syngo.via molecular imaging (MI) workflows comprise medical diagnostic applications for viewing, manipulation, quantification, analysis and comparison of medical images from single or multiple imaging modalities with one or more time-points. These workflows support functional data, such as positron emission tomography (PET) or nuclear medicine (NM), as well as anatomical datasets, such as computed tomography (CT) or magnetic resonance (MR). syngo.via MI workflows can perform harmonization of SUV (PET) across different PET systems or different PET reconstruction methods.

syngo.via MI workflows are intended to be utilized by appropriately trained health care professionals to aid in the management of diseases, including those associated with oncology, cardiology, neurology, and organ function. The images and results produced by the syngo.via MI workflows can also be used by the physician to aid in radiotherapy treatment planning.

Device Description

syngo.via MI Workflows (including Scenium and syngo MBF applications) is a multi-modality post-processing software only medical device intended to aid in the management of diseases, including those associated with oncology, cardiology, neurology, and organ function. The syngo.via MI Workflows applications are part of a larger syngo.via client/server system which is intended to be installed on common IT hardware. The hardware itself is not seen as part of the syngo.via MI Workflows medical device.

The syngo.via MI Workflows software addresses the needs of the following typical users of the product:

Reading Physician / Radiologist – Reading physicians are doctors who are trained in interpreting patient scans from PET, SPECT and other modality scanners. They are highly detail oriented and analyze the acquired images for abnormalities, enabling ordering physicians to accurately diagnose and treat scanned patients. Reading physicians serve as a liaison between the ordering physician and the technologists, working closely with both.
Technologist – Nuclear medicine technologists operate nuclear medicine scanners such as PET and SPECT to produce images of specific areas and states of a patient's anatomy by administering radiopharmaceuticals to patients orally or via injection. In addition to administering the scan, the technologist must properly select the scan protocol, keep the patient calm and relaxed, monitor the patient's physical health during the protocol and evaluate the quality of the images. Technologists work very closely with physicians, providing them with quality-checked scan images.

The software has been designed to integrate the clinical workflow for the above users into a server-based system that is consistent in design and look with the base syngo.via platform and other syngo.via software applications. This ensures a similar look and feel for radiologists that may review multiple types of studies from imaging modalities other than Molecular Imaging, such as MR.

The syngo.via MI workflows software supports integration through DICOM transfers of positron emission tomography (PET) or nuclear medicine (NM) data, as well as anatomical datasets, such as computed tomography (CT) or magnetic resonance (MR).

Although data is automatically imported into the server based on predefined configurations through the hospital IT system, data can also be manually imported from external media, including CD, external mass storage devices, etc.

The Siemens syngo.via platform and the applications that reside on it, including syngo.via MI Workflows, are distributed via electronic medium. The Instructions for Use is also delivered via electronic medium.

syngo.via MI Workflows includes 2 workflows (syngo.MM Oncology and syngo.MI General) as well as the Scenium neurology software application and the syngo MBF cardiology software application which are launched from the OpenApps framework within the MI General workflow.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study details for the syngo.via MI Workflows, Scenium, and syngo MBF devices:

Acceptance Criteria and Reported Device Performance

For Lung and Lung Lobe Segmentation:

Acceptance Criteria Category	Specific Criteria	Reported Device Performance (Subject Device vs. Predicate)
New Organs	Average Dice coefficient per organ > 0.8 OR Average Symmetric Surface Distance (ASSD) per organ < 10 mm (2 voxel of worst slice thickness).	Not applicable for lung lobes, as they are existing organs.
Unchanged Organs	Average Dice coefficient per organ within +/- 0.03 of predicate.	Not explicitly stated for lung lobes, but the overall statement below is crucial.
Improved Organs	Average Dice coefficient per organ >= predicate.	The average Dice coefficient for the 20 subjects was higher for each lobe in the subject device than in the predicate device, although not greater than a +0.03 difference for all lobes.

For PERCIST Liver Reference Region Placement (Binary Liver Mask, input to the algorithm):

Acceptance Criteria Category	Specific Criteria	Reported Device Performance
New/Existing Organs	Average Dice coefficient > 0.8 OR Average Symmetric Surface Distance (ASSD) < 10 mm.	The liver met both criteria.

