(111 days)
Yes
The device description explicitly states that it uses "deep learning algorithms" and a "3D Deep Image-to-Image network" for image post-processing and segmentation, which are forms of AI/ML.
No.
The device is an image processing software that provides quantitative and qualitative analysis to support diagnosis and assessment of musculoskeletal disease, but it does not directly treat or prevent a disease.
Yes.
The device's intended use is to "support radiologists and physicians... in the evaluation and assessment of musculoskeletal disease," and it provides "quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images," which directly contributes to diagnosis.
Yes
The device description explicitly states that Al-Rad Companion (Musculoskeletal) is a "software-only image post-processing application".
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs are used to examine specimens derived from the human body. The intended use and device description clearly state that AI-Rad Companion (Musculoskeletal) processes previously acquired Computed Tomography (CT) DICOM images. These are medical images, not biological specimens like blood, urine, or tissue.
- The functionality described is image processing and analysis. The software performs tasks like segmentation, labeling, and measurements on the CT images. This is distinct from the types of tests performed by IVDs, which typically involve chemical, biological, or immunological analysis of specimens.
The device is an image processing software that aids in the interpretation of medical images, which falls under the category of medical devices, but not specifically In Vitro Diagnostics.
No
The letter does not state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
AI-Rad Companion (Musculoskeletal) is an 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 musculoskeletal disease.
It provides the following functionality:
- Segmentation of vertebras
- Labelling of vertebras
- Measurements of heights in each vertebra and indication if they are critically different
- Measurement of mean Hounsfield value in volume of interest within vertebra.
Only DICOM images of adult patients are considered to be valid input.
Product codes (comma separated list FDA assigned to the subject device)
JAK
Device Description
Al-Rad Companion (Musculoskeletal) is software-only image post-processing application that uses deep learning algorithms to post-process CT data of the thorax. Al-Rad Companion (Musculoskeletal) supports workflows for visualization and various measurements of musculoskeletal disease, including:
- Segmentation of vertebras
- Labelling of vertebras
- Measurements of heights in each vertebra and indication if they are critically different
- Measurement of mean Hounsfield value in volume of interest within vertebra
As an update to the previously cleared predicate device, the following modifications have been made:
-
- Modified Indication for Use Statement
-
- Support of software Al-Rad Companion VA10
- a. Detection of vertebras (identical)
- b. Labelling of Vertebras (identical)
- c. Segmentation, Size/HU measurement (modified)
- d. Vertebra categories (modified)
-
- Subject device claims list
Al-Rad Companion (Musculoskeletal) uses the same deep learning technology as in the previously cleared reference device Siemens Al-Rad Companion (Cardiovascular) (K183268). More precisely, a 3D Deep Image-to-Image network is used for organ segmentation. The main structure of the network is designed following a symmetric way as a convolutional encoder-decoder. All blocks of the network consist of 3D convolutional and bilinear upscaling layers.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Computed Tomography
Anatomical Site
Musculoskeletal (specifically vertebras)
Indicated Patient Age Range
Adult patients
Intended User / Care Setting
Radiologists and physicians from emergency medicine, specialty care, urgent care, and general practice.
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
The performance of the Al-Rad Companion (Musculoskeletal) device has been validated in a retrospective performance study on chest CT data (N=140, data from multiple clinical sites across the United States and Europe). Ground truth annotations were established using manual vertebra height and density measurements performed by four radiologists (two readers per case plus a third reader for adjudications).
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
The performance of the Al-Rad Companion (Musculoskeletal) device has been validated in a retrospective performance study on chest CT data (N=140, data from multiple clinical sites across the United States and Europe). Ground truth annotations were established using manual vertebra height and density measurements performed by four radiologists (two readers per case plus a third reader for adjudications). Inter-reader-variability, i.e. the 95%limits of agreement (LoA) of the measurements performed by the radiologists, was assessed. The ratio of vertebra height measurements generated by the subject device lying within the LoA around the ground truth was 95.1% for thin slices (≤1 mm slice thickness) and 87.5% for thicker slices (>1 mm slice thickness). Analogously the ratio of vertebra density measurements lying within the LoA was 98.8%. Performance was consistent for all critical subgroups, such as vendors or reconstruction parameters and patient age.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
The ratio of vertebra height measurements generated by the subject device lying within the LoA around the ground truth was 95.1% for thin slices (≤1 mm slice thickness) and 87.5% for thicker slices (>1 mm slice thickness). Analogously the ratio of vertebra density measurements lying within the LoA was 98.8%.
