Who is the manufacturer of AI-Rad Companion (Musculoskeletal)?

Siemens Medical Solutions USA, Inc. filed the 510(k) submission for AI-Rad Companion (Musculoskeletal) (K222361).

What is the FDA product code for AI-Rad Companion (Musculoskeletal)?

JAK. It is classified under 21 CFR 892.1750 as a Class 2 device in the Radiology panel.

What is the regulatory pathway for AI-Rad Companion (Musculoskeletal)?

510(k) clearance (submission type: Traditional).

Does AI-Rad Companion (Musculoskeletal) use AI or machine learning?

Yes. AI-Rad Companion (Musculoskeletal) (K222361) is flagged as an AI/ML-enabled medical device in the FDA's AI/ML device list.

Who can help prepare an FDA 510(k) submission like AI-Rad Companion (Musculoskeletal)?

Innolitics — a medical-device software consultancy — provides regulatory and engineering support for FDA 510(k) submissions. See https://innolitics.com/services/510ks/ or contact https://innolitics.com/contact.

Can an AI/ML device like AI-Rad Companion (Musculoskeletal) get a guaranteed FDA 510(k) clearance?

Yes. Innolitics offers a guaranteed FDA 510(k) clearance engagement for AI/ML Software as a Medical Device — combining engineering and regulatory execution under one agreement with a guaranteed clearance outcome. Eligibility: the device must be an AI/ML Software as a Medical Device (SaMD), and its performance must meet the criteria Innolitics determines during an initial regulatory strategy engagement to be necessary for FDA clearance. See https://innolitics.com/services/end-to-end-samd/ or contact https://innolitics.com/contact.

AI-Rad Companion (Musculoskeletal)

K222361 · Siemens Medical Solutions USA, Inc. · JAK · Oct 20, 2022 · Radiology

Device Facts

Record ID	K222361
Device Name	AI-Rad Companion (Musculoskeletal)
Applicant	Siemens Medical Solutions USA, Inc.
Product Code	JAK · Radiology
Decision Date	Oct 20, 2022
Decision	SESE
Submission Type	Traditional
Regulation	21 CFR 892.1750
Device Class	Class 2
Attributes	AI/ML, Software as a Medical Device

Intended Use

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.

Device Story

AI-Rad Companion (Musculoskeletal) is image processing software; inputs are previously acquired adult CT DICOM images. Device uses deep learning algorithms to perform 3D segmentation and labeling of vertebrae; calculates vertebral heights and mean Hounsfield units (HU); identifies critical height differences. Operates via cloud or on-premise edge deployment within customer network; integrated with teamplay digital health platform. Results are presented to radiologists/physicians as quantitative, structured, and text reports (DICOM secondary capture/TID 1500). Output assists clinicians in musculoskeletal disease assessment; enhances workflow efficiency by automating measurements. System supports remote monitoring and maintenance.

Clinical Evidence

Bench testing only. Performance validated on 140 chest CTs (1,553 thoracic vertebrae) from diverse manufacturers (GE, Philips, Siemens, Toshiba). Primary endpoints: segmentation accuracy, vertebral labeling, and height/density measurement consistency. Results: 8.6% failure rate for labeling/missing measurements; inter-reader variability for height measurements within LoA for 95.5% of cases (slice thickness ≤1.0mm) and 92.6% (slice thickness >1.0mm). Sub-group analysis showed performance equal or superior to predicate.

Technological Characteristics

Software-only device; deep learning image-to-image network for 3D segmentation. Connectivity: Cloud and on-premise edge deployment; DICOM-compliant. Standards: IEC 62366-1 (usability), ISO 14971 (risk management), AAMI/ANSI/IEC 62304 (software lifecycle), IEC 82304-1 (health software safety).

Indications for Use

Indicated for adult patients undergoing CT imaging to support radiologists and physicians in emergency, specialty, urgent, and general practice settings for the evaluation and assessment of musculoskeletal disease.

