaluation, AI-Rad Companion (Pulmonary) VA40 was found to substantially equivalent 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 substantial equivalence of the subject device in that it performs as well as or better than to the predicate device that is currently marketed.

# 11. Summary Clinical Tests

The predicate (K213713) 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 (Pulmonary). Verification and validation of the enhancements and improvements have been performed and these modifications have been validated for their intended use. The data from these activities were used to support the subject device and the substantial equivalence argument. No animal testing has been performed on the subject device.

# 12. 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 CT images.

# 13. Substantial Equivalence and Conclusion

AI-Rad Companion (Pulmonary) is substantially equivalent to the follow predicate device (Table 5):

| Predicate Device | FDA Clearance<br>Number | FDA Clearance<br>Date | Main Product Code |
|---------------------------------|-------------------------|-----------------------|-------------------|
| AI-Rad Companion<br>(Pulmonary) | K213713 | August 11, 2022 | JAK |

Table 5: Predicate device for AI-Rad Companion (Pulmonary)

AI-Rad Companion (Pulmonary) has the same intended use and similar technical characteristics compared to the predicate device, AI-Rad Companion (Pulmonary) [K213713], with respect to the software features, functionalities and core algorithms. The enhancements and improvements provided in AI-Rad Companion (Pulmonary) increase the clinical utility and reduce the complexity of the imaging workflow for the clinical user. 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.