For PERCIST Liver Reference Region Placement (Algorithm itself):

Acceptance Criteria Category	Specific Criteria	Reported Device Performance (Subject Device vs. Predicate)
Agreement with Expert Readers	Subject device yields results in better agreement with semi-automatic evaluation by expert readers compared with the predicate method.	Subject device shown to yield results in better agreement with semi-automatic evaluation by expert readers compared with the method of placement used in the predicate device.
Intersection with Suspicious Uptake	Fewer intersections with suspicious uptake masks compared to the predicate device.	Subject device had fewer intersections (4 cases) compared to the predicate device (13 cases) out of 129 subjects.

Study Details

1. Sample Size Used for the Test Set and Data Provenance:

Lung and Lung Lobe Segmentation:
- Sample Size: 20 patients.
- Data Provenance: Retrospective.
  - Approximately 50% of patients were from the US.
  - The remaining patients were not specified but implied to be from outside the US (given the 50% US mention).
  - All patients were from Siemens Scanners.
PERCIST Liver Reference Region Placement (for Binary Liver Mask):
- Sample Size: 20 patients.
- Data Provenance: Retrospective.
  - Patients were obtained from clinical partners in Europe and USA.
  - All subjects were from Siemens Scanners.
PERCIST Liver Reference Region Placement (for Algorithm evaluation):
- Sample Size: 129 subjects.
- Data Provenance: Retrospective (implied, as it refers to "PET/CT scans presenting foci"). Specific countries of origin are not mentioned, but the clinical partners from Europe and USA for the binary liver mask suggest a similar provenance.

2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:

Lung and Lung Lobe Segmentation: Not explicitly stated. The phrase "Quantitative evaluation of the segmentation results was performed using the commonly used overlap measure Dice coefficient (DSC)" suggests a reference standard was used, likely derived from expert manual segmentation by one or more experts, but the exact number and qualifications are not provided in this document.
PERCIST Liver Reference Region Placement (for Algorithm evaluation - first analysis):
- Number of Experts: Two expert readers.
- Qualifications: "Expert readers" is the only qualification given. Their specific background (e.g., radiologists, years of experience) is not detailed.
PERCIST Liver Reference Region Placement (for Algorithm evaluation - second analysis):
- Number of Experts: One expert reader.
- Qualifications: "An expert reader" is the only qualification given.

3. Adjudication Method for the Test Set:

Lung and Lung Lobe Segmentation: Not explicitly stated.
PERCIST Liver Reference Region Placement (first analysis): "Liver VOI positioning obtained semi-automatically by two expert readers." This implies a consensus or agreement process, but a specific adjudication method (e.g., 2+1, 3+1) is not detailed. It just states the reference standard was "obtained semi-automatically by two expert readers."
PERCIST Liver Reference Region Placement (second analysis): "Suspicious uptake masks identified by an expert reader." This indicates a single expert was used for identifying the reference truth for this part of the analysis, so no adjudication method is mentioned.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

No, an MRMC comparative effectiveness study was not explicitly stated as having been performed. The studies described compare the algorithm's performance to a ground truth established by experts or compare the subject device algorithm's performance directly to the predicate device's algorithm, rather than evaluating human readers with and without AI assistance.

5. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done:

Yes, standalone (algorithm only) performance was evaluated for both lung/lung lobe segmentation and PERCIST liver reference region placement. The results reported (Dice coefficients, ASSD, number of intersections) are direct metrics of the algorithms' outputs compared to a ground truth or predicate algorithm. The document states that "the user can manually reposition the PERCIST liver reference region at any time," which indicates that the algorithm's performance is standalone, and human intervention is a subsequent step, not part of the core evaluation.