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
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
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Siemens Medical Solutions USA, Inc. % M. Alaine Medio Regulatory Affairs Professional 810 Innovation Drive KNOXVILLE TN 37932
March 16, 2020
Re: K193267
Trade/Device Name: Al-Rad Companion (Musculoskeletal) Regulation Number: 21 CFR 892.1750 Regulation Name: Computed tomography x-ray system Regulatory Class: Class II Product Code: JAK Dated: February 20, 2020 Received: February 21, 2020
Dear M. Alaine Medio:
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/cfpmp/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
1
https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about 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 T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
510(k) Number (if known)
Device Name AI-Rad Companion (Musculoskeletal)
Indications for Use (Describe)
AI-Rad Companion (Musculoskeletal) is an image processing software that provides quantitative andysis 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 musculoskeletal disease. It provides the following functionality:
- · Segmentation of vertebras
- · Labelling of vertebras
- Measurements of heights in each vertebra and indication if they are critically different
- · Measurement of mean Hounsfield value in volume of interest within vertebra.
Only DICOM images of adult patients are considered to be valid input
| Type of Use (Select one or both, as applicable) | |
|---|---|
| ------------------------------------------------- | -- |
X Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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3
510(k) Summary for Al-Rad Companion (Musculoskeletal) K193267
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.
Identification of the Submitter
| Manufacturer: | Siemens Healthcare GmbH
Siemensstr. 1
D-91301 Forchheim, Germany
Establishment Registration Number
3004977335 |
|-------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|
| Importer / Distributor: | Siemens Medical Solutions USA, Inc.
40 Liberty Boulevard
Malvern, PA 19355
Establishment Registration Number
2240869 |
| Submitter: | M. Alaine Medio, RAC
Regulatory Affairs
Siemens Medical Solutions USA, Inc.
Molecular Imaging
810 Innovation Drive
Knoxville, TN 37932 |
| Alternative Contact: | Tabitha Estes
Regulatory Affairs |
| Telephone Number: | (865)206-0337 |
| Fax Number: | (865)218-3019 |
| Date of Submission: | November 25, 2019 |
Identification of the product
| Device Proprietary Name: | Al-Rad Companion (Musculoskeletal) |
|---|---|
| Common Name: | Al-Rad Companion (Musculoskeletal) |
| Classification Name: | Computed Tomography X-ray System |
| Regulation: | 21 CFR 892.1750 |
| Product Code: | JAK |
| Classification Panel: | Radiology |
| Device Class: | Class II |
Marketed Devices to which Equivalence is claimed
Predicate Device:
| Device Proprietary Name: | syngo.CT Bone Reading |
|---|---|
| Manufacturer: | Siemens Healthcare GmbH |
| Classification Name: | Computed Tomography X-ray System |
| Regulation: | 21 CFR 892.1750 |
| Product Code: | JAK |
| Classification Panel: | Radiology |
| Device Class: | Class II |
| 510(k) Number: | K123584 cleared March 12, 2013 |
4
Reference Device:
| Device Proprietary Name: | Al-Rad Companion (Cardiovascular) |
|---|---|
| Manufacturer: | Siemens Healthcare GmbH |
| Classification Name: | Computed Tomography X-ray System |
| Regulation: | 21 CFR 892.1750 |
| Product Code: | JAK |
| Subsequent Product Code | LLZ |
| Classification Panel: | Radiology |
| Device Class: | Class II |
| 510(k) Number: | K183268 cleared October 09, 2019 |
Device Description
Al-Rad Companion (Musculoskeletal) is software-only image post-processing application that uses deep learning algorithms to post-process CT data of the thorax. Al-Rad Companion (Musculoskeletal) supports workflows for visualization and various measurements of musculoskeletal disease, including:
- Segmentation of vertebras ●
- Labelling of vertebras
- Measurements of heights in each vertebra and indication if they are critically ● different
- . Measurement of mean Hounsfield value in volume of interest within vertebra
As an update to the previously cleared predicate device, the following modifications have been made:
-
- Modified Indication for Use Statement
-
- Support of software Al-Rad Companion VA10
- a. Detection of vertebras (identical)
- b. Labelling of Vertebras (identical)
- c. Segmentation, Size/HU measurement (modified)
- d. Vertebra categories (modified)
-
- Subject device claims list
Al-Rad Companion (Musculoskeletal) uses the same deep learning technology as in the previously cleared reference device Siemens Al-Rad Companion (Cardiovascular) (K183268). More precisely, a 3D Deep Image-to-Image network is used for organ segmentation. The main structure of the network is designed following a symmetric way as a convolutional encoder-decoder. All blocks of the network consist of 3D convolutional and bilinear upscaling layers.
