(120 days)
Yes
The device description explicitly states that the software "utilizes machine learning and deep learning algorithms".
No.
This device is image processing software designed to support radiologists and physicians in the evaluation and assessment of lung disease by providing quantitative and qualitative analysis of CT images. It functions as an adjunct tool for diagnosis and analysis, not directly for treating or preventing disease.
Yes
The "Intended Use / Indications for Use" states that the device provides quantitative and qualitative analysis to support radiologists and physicians "in the evaluation and assessment of disease of the lungs," which is a diagnostic purpose. The device performs segmentation and measurements of lung features and nodules and identifies areas with abnormal Hounsfield values, all of which are activities associated with diagnosing disease.
Yes
The device is described as "image processing software" and its functionality is solely based on analyzing previously acquired CT DICOM images. There is no mention of any hardware component being part of the device itself.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVD Definition: In Vitro Diagnostics are medical devices used to perform tests on samples taken from the human body, such as blood, urine, or tissue, to provide information about a person's health.
- Device Function: The AI-Rad Companion (Pulmonary) is described as image processing software that analyzes Computed Tomography (CT) DICOM images. It works with previously acquired medical images, not biological samples.
- Intended Use: The intended use is to support clinicians in the evaluation and assessment of lung disease by providing quantitative and qualitative analysis of these images. This is a function related to medical imaging interpretation, not laboratory testing of biological specimens.
Therefore, the device falls under the category of medical imaging software or a medical image analysis device, not an In Vitro Diagnostic.
No
The letter explicitly states "Control Plan Authorized (PCCP) and relevant text: Not Found", which does not indicate that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / 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 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.
Product codes
JAK, QIH
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 ●
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.
Mentions image processing
Yes
Mentions AI, DNN, or ML
AI-Rad Companion (Pulmonary) is an image processing software that utilizes machine learning and deep learning algorithms
AI-based identification of areas with elevated Hounsfield values.
Input Imaging Modality
Computed Tomography DICOM images
Anatomical Site
lungs (pulmonary), thorax
Indicated Patient Age Range
adult patients, 22 years and older
Intended User / Care Setting
radiologists and physicians from specialty care and general practice
healthcare professionals familiar with the post processing of CT images.
Description of the training set, sample size, data source, and annotation protocol
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.
Description of the test set, sample size, data source, and annotation protocol
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.
Manufacturer:
Canon/Toshiba: 18
GE: 35
Philips: 15
Siemens: 32
Data Origin:
US: 69
Germany: 31
Dose:
Low: 31
Conventional: 69
Contrast Enhancement:
Contrasted: 47
Native: 53
Slice Thickness [mm]:
≤1.25: 37
(1.25-2]: 25
(2-3]: 38
Age group [years]:
[21-40): 17
[40-60): 28
[60-70): 24
[70-80): 21
≥80: 10
Nodule type:
Solid: 90
Calcified: 12
Sub-solid: 98
Nodule Size Range [mm]:
[3-6): 25
[6-10): 22
[10-20): 33
[20-30]: 7
Patient Sex:
Male: 47
Female: 53
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.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Performance Software Validation
The lesion segmentation algorithm underwent a 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.
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.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Failure Rate: average DICE for predicate solid nodules
Consistency of Subgroup results: Average DICE not smaller than DICE of overall cohort minus 1 STD. Bias of three metrics not exceed ±1 STD. RMSE of three metrics not exceed RMSE of overall cohort +1 STD each.
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 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.
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 (DICE@fda.hhs.gov) or phone (1-800-638-2041 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)
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)
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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.
40 Liberty Boulevard
Malvern, PA 19355
Registration Number: 2240869 |
|----------------------|------------------------------------------------------------------------------------------------------------------|
| Manufacturing Site | Siemens Healthcare GmbH
Henkestrasse 127
Erlangen, Germany 91052
Registration Number: 3002808157 |
2. Contact Person
Kira Morales Regulatory Affairs Manager Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19335 Phone: +1 (484) 901 - 9471 Email: kira.morales@siemens-healthineers.com
3. Device Name and Classification
| Product Name: | AI-Rad Companion (Pulmonary) |
|---|---|
| Trade Name: | AI-Rad Companion (Pulmonary) |
| Classification Name: | Computed Tomography X-Ray System |
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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).
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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 ●
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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
AI-Rad Companion (Pulmonary)
VA40 | Predicate Device
AI-Rad Companion (Pulmonary)
(K213713) | Comparison
Results |
|---------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------|
| Modality | CT | CT | Identical |
| Segmentation of
lungs | Creation of a lung segmentation
mask by combining the
segmentation masks of 5 lung
lobes. | Creation of a lung segmentation
mask by combining the
segmentation masks of 5 lung
lobes. | Identical |
| Segmentation of
lung lobes | Computation of segmentation
masks of the five lung lobes (right
upper (RUL), right middle (RML),
right lower (RLL), left upper (LUL)
and left lower (LLL) lobe) for a
given CT data set of the chest. | Computation of segmentation
masks of the five lung lobes (right
upper (RUL), right middle (RML),
right lower (RLL), left upper (LUL)
and left lower (LLL) lobe) for a
given CT data set of the chest. | Identical |
| Parenchyma
evaluation | The parenchyma evaluation uses
the lobe mask, counts all voxels per
lobe, counts image voxels below -
950 HU, and calculates the
percentages of these voxels relative
to the total number of voxels.