6. The Type of Ground Truth Used:

Lung and Lung Lobe Segmentation: The ground truth was based on a reference standard used to calculate Dice coefficient (DSC), which typically implies expert segmentation/consensus.
PERCIST Liver Reference Region Placement (Binary Liver Mask): The ground truth was used to calculate Dice coefficient and ASSD, indicating expert segmentation/consensus for the liver mask.
PERCIST Liver Reference Region Placement (Algorithm evaluation - first analysis): Ground truth was "liver VOI positioning obtained semi-automatically by two expert readers," which is expert consensus/semi-automatic expert delineation.
PERCIST Liver Reference Region Placement (Algorithm evaluation - second analysis): Ground truth was "suspicious uptake masks identified by an expert reader," which is expert identification/delineation.

7. The Sample Size for the Training Set:

Lung and Lung Lobe Segmentation: "re-trained with additional data." The specific sample size for the training set is not provided in this document.
PERCIST Liver Reference Region Placement: Explicitly stated "No overlap of patients between training, tuning, and test cohorts," indicating a training set was used, but the specific sample size is not provided.

8. How the Ground Truth for the Training Set was Established:

Lung and Lung Lobe Segmentation: Implicitly, the training set would have had ground truth established through expert annotation/segmentation, similar to how ground truth for the test set is typically established for such algorithms, but this is not explicitly detailed in the document.
PERCIST Liver Reference Region Placement: Implicitly, the training set would have had ground truth established through expert annotation/segmentation, but this is not explicitly detailed in the document.

Summary

U.S. Food & Drug Administration 510(k) Clearance Letter

Page 1

U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov

Doc ID # 04017.07.05

July 3, 2025

Siemens Medical Solutions USA, Inc.
Clayton Ginn
Regulatory Affairs Professional
2501 North Barrington Road
Hoffman Estates, Illinois 60192

Re: K251528
Trade/Device Name: syngo.via MI Workflows; Scenium; syngo MBF
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical Image Management And Processing System
Regulatory Class: Class II
Product Code: QIH
Dated: May 19, 2025
Received: May 19, 2025

Dear Clayton Ginn:

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"

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K251528 - Clayton Ginn
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(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).

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

All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.

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-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.

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K251528 - Clayton Ginn
Page 3

For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/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-devices/device-advice-comprehensive-regulatory-assistance/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,

Daniel M. Krainak, Ph.D.
Assistant Director
DHT8C: Division of Radiological
Imaging and Radiation Therapy Devices
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health

Enclosure

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Indications for Use

Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions.
K251528

Please provide the device trade name(s).
syngo.via MI Workflows;
Scenium;
syngo MBF

Please provide your Indications for Use below.

Please select the types of uses (select one or both, as applicable).
☑ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)

syngo.via MI Workflows
K251528

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510(k) Summary

K251528

1. Identification of the Submitter

Submitter / Primary Contact Person
Clayton Ginn
Regulatory Affairs
clayton.ginn@siemens-healthineers.com
+1 (865) 898-2692

Secondary Contact Person
Brian Wui
Regulatory Affairs
hansong.wui@siemens-healthineers.com
+1 (865) 367-4337

Applicant Name and Address
Siemens Medical Solutions, Inc. USA
2501 North Barrington Road
Hoffman Estates IL, 60192, USA
Establishment Registration Number: 1423253

Date of Preparation
May 15th, 2025

2. Device Name and Classification

Product Trade Name:	syngo.via MI Workflows; Scenium; syngo MBF
Common Name:	Medical image management and processing system
Classification Name:	Automated Radiological Image Processing Software
Classification Panel:	Radiology
CFR Section:	21 CFR §892.2050
Device Class:	Class II
Product Code:	QIH

3. Predicate Devices

Primary Predicate Device:
Product Trade Name: syngo.via MI Workflows; Scenium; syngo MBF
510(k) Number: K242275
Clearance Date: 08/30/2024

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Common Name: Medical image management and processing system
Classification Name: Automated Radiological Image Processing Software
Classification Panel: Radiology
CFR Section: 21 CFR §892.2050
Device Class: Class II
Product Code: QIH

Reference Predicate Device:
Product Trade Name: syngo.via MI Workflows; Scenium; syngo MBF
510(k) Number: K232000
Clearance Date: 11/28/2023
Common Name: Medical image management and processing system
Classification Name: Automated Radiological Image Processing Software
Classification Panel: Radiology
CFR Section: 21 CFR §892.2050
Device Class: Class II
Product Code: QIH

4. Device Description

The syngo.via MI Workflows software addresses the needs of the following typical users of the product:

Reading Physician / Radiologist – Reading physicians are doctors who are trained in interpreting patient scans from PET, SPECT and other modality scanners. They are highly detail oriented and analyze the acquired images for abnormalities, enabling ordering physicians to accurately diagnose and treat scanned patients. Reading physicians serve as a liaison between the ordering physician and the technologists, working closely with both.