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 (Musculoskeletal) (K193267)

Related Devices

K193267 — Al-Rad Companion (Musculoskeletal) · Siemens Medical Solutions USA, Inc. · Mar 16, 2020
K241593 — BoneMetrics (US) · Gleamer Sas · Feb 5, 2025
K213944 — HealthOST · Nanoxai , Ltd. · Apr 22, 2022
K253944 — Data Analysis Facilitation Suite (DAFS) · Voronoi Health Analytics Incorporated · Mar 16, 2026
K222360 — AI-Rad Companion (Cardiovascular) · Siemens Medical Solutions U.S.A. · Apr 6, 2023

Submission Summary (Full Text)

{0}------------------------------------------------ Image /page/0/Picture/0 description: The image contains the logos of the Department of Health & Human Services and the Food and Drug Administration (FDA). The Department of Health & Human Services logo is on the left, and the FDA logo is on the right. The FDA logo is a blue square with the letters "FDA" in white, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue. October 20, 2022 Siemens Medical Solutions USA, Inc. % Kira Kuzmenchuk Regulatory Affairs Manager 40 Liberty Blvd. MALVERN PA 19355 Re: K222361 Trade/Device Name: AI-Rad Companion (Musculoskeletal) Regulation Number: 21 CFR 892.1750 Regulation Name: Computed Tomography X-Ray System Regulatory Class: Class II Product Code: JAK Dated: October 4, 2022 Received: October 12, 2022 Dear Kira Kuzmenchuk: We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading. If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register. Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal {1}------------------------------------------------ statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-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, Laurel Burk, Ph.D. Assistant Director Diagnostic X-Ray Systems Team DHT8B: Division of Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health Enclosure {2}------------------------------------------------ #### Indications for Use 510(k) Number (if known) #### K222361 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) #### 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}------------------------------------------------ K222361 SIEMENS Healthineers # 510(k) SUMMARY FOR AI-RAD COMPANION (Musculoskeletal) SW version VA20 Submitted by: Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19355 Date Prepared: October 20, 2022 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 Mail Code: 65-1A Registration Number: 2240869 | |----------------------|--------------------------------------------------------------------------------------------------------------------------------------| | Manufacturing Site | Siemens Healthcare GmbH Henkestrasse 127 Erlangen, Germany 91052 Registration Number: 3002808157 | ### 2. Contact Person Kira Kuzmenchuk Regulatory Affairs Specialist Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19335 Email: Kira.Kuzmenchuk@siemens-healthineers.com Phone: (484) 901-9471 ## 3. Device Name and Classification | Product Name: | AI-Rad Companion (Musculoskeletal) | |---------------|------------------------------------| | Trade Name: | AI-Rad Companion (Musculoskeletal) | {4}------------------------------------------------ Image /page/4/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. Classification Name: Classification Panel: CFR Section: Device Class: Product Code: Computed Tomography X-Ray System Radiology 21 CFR §892.1750 Class II JAK ## 4. Predicate Device | Product Name: | AI-Rad Companion (Musculoskeletal) | |-----------------------|------------------------------------| | Propriety Trade Name: | AI-Rad Companion (Musculoskeletal) | | 510(k) Number: | K193267 | | Clearance Date: | March 16, 2020 | | Classification Name: | Computed Tomography X-Ray System | | Classification Panel: | Radiology | | CFR Section: | 21 CFR §892.1750 | | Device Class: | Class II | | Primary Product Code: | JAK | | Recall Information: | N/A | ## 5. 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. ## 6. Device Description AI-Rad Companion (Musculoskeletal) SW version VA20 is an enhancement to the previously cleared device AI-Rad Companion (Musculoskeletal) K193267 that utilizes deep learning algorithms to provide quantitative and qualitative analysis to computed tomography DICOM images to support qualified clinicians in the evaluation and assessment of the spine. {5}------------------------------------------------ Image /page/5/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. As an update to the previously cleared device, the following modifications have been made: - Enhanced AI Algorithm The vertebrae segmentation accuracy has been improved through retraining the algorithm. - DICOM Reports The reports generated out of the system have been enhanced to support both human and machine-readable formats. Additionally, an update version of the system changed the DICOM structured report format to TID 1500 for applicable content. - Architecture Enhancement for on premise Edge deployment The system supports the existing cloud deployment as well as an on premise "edge" deployment. The system remains hosted in the teamplay digital health platform and remains driven by the AI-Rad Companion Engine. Now the edge deployment implies that the processing of clinical data and the generation of results can be performed onpremises within the customer network. The edge system is fully connected to the cloud for monitoring and maintenance of the system from remote. ## 7. Substantially Equivalent (SE) And