MI View&GO is a medical diagnostic application for viewing, manipulation, quantification, analysis and comparison of medical images with one or more time-points. MI View&GO supports 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).

MI View&GO is intended to be utilized by appropriately trained health care professionals to aid in the management of diseases associated with oncology, cardiology, neurology, and organ function. The images and results produced by MI View&GO can also be used by the physician to aid in radiotherapy treatment planning.

Device Description

MI View&GO is a software-only medical device which will be delivered in conjunction with Siemens SPECT/CT and PET/CT scanners. MI View&GO software provides additional specific capabilities for handling of PET and SPECT as well as CT and MR data directly at the acquisition console.

The MI View&GO software integrates molecular imaging more efficiently in the clinical environment by providing an interface for its users to review, post-process and read medical images immediately after acquisition. The purpose of the MI View&GO is to allow the technologist and reading physician to:

Review acquired and reconstructed images at the scanner console
Determine that the acquired data is of sufficient quality for reading, so the patient can be released.
Prepare images for reading
Perform a basic read

AI/ML Overview

Here's an analysis of the acceptance criteria and study detailed in the provided FDA 510(k) clearance letter for MI View&GO, structured according to your requested points:

Acceptance Criteria and Device Performance Study for MI View&GO (K254016)

1. Acceptance Criteria and Reported Device Performance

Acceptance Criteria Category	Specific Acceptance Criteria	Reported Device Performance
Improved Lung Segmentation (Auto Lung 3D)	For new organs (N/A for lung lobes, as they are existing organs with improved models)	Not applicable, as lung lobes are "improved organs," not "new organs."
	For unchanged organs (other than lungs and lung lobes)	Dice-score on other organs (not retrained) remained unchanged and was verified by recalculating the Dice score with the new algorithm.
	For improved organs (Lung Lobes): Average Dice coefficient per organ shall be greater than or equal to the average Dice coefficient per organ of the predicate algorithm.	The average Dice coefficient for all 20 subjects was higher for each lobe in the subject device than in the predicate device. (Note: The document also states "although not greater than a +0.03 difference for all lobes," which clarifies that while improved, the improvement might not be substantial for every lobe.)
Improved PERCIST Liver Algorithm (binary liver mask input)	Average Dice coefficient > 0.8	The liver met this criterion.
	Average Symmetric Surface Distance (ASSD) < 10 mm	The liver met this criterion.
Improved PERCIST Liver Algorithm (Reference Region Placement)	N/A (Comparative analysis, not a specific criterion for a single metric)	Demonstrated to yield results in better agreement with semi-automatic evaluation by expert readers compared with the predicate method.
Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks)	N/A (Comparative analysis, goal is fewer intersections)	Subject device had fewer intersections (4 cases) compared to the predicate device (13 cases) out of 129 subjects.

2. Sample Size Used for the Test Set and Data Provenance

Improved Lung Segmentation:
- Sample Size: 20 patients.
- Data Provenance:
  - Retrospective.
  - Half of the patients were new, and the other 50% were randomly selected from the predicate testing cohort.
  - 50% of patients were from the US.
  - All patients from Siemens Scanner.
Improved PERCIST Liver Algorithm (binary liver mask input):
- Sample Size: 20 patients.
- Data Provenance:
  - Patients obtained from clinical partners in Europe and USA.
  - Randomly selected with stratification.
  - All subjects from Siemens Scanner.
Improved PERCIST Liver Algorithm (Reference Region Placement & Intersection with Suspicious Uptake Masks):
- Sample Size: 129 subjects for the "intersection with suspicious uptake masks" analysis.
- Data Provenance: Not explicitly stated for the "reference region placement" analysis, but implied to be from the same or similar source as the 129 subjects.

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

Improved Lung Segmentation: Not explicitly stated. The ground truth for segmentation metrics (Dice, ASSD) is typically established by manual segmentation performed by experts, but the number of experts and their qualifications are not detailed in this document.
Improved PERCIST Liver Algorithm (Reference Region Placement):
- Number of Experts: Two expert readers.
- Qualifications: "Expert readers" is mentioned, but specific qualifications (e.g., radiologist 10 years experience) are not provided.
Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks):
- Number of Experts: One expert reader.
- Qualifications: "Expert reader" is mentioned; specific qualifications are not provided.

4. Adjudication Method for the Test Set

Improved Lung Segmentation: Not explicitly mentioned. For segmentation ground truth derived from multiple experts, methods like consensus or averaging are common, but not specified here.
Improved PERCIST Liver Algorithm (Reference Region Placement): Semi-automatic evaluation by two expert readers. The document states the subject device algorithm was compared to this "reference standard," implying this semi-automatic output was considered the ground truth. No explicit adjudication method (like 2+1) is described for resolving differences between the two experts, if they occurred.
Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks): Identified by "an expert reader." This implies a single expert's identification served as the ground truth. No adjudication mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No, a formal MRMC comparative effectiveness study involving human readers with and without AI assistance is not described in this document.
The studies conducted focus on the algorithm's performance against historical data, expert interpretations, or comparing an improved algorithm to a predicate algorithm.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Study was Done

Yes, standalone performance studies were conducted for specific features:
- Improved Lung Segmentation: The Dice coefficient and ASSD evaluation was a standalone algorithmic performance assessment against presumed expert-derived ground truth.
- Improved PERCIST Liver Algorithm (binary liver mask input): The Dice coefficient and ASSD evaluation for the liver mask was a standalone algorithmic performance assessment.
- Improved PERCIST Liver Algorithm (Reference Region Placement): The comparison of the algorithm's results to the semi-automatic evaluation by two expert readers is a standalone algorithm assessment, where the expert input constitutes the ground truth.
- Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks): This was a standalone algorithmic evaluation of how often the algorithm's PERCIST VOIs intersected suspicious uptake areas identified by an expert.