Page 7

Technologist – Nuclear medicine technologists operate nuclear medicine scanners such as PET and SPECT to produce images of specific areas and states of a patient's anatomy by administering radiopharmaceuticals to patients orally or via injection. In addition to administering the scan, the technologist must properly select the scan protocol, keep the patient calm and relaxed, monitor the patient's physical health during the protocol and evaluate the quality of the images. Technologists work very closely with physicians, providing them with quality-checked scan images.

5. Indications for Use

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6. Indications for Use Comparison to the Predicate Device

The indications for use are the same between the subject device and the primary predicate device.

7. Comparison of Technological Characteristics with the Predicate Device

syngo.via MI Workflows with software version VC10, Scenium with software version VE70, and syngo MBF with software version VB30 software provide the same technological characteristics in terms of materials, energy source, and control mechanisms when compared to the legally marketed predicate device since all devices are software only devices.

The software features have been modified in comparison to the predicate device to support enhanced device functionality.

The intended use, indications for use, and fundamental scientific technology for the subject device remains unchanged from the predicate device. No features present from the predicate device have been de-scoped.

At a high level, the subject and predicate devices are based on the following same technological elements:

Data Supported (PET, SPECT, CT, MR)
Server/Client architecture
Workflow Activities (preprocessing, evaluation and reading, reporting and storage)
Feature Licensing Structure
SUV values calculated

The following technological differences exist between the subject device and predicate devices.

syngo.via MI Workflows VC10:

MI General

Improved Lung Segmentation in Anatomy Segmentation and Auto Lung 3D
VQ Ratio and Subtraction Images for Auto Lung 3D
General Masking Tool for PET/SPECT/GNM
Deauville Score from MM Oncology
Improved Liver Reference Region Placement
CT Oncology Extensions
- Basic Oncology Tools: Assisted Perpendicular (RECIST/WHO) and Nodule Marker (Lesion quantification / lung lesion segmentation)
- Lung CAD
Layout editing and User Presets updates
Organ Processing Updates
- CBF Image Creation and Display
- GSA Liver
- Cardiac Shunt

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MM Oncology

Extended Large Matrix CT Data Support
Advanced Density Tool Enhancements for CT Segmentations
SPP 2.0 Extended Support (DE Preprocessing, VNC/Iodine Fusion, DE Layouts)

Scenium VE70:

No changes

syngo MBF VB30:

No changes

Any differences in technological characteristics do not raise different questions of safety and effectiveness. Testing and validation are completed. Test results show that the subject devices are comparable to the predicate devices in terms of technological characteristics and safety and effectiveness and therefore are substantially equivalent to the predicate devices.

8. Non-Clinical and/or Clinical Test Summary & Conclusions

The following performance data were provided in support of the substantial equivalence determination.

Non-Clinical Testing

'Enhanced' software documentation per FDA's guidance document "Content of Premarket Submissions for Device Software Functions" issued in June, 2023 is also included as part of this submission. The performance data demonstrates continued conformance with special controls for medical devices containing software. The testing supports that all software specifications have met the acceptance criteria. Verification and validation testing substantiates all requirement and functional specifications, including specifications related to device hazards, and supports the claim of substantial equivalence.

Lung and Lung Lobe Segmentation

In addition to verification and validation testing, performance evaluation was conducted in order to ensure the safety and effectiveness of the lung lobe segmentation algorithm compared to the predicate device. The lung lobe segmentation algorithm was re-trained with additional data and is utilized within the Auto Lung 3D and Anatomy Segmentation features of the MI General workflow.