Technological Characteristics The intended use of the predicate device and the subject device are equivalent. The subject device, AI-Rad Companion (Musculoskeletal) VA20 is substantially equivalent with regard to the intended use and technical characteristics compared to the predicate device, AI-Rad Companion (Musculoskeletal) (K193267), with respect to the software features, functionalities, and core algorithms. The additional features, enhancements and improvements provided in AI-Rad Companion (Musculoskeletal) VA20 increase the usability and reduce the complexity of the imaging workflow for the clinical user. The risk analysis and non-clinical data support that both devices perform equivalently and do not raise different questions of the safety and effectiveness. | Feature | Subject Device AI-Rad Companion (Musculoskeletal) VA20 | Predicate Device AI-Rad Companion (Musculoskeletal) (K193267) | |------------------------------|-----------------------------------------------------------------|------------------------------------------------------------------------| | Modality | CT | CT | | Detection of Vertebrae | Detection of Vertebras | Detection of Vertebras | | Labeling of Vertebrae | Labeling of Vertebras | Labeling of Vertebras | | Segmentation of Vertebrae | Deep learning based segmentation of vertebras | Deep learning based segmentation of vertebras | The comparison between the above referenced predicate device are listed at a high-level in the following table. {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 words is a graphic of orange dots arranged in a circular pattern. | Measurement of Heights | Distance measurements based on segmentation results and comparison with neighboring measurements | Distance measurements based on segmentation results and comparison with neighboring measurements | |------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------| | Measurement of Hounsfield (HU) Values | HU measurements based on segmentation results | HU measurements based on segmentation results | | Algorithm | Deep learning image to image network for 3D segmentation | Deep learning image to image network for 3D segmentation | | Deployment | Cloud and on-premise deployment | Cloud deployment | | Reports | Quantitative, Structured and Text reports with DICOM secondary capture & TID 1500 in both human and machine readable formats. | Quantitative, Structured and Text reports with DICOM secondary capture images | Table 1: Technological Comparisons 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. Siemens is of the opinion that AI-Rad Companion (Musculoskeletal) VA20 is substantially equivalent to the currently marketed device, AI-Rad Companion (Musculoskeletal) (K193267). ### 8. Nonclinical Tests Non-clinical tests were conducted to test the functionality of AI-Rad Companion (Musculoskeletal). 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. 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 (Musculoskeletal) complies with the FDA guidance document, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" (May 11, 2005) as well as with the following voluntary FDA recognized Consensus Standards listed in Table 2. | Recognition Number | Product Area | Title of Standard | Reference Number and Date | Standards Development Organization | |-----------------------|-----------------|---------------------------------------------------------------------------------------------------------------------|---------------------------------|------------------------------------------| | 5-114 | General | Medical Devices – Application of usability engineering to medical devices [including Corrigendum 1 (2016)] | 62366-1: 2015- 02 | IEC | | 5-125 | General | Medical Devices – application of risk management to medical devices | 14971:2007 | ISO | {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 text is a graphic of several orange dots arranged in a circular pattern. | 13-79 | Software/ Informatics | Medical device software – software life cycle processes [Including Amendment 1 (2016)] | 62304: 2006/A1:2016 | AAMI ANSI IEC | |--------|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------|---------------------| | 12-300 | Radiology | Digital Imaging and Communications in Medicine (DICOM) Set | PS 3.1 – 3.20 (2016) | NEMA | | 12-261 | Radiology | Information Technology – Digital Compression and coding of continuous -tone still images: Requirements and Guidelines [including: Technical Corrigendum 1(2005)] | 10918-1 1994- 02-15 | ISO IEC | | 5-134 | General | Medical devices – symbols to be used with information to be supplied by the manufacturer – Part 1: General Requirements | 15223-1 Fourth edition 2021-07 | ISO IEC | | 13-97 | Software/ Informatics | Health software – Part 1: General requirements for product safety | 82304-1 Edition 1.0 2016-10 | IEC | Table 2: List of recognized standards #### Verification and Validation Software documentation for a Moderate Level of Concern software, per FDA's Guidance Document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" issued on May 11, 2005, is also included as part of this submission. The performance data demonstrates continued conformance with special controls for medical devices containing software. Non-clinical tests were conducted on the subject device 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 "Content of Premarket Submission for Management of Cybersecurity in Medical Devices: Guidance for Industry and Food and