Indications for Use
Al-Rad Companion (Musculoskeletal) is an 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 musculoskeletal disease.
It provides the following functionality:
- . Segmentation of vertebras
- Labelling of vertebras
- . Measurements of heights in each vertebra and indication if they are critically different
5
- Measurement of mean Hounsfield value in volume of interest within vertebra.
Only DICOM images of adult patients are considered to be valid input.
| Subject Device | Predicate Device |
|---|---|
| Siemens | |
| Al-Rad Companion (Musculoskeletal) | Siemens |
| syngo.CT Bone Reading | |
| (K123585) | |
| Al-Rad Companion (Musculoskeletal) is an 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 musculoskeletal disease. |
It provides the following functionality:
Segmentation of vertebras Labelling of vertebras Measurements of heights in each vertebra and indication
if they are critically different Measurement of mean Hounsfield value in volume of
interest within vertebra Only DICOM images of adult patients are considered to be valid
input. | The syngo.CT Bone Reading is image analysis software for CT
volume data sets which has been continuously acquired with
computed tomography (CT) imaging systems. The software
combines following digital image processing and visualization tools:
multiplanar reconstruction (MPR) thin/thick, maximum
intensity projection (MIP) thin/thick, inverted MIP
thin/thick, volume rendering technique (VRT) geometric measurement tools (distance line, polyline,
marker, arrow, angle) HU measurement tools (Pixel lens, ROI circle, ROi
polygonal, ROI freehand, VOI sphere) curved MPR visualization (unfolded ribs and spine views),
crosssection MPRs tools for creation and editing of anatomical centerline
paths tools for creation and editing of anatomical labels The specific visualizations of spine and rib structures allow for easy
manual identification and marking of pathologies such as bone
lesions or fractures.
Reporting and documentation of results is facilitated by using of
appropriate reporting tool, statistics and creation of ranges and
snapshots |
The Indications for Use for the subject device provides the following modifications as compared to the predicate device:
-
- New Subject Device Name: Al-Rad Companion (Musculoskeletal)
-
- The general goal of the subject device is to evaluate vertebras. The fundamental functionality as listed is in the subject device's Indications for Use is similar to the predicate device's Indications for Use. Features like segmentation and labeling of vertebras are marked as "HU Measurement tools" and "Tools for creation and editing of anatomical labels" in the predicate device. Measurement of heights and HU refer to "geometric measurement tools" and "HU measurement tools" in the predicate device' Indications for Use.
The Al-Rad Companion (Musculoskeletal) Indication for Use Statement was revised to reflect subject device specific functionality and to improve clarity. This IFU statement operates within the scope of the intended use for this and the cleared primary predicate device.
6
Comparison of Technological Characteristics with the Predicate Device
In comparison to the predicate device, the subject device provides comparable outputs in terms of vertebrae visualization/segmentation and labeling. A tabular high-level comparison of the subject device and predicate device as well as the refence device is provided as Table 1 and Table 2 below.
| Feature | Subject Device | Predicate Device | Comparison Results |
|---|---|---|---|
| Siemens Al-Rad Companion | |||
| (Musculoskeletal) | syngo.CT Bone Reading | ||
| (K123584) | |||
| Detection of | |||
| vertebrae | Detection of Vertebras | Detection of Vertebras | Same |
| Labeling of | |||
| vertebrae | Labelling of vertebras | Labelling of vertebras | Same |
| Segmentation of | |||
| vertebrae | Deep-learning-based | ||
| segmentation of vertebras | Model-based segmentation of | ||
| vertebras | Equivalent | ||
| *1) | |||
| Measurement of | |||
| heights | Distance measurements | ||
| based on segmentation | |||
| results and comparison with | |||
| neighboring measurements | Manual distance measurements | ||
| in vertebra and visual | |||
| comparison with neighboring | |||
| vertebra(s) | Equivalent | ||
| *2) | |||
| Measurement of | |||
| Hounsfield (HU) | |||
| value | HU measurements based on | ||
| segmentation results | HU measurement in region of | ||
| interest | Equivalent | ||
| *3) |
Table 1 Predicate Device Comparable Properties
Explanation to 1, 2, 3:
*1) Segmentation in predicate device is a model-based approach, in the subject device, however, a deep-learning algorithm has been included.
*2) The workflow in the predicate device requires manual height measurements, the user has to compare the measured heights applying the Genant criteria. In the subject device, the height measurements are automatically performed including the comparisons to the neighboring counterpart and the application of the Genant criteria.