Quantitative evaluation of the segmentation results was performed using the commonly used overlap measure Dice coefficient (DSC). The algorithm was tested retrospectively on an independent cohort of 20 patients that were not part of training or tuning cohort. The test cohort was augmented compared to the predicate to include new subjects. Half of the patients in the test cohort were new and the other 50% were randomly selected from the predicate testing cohort. Performance was compared between the predicate and updated algorithms. In summary:

I. No overlap of patients between training, tuning, and test cohorts.
II. Relevant test cohort parameters are as follows:

~50% male patients
Slice thickness <=5 mm

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50% of patients were from the US
All patients from Siemens Scanner
Adults, age > 21

Acceptance Criteria for organ segmentation in syngo.via MI Workflows:

For new organs, the average Dice coefficient per organ shall be greater than 0.8 or the average symmetric surface distance (ASSD) per organ less than 2 voxel of worst slice thickness, i.e. 10 mm.
For unchanged organs, the average Dice coefficient per organ shall be within +/-0.03 of the average Dice coefficient per organ of the predicate algorithm.
For improved organs, the average Dice coefficient per organ shall be greater or equal than the average Dice coefficient per organ of the predicate algorithm.

The average Dice coefficient for the 20 subjects was higher for each lobe in the subject device than in the predicate device, although not greater than a +0.03 difference for all lobes.

The anatomy segmentation feature supports segmentation for a wide variety of organs. Since each organ utilizes a different model, the Dice-score on other organs (other than lungs and lung lobes) that were not retrained remained unchanged and this was verified by recalculating the Dice score with the new algorithm.

PERCIST Liver Reference Region Placement

Additionally, performance evaluation was conducted for the updated PERCIST Liver Reference Region placement. The algorithm takes as input the PET/CT image together with a binary liver mask and returns the coordinates of the reference region center.

The performance specifications for the binary liver mask used as input to the algorithm are detailed below. This AI/ML segmentation algorithm is the same segmentation algorithm as utilized in the Anatomy Segmentation feature of the reference predicate device (K232000).

The test data consisted of 20 patients. The patients were obtained from clinical partners in Europe and USA. The data was randomly selected using the following stratification:

No overlap of patients between training, tuning, and test cohorts
Adults, Age > 21
~50% male patients
Slice thickness <= 5 mm
All subject from Siemens Scanner

The acceptance criteria for the liver and all other organs supported by the anatomy segmentation feature is an average Dice coefficient greater than 0.8 or an average symmetric surface distance (ASSD) less than 10 mm. The liver met both criteria.

The performance evaluation for the updated PERCIST Liver Reference Region placement is detailed below.

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In the first analysis conducted, the reference standard used to evaluate the subject device method performance consisted of liver VOI positioning obtained semi-automatically by two expert readers. The subject device algorithm was then compared to the reference standard and shown to yield results in better agreement with semi-automatic evaluation by expert readers compared with the method of placement used in the predicate device.

The second analysis conducted focused on PET/CT scans presenting foci with suspicious tracer uptake either in the liver or with spill over to the liver, evaluating the subject and predicate devices based on how often the PERCIST VOIs intersected the suspicious uptake masks identified by an expert reader. Out of 129 subjects included in the analysis, the subject device had fewer intersections (4 cases) compared to the predicate device (13 cases). As with the predicate device, the user can manually reposition the PERCIST liver reference region at any time.

Clinical Testing

Clinical testing was not conducted for this submission.

Conclusion

There are no differences in the Indications for Use, Intended Use, or Fundamental Technological Characteristics of the updated syngo.via MI Workflows software (including Scenium and syngo MBF) as compared to the currently commercially available syngo.via MI Workflows software (K242275).

Both the current and predicate devices are used for viewing, manipulation, quantification, analysis, and comparison of medical images from single or multiple imaging modalities with one or more time-points.

Additionally, the new features implemented within this release do not raise any new issues of safety and effectiveness as compared to the predicate device. The predicate devices were cleared based on the results of non-clinical testing including verification and validation. The subject device is also validated using the same methods as used for the predicate devices. The non-clinical verification and validation demonstrate that the subject devices should perform as intended in the specified use conditions.

Based on this information, as well as the documentation in support of the modifications, the subject devices with the modifications outlined in this application are substantially equivalent to the predicate devices.

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).