Drug Administration Staff" (October 18, 2018) 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. {8}------------------------------------------------ Image /page/8/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. ### 9. Performance Software Validation To validate the AI-Rad Companion (Musculoskeletal) VA40 software from a clinical perspective, the algorithm contained in the product underwent a scientific evaluation. The results of clinical data-based software validation for the subject device AI-Rad Companion (Musculoskeletal) demonstrated equivalent performance in comparison to the reference device. A complete scientific evaluation report is provided in support of the device modifications. Performance testing for AI-Rad Companion (Musculoskeletal) was performed on 140 subjects (clinically relevant patient data shown in Table 2) during product development. Additionally, the segmentation and height measurements of the thoracic vertebral bodies were reassessed for accuracy. | Validation Type | Acceptance Criteria | |-----------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------| | Mislabeling of Vertebrae or absence of height measurement | Ratio of cases that are mislabeled or missing measurements shall be <10% of all cases | | Inter-reader variability: heights calculated by AIRC & the ground truth should be within the LoA reported | For cases with slice thickness $\le$ 1.0 mm, the difference should be within the LoA for $\ge$ 95% of cases | | | For cases with slice thickness >1.0mm, the difference should be within the LoA for $\ge$ 85% of cases | | Consistency of height and density measurement across critical sub-groups | For each sub-group, the ratio of measurements within the corresponding LoA should not drop by more than 5% compared to the ratio for all data sets | #### Acceptance Criteria Table 2: Acceptance Criteria Information {9}------------------------------------------------ Image /page/9/Picture/0 description: The image shows the Siemens Healthineers logo. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. There are also some orange dots to the right of the words. #### Summary Performance Data | Validation Type | Results | |-----------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Mislabeling of Vertebrae or absence of height measurement | Failure Rate of 8.6% | | Inter-reader variability: heights calculated by AIRC & the ground truth should be within the LoA reported | For cases with slice thickness ≤1.0 mm, the difference was 95.5% | | | For cases with slice thickness >1.0mm, the difference was 92.6% | | Consistency of height and density measurement across critical sub-groups | Overall failure rate of the subject device was consistent with the predicate as well as having the results of all sub-group analysis rated equal or superior to the predicate | Table 3: Summary Performance Data #### Testing Data Information | # data sets | 140 Chest CTs (1,553 thoracic vertebrae) | |-------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Pathologies / Patient info | KUM (N=80): Primary indications: Lung/airways 10; infect focus 10; malignancy 22, follow-up 10; (cardio-)vascular 14; ischemia 2; bleeding 4; trauma 1; lymph nodes 3; inflammation 1; unknown 3. NLST (N=60): Comorbidities: diabetes 7; heart disease 10; hypertension 24; cancer 9; emphysema/COPD 14; asthma 2; pneumonia 13; chron. bronchitis 1. Smoking history (pack years): median: 47, IQR: [38, 70] | | Sex | male: 47, female: 93 | | Age [yrs] | ≤55: 16, (55, 65]: 59, (65, 75]: 38, >75: 25 median: 65, IQR: [60, 72] | | Manufacturer | GE: 32, Philips: 20, Siemens: 68, Toshiba: 20 | | Slice Thickness [mm] | ≤1.0: 60, (1.0, 1.5]: 24, (1.5, 2.0]: 56 | | Dose | KUM: CTDIVol [mGy]: median 4.8, IQR: [1.3, 10.7] NLST: low dose (screening) | | Reconstruction method | Filtered backprojection: 107 Iterative reconstruction: 33 | | Reconstruction kernel | soft: 25, medium: 68, hard: 47 | | Contrast Enhancement | enhanced: 59, native: 81 | Table 4: Testing Data Information {10}------------------------------------------------ Image /page/10/Picture/0 description: The image shows the logo for Siemens Healthineers. The word "SIEMENS" is written in teal, and the word "Healthineers" is written in orange below it. To the right of the words is a graphic of orange dots arranged in a circular pattern. #### Standard Annotation Process For ground truth annotations, four board-certified radiologists were selected. Vertebra heights and average density (HU) values were measured manually and loaded into the application and automatic detection and labelling was performed. The 140 cases were randomly distributed across the four readers such that each case was read independently by two radiologists in randomized order. For outliers, a third annotation was blindly provided by one of the radiologist who had not annotated before. The ground truth was generated by the average of the two most concordant measurements. For all other cases, the two annotations were used as ground truth. #### Testing & Training Data Independence The training data used for the training of the post-processing algorithm is independent of the data used to test the algorithm. ## 10. Clinical Tests No clinical tests were conducted to test the performance and functionality of the modifications introduced within AI-Rad Companion (Musculoskeletal). 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. ## 11. 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.