Additionally, it sums the individual
lobe results and calculates the
percentage for the complete lung. | The parenchyma evaluation uses
the lobe mask, counts all voxels per
lobe, counts image voxels below -
950 HU, and calculates the
percentages of these voxels relative
to the total number of voxels.
Additionally, it sums the individual
lobe results and calculates the
percentage for the complete lung. | Identical |
| Parenchyma
Ranges | The percentages are likewise
dedicated to the 4 ranges. Name of
ranges and their ranges are
configurable by the user. | The percentages are likewise
dedicated to the 4 ranges. Name of
ranges and their ranges are
configurable by the user. | Identical |
| Pulmonary
Density | AI-based identification of areas
with elevated Hounsfield values.
Threshold-based identification of
highest elevated Hounsfield values
inside these elevated regions, by a
predefined threshold of -200 HU | AI-based identification of areas
with elevated Hounsfield values.
Threshold-based identification of
highest elevated Hounsfield values
inside these elevated regions, by a
predefined threshold of -200 HU | Identical |
| Visualization of
segmentation
and parenchyma
results | Color overlay of MPR and VRT
with evaluation results | Color overlay of MPR and VRT
with evaluation results | Identical |
| Interface to
LungCAD | Interface to syngo.CT LungCAD | Interface to syngo.CT LungCAD | Identical |
| Lesion
segmentation | Segmentation of solid and sub-solid
lung lesions including the following
data:
Relative change of
maximum 2D diameter [%] relative change of
maximum orthogonal 2D
diameter [%] relative change of mean 2D
diameter [%], relative change of
maximum 3D diameter [%] Relative change of volume
(volume doubling time [d],
negative growth [%]) | Segmentation of lung lesions
including the following data:
Relative change of
maximum 2D diameter [%] relative change of
maximum orthogonal 2D
diameter [%] relative change of mean 2D
diameter [%], relative change of
maximum 3D diameter [%] Relative change of volume
(volume doubling time [d],
negative growth [%]) | Enhanced -
addition of
sub-solid
lesion
segmentation |
| Visualization of
lesion
segmentation
results | Color overlay of MPR and VRT
with evaluation | Color overlay of MPR and VRT
with evaluation | Identical |
| Lesion follow-
up | Correlation of segmented lung
lesions with known priors using the
data from the lesion segmentation. | Correlation of segmented lung
lesions with known priors using the
data from the lesion segmentation. | Identical |
| Deployment | Cloud and Edge (on-premise)
deployments | Cloud and Edge (on-premise)
deployments | Identical |
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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.
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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
Number | Product
Area | Title of Standard | Reference
Number and
Date | Standards
Development
Organization |
|-----------------------|--------------------------|---------------------------------------------------------------------------------------------------------------------|----------------------------------------------|------------------------------------------|
| 5-129 | General | Medical Devices – Application
of usability engineering to
medical devices [including
Corrigendum 1 (2016)] | 62366-1:
2020-06 | IEC |
| 5-125 | General | Medical Devices – application
of risk management to medical
devices | 14971 Third
Edition 2019-
12 | ISO |
| 13-79 | Software/
Informatics | Medical device software -
software life cycle processes
[Including Amendment 1
(2016)] | 62304 Edition
1.1 2015-06
Consolidated | IEC |
| 12-349 | Radiology | Digital Imaging and
Communications in Medicine
(DICOM) Set | PS 3.1 - 3.20
2022d | NEMA |
| 5-134 | Radiology | Medical devices – symbols to
be used with information to be
supplied by the manufacturer | 15223-1
Fourth
Edition 2021-
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.
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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 | average |
| DICE for predicate solid nodules | |
| Consistency of Subgroup results | Average DICE not smaller than DICE of overall cohort |
| minus 1 STD | |
| Bias of three metrics not exceed ±1 STD | |
| RMSE of three metrics not exceed RMSE of overall | |
| cohort +1 STD each |
Acceptance Criteria:
Table 3: Acceptance Criteria for Subject Device Performance
Testing Data Information:
| Category | Frequency |
|---|---|
| Manufacturer | Canon/Toshiba: 18 |
| GE: 35 | |
| Philips: 15 | |
| Siemens: 32 |
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| Data Origin | US: 69
Germany: 31 |
|------------------------|---------------------------------------------------------------------|
| Dose | Low: 31
Conventional: 69 |
| Contrast Enhancement | Contrasted: 47
Native: 53 |
| Slice Thickness [mm] | ≤1.25: 37
(1.25-2]: 25
(2-3]: 38 |
| Age group [years] | [21-40): 17
[40-60): 28
[60-70): 24
[70-80): 21
≥80: 10 |
| Nodule type | Solid: 90
Calcified: 12
Sub-solid: 98 |
| Nodule Size Range [mm] | [3-6): 25
[6-10): 22
[10-20): 33
[20-30]: 7 |
| Patient Sex | Male: 47
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
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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
Number | FDA Clearance
Date | Main Product Code |
|---------------------------------|-------------------------|-----------------------|-------------------|
| AI-Rad Companion
(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.