7. The Type of Ground Truth Used

Improved Lung Segmentation: Likely expert consensus/manual segmentation (implied by Dice coefficient and ASSD, which compare algorithm output to a gold standard segmentation).
Improved PERCIST Liver Algorithm (binary liver mask input): Likely expert consensus/manual segmentation (implied by Dice coefficient and ASSD for the liver mask).
Improved PERCIST Liver Algorithm (Reference Region Placement): Expert semi-automatic evaluation from two expert readers. These semi-automatic outputs were treated as the reference standard.
Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks): Expert identification of suspicious tracer uptake masks by a single expert reader.

8. The Sample Size for the Training Set

Not explicitly stated in the document. The document mentions that the lung lobe segmentation algorithm was "re-trained with additional data" and that there was "No overlap of patients between training, tuning, and test cohorts," but does not provide details on the training set's size.

9. How the Ground Truth for the Training Set Was Established

Not explicitly stated in the document. For machine learning models, ground truth for training data is typically established through expert labeling (e.g., manual segmentation, disease annotation), but the specifics are not provided here.

Summary

FDA 510(k) Clearance Letter - MI View&GO

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

Doc ID # 04017.08.03

February 10, 2026

Siemens Medical Solutions USA, Inc.
Brian Wui
Regulatory Affairs
2501 N. Barrington Rd.
Hoffman Estates, Illinois 60192

Re: K254016
Trade/Device Name: MI View&GO
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical image management and processing system
Regulatory Class: Class II
Product Code: QIH
Dated: February 5, 2026
Received: February 5, 2026

Dear Brian Wui:

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.

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K254016 - Brian Wui Page 2

Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

Your device is also subject to, among other requirements, the Quality Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13484 clause 8.3 (Nonconforming product), and ISO 13485 clause 8.5 (Corrective and preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 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 Management System Regulation (QMSR) (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.

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-

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K254016 - Brian Wui Page 3

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. K254016

Please provide the device trade name(s). MI View&GO

Please provide your Indications for Use below.

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

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510(k) Summary

K254016

1. Identification of the Submitter

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

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

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

2. Device Name and Classification

Product Trade Name: MI View&GO
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: MI View&GO
510(k) Number: K242300
Clearance Date: 08/02/2024
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

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Predicate / Reference Device
Trade Name: syngo.via MI Workflows
510(k) Number: K251528
Clearance Date: 05/19/2025
Common Name: Medical image management and processing system
Classification Name: Automated Radiological Image Processing Software
Classification Panel: Radiology
CFR Section: 21 CFR §892.5050
Device Class: Class II
Product Code: QIH

4. Device Description

Review acquired and reconstructed images at the scanner console
Determine that the acquired data is of sufficient quality for reading, so the patient can be released.
Prepare images for reading
Perform a basic read

5. Indications for Use

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.

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7. Comparison of Technological Characteristics with the Predicate Device

MI View&GO with software version VA40 software provides 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.

MI View&GO VA40:

Features
- General Masking Tool
- VQ Ratio and Subtraction Images for Auto Lung 3D
- Improved Lung Segmentation
- Deauville Score
- Improved PERCIST Liver algorithm
Improvements to Layout and Usability
- Cardiac Layout Enhancement
- Improvements to Layout Editing and User Presets

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.

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Software Verification and Validation

'Enhanced' software documentation per FDA's guidance document "Content of Premarket Submissions for Device Software Functions" issued on 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 predetermined 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.

Performance Testing

The device under application, MI View&GO (K254061), deploys the same improved Lung Segmentation and improved PERCIST Liver algorithm as cleared in the predicate / reference device syngo.via VC10 (K251528).

Improved Lung 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 Auto Lung 3D.

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

Acceptance Criteria for organ segmentation:

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.

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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.

Improved PERCIST Liver algorithm

Additionally, performance evaluation was conducted for the improved PERCIST Liver algorithm.

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.

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 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 algorithm is detailed below.

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

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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.

Risk Analysis

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 predetermined acceptance criteria. Testing for verification and validation of the device was found acceptable to support the claims of substantial equivalence.

Standards

Siemens hereby certifies that MI View&GO meets the following FDA Recognized Consensus standards listed below:

Designation Number and Edition/Date	Title	Standards Development Organization	Recognition Number
PS 3.1 - 3.20 2023e	Digital Imaging and Communications in Medicine (DICOM) Set	NEMA	12-352
62304 Edition 1.1 2015-06 CONSOLIDATED VERSION	Medical Device Software –Software Life Cycle Processes	AAMI, ANSI, IEC	13-79
14971 Third Edition 2019-12	Medical devices – Application of risk management to medical devices	ISO	5-125
62366-1 Edition 1.1 2020-06 CONSOLIDATED VERSION	Medical devices - Part 1: Application of usability engineering to medical devices	AAMI, ANSI, IEC	5-129
15223-1 Fourth edition 2021-07	Medical devices - Symbols to be used with medical device labels, labelling, and information to be supplied - Part 1: General requirements	ISO	5-134

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| 20417 First edition 2021-04 Corrected version 2021-12 | Medical devices – Information to be supplied by the manufacturer | ISO | 5-135 |

Conclusion

There are no differences in the Indications for Use, Intended Use, or Fundamental Technological Characteristics of the MI View&GO VA40 software as compared to the currently commercially available MI View&GO VA30 software (K242300).

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. Based on this information, as well as the documentation in support of the modifications, it is Siemens' opinion that the MI View&GO software—with the modifications outlined in this application—is substantially equivalent to the predicate device.

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).