*3) The Algorithm of the subject device uses the output of the segmentation step and fits a cylinder into each vertebra. Herein, the mean Hounsfield value is calculated.
| Feature | Subject Device | Reference Device | Comparison Result |
|---|---|---|---|
| Siemens Al-Rad Companion (Musculoskeletal) | Siemens Al-Rad Companion (Cardiovascular) (K183268) | ||
| Deep Learning Technology | Deep Image-to-image network for 3D segmentation of organs | Deep Image-to-image network for 3D segmentation of organs | Same |
Table 2 Reference Device Comparable Properties
Al-Rad Companion (Musculoskeletal) uses the same deep learning technology as the reference device Al-Rad Companion (Cardiovascular): a 3D Deep Image-to-Image network is used for organ segmentation. The main structure of the network is designed following a symmetric way as a convolutional encoder-decoder. All blocks of the network consist of 3D convolutional and bilinear upscaling layers.
7
Performance Data
Non-Clinical Performance Data Summary
The performance data demonstrates continued conformance with special controls for medical devices containing software. Non-clinical tests were conducted on the Subject Device Al-Rad Companion (Musculoskeletal) software version VA10 during product development. These tests are documented via Verification and Validation Traceability Analysis. For the subject device., Siemens used the same testing process with the same testing workflow as used to clear the predicate device. The result of all testing conducted was found acceptable to support the claim of substantial equivalence.
Clinical Performance Data Summary
The performance of the Al-Rad Companion (Musculoskeletal) device has been validated in a retrospective performance study on chest CT data (N=140, data from multiple clinical sites across the United States and Europe). Ground truth annotations were established using manual vertebra height and density measurements performed by four radiologists (two readers per case plus a third reader for adjudications). Inter-reader-variability, i.e. the 95%limits of agreement (LoA) of the measurements performed by the radiologists, was assessed. The ratio of vertebra height measurements generated by the subject device lying within the LoA around the ground truth was 95.1% for thin slices (≤1 mm slice thickness) and 87.5% for thicker slices (>1 mm slice thickness). Analogously the ratio of vertebra density measurements lying within the LoA was 98.8%. Performance was consistent for all critical subgroups, such as vendors or reconstruction parameters and patient age.
Risk Analysis Summary
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.
Voluntary Conformance Standards
Al-Rad Companion has been tested to meet the requirements of conformity to multiple industry standards. Non-clinical performance testing demonstrated that Al-Rad Companion complies with the following voluntary FDA recognized Consensus Standards listed in Table 3 below.
| Recognition
Number | Product
Area | Title of Standard | Publication
Date | Standards
Development
Organization |
|-----------------------|--------------------------|------------------------------------------------------------------------------------------------------------|---------------------|------------------------------------------|
| 12-300 | Radiology | Digital Imaging and Communications
in Medicine (DICOM) Set; PS 3.1 -
3.20 | 06/27/2016 | NEMA |
| 13-79 | Software | Medical Device Software - Software
Life Cycle Processes;
IEC 62304 Edition 1.1 2015-06 | 06/26/2015 | IEC |
| 5-40 | Software/
Informatics | Medical devices – Application of risk
management to medical devices;
14971 Second Edition 2007-03-01 | 08/20/2012 | ISO |
Table 3 Voluntary Conformance Standards
8
Cybersecurity
Siemens conforms to the cybersecurity requirements 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. 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.
Summary
Al-Rad Companion (Musculoskeletal) was tested and found to be safe and effective for intended users, uses and use environments through the design control verification and validation 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.
General Safety and Effectiveness:
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, and radiation hazards, Siemens adheres to recognized and established industry practice and standards.
Conclusions
Al-Rad Companion (Musculoskeletal) has the same intended use as the predicate device. The indications for use have been modified to include a more succinct summary of device specific performance, but is still within the scope of the intended use and regulatory classification of the predicate device. The fundamental technological characteristics, such as image visualization and image manipulation, are the same as the predicate device. The result of all testing conducted was found acceptable to support the claim of substantial equivalence.
The predicate device was cleared based on non-clinical supportive evidence. The results of those tests demonstrated that the predicate device was 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, Al-Rad Companion (Musculoskeletal), Siemens used the same testing with the same workflows used to clear the predicate device to demonstrate safety and performance of the technical workflow. Clinical applicability was demonstrated via softwaredata based validations that were derived in the same intended environment as the predicate device. Since both devices were tested using the same methods, Siemens believes that the data generated from the Al-Rad Companion (Musculoskeletal) software testing supports a finding of substantial equivalence.