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The Siemens Biograph systems are combined X-Ray Computed Tomography (CT) and Positron Emission Tomography (PET) scanners that provide registration and fusion of high resolution physiologic and anatomic information.

The CT component produces cross-sectional images of the body by computer reconstruction of X-Ray transmission data from either the same axial plane taken at different angles or spiral planes taken at different angles. The PET subsystem images and measures the distribution of PET radiopharmaceuticals in humans for the purpose of determining various metabolic (molecular) and physiologic functions within the human body and utilizes the CT for fast attenuation correction maps for PET studies and precise anatomical reference for the fused PET and CT images.

The system maintains independent functionality of the CT and PET devices, allowing for single modality CT and/or PET diagnostic imaging.

These systems are intended to be utilized by appropriately trained health care professionals to aid in detecting, localizing, diagnosing, staging, and restaging of lesions, tumors, disease, and organ function for the evaluation of diseases and disorders such as, but not limited to, cardiovascular disease, neurological disorders, and cancer. The images produced by the system can also be used by the physician to aid in radiotherapy treatment planning and interventional radiology procedures.

This CT system can be used for low dose lung cancer screening in high risk populations. *

• As defined by professional medical societies. Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365; 395-409) and subsequent literature, for further information.

The Biograph Vision and Biograph mCT PET/CT systems are combined multi-slice X-Ray Computed Tomography and Positron Emission Tomography scanners. These systems are designed for whole-body oncology, neurology and cardiology examinations. The Biograph Vision and Biograph mCT systems provide registration and fusion of high-resolution metabolic and anatomic information from the two major components of each system (PET and CT). Additional components of the system include a patient handling system and acquisition and processing workstations with associated software.

Biograph Vision and Biograph mCT software is a command-based program used for patient management, data management, scan control, image reconstruction and image archival and evaluation. All images conform to DICOM imaging format requirements.

The software for the Biograph Vision and Biograph mCT systems, which are the subject of this application, is substantially equivalent to the commercially available Biograph Vision and Biograph mCT software.

• Somaris Software (cleared in K230421)
  • Upgrade to the latest revision of Somaris Software (Somaris/7 syngo CT VB30) with modified software features:
    • FAST Bolus
    • FAST 4D
    • FAST Applications (FAST Spine, FAST Planning)
    • Automatic Patient Instructions
    • Additional default exam protocols
    • Additional kV setting for Tin Filtration
• PETsyngo software
  • SMART Image Framer (available for Vision 600 and X models only – cleared in K223547)
• Updated computer hardware due to obsolescence issues (cleared in K230421). These changes do not affect system performance characteristics and have no impact on safety or effectiveness.

The Biograph Vision may also use the names Biograph Vision Quantum and Peak for marketing purposes.

Here's an analysis of the provided FDA 510(k) clearance letter for Siemens Biograph Vision and mCT PET/CT Systems, focusing on acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document describes the performance of the updated software (VG85) for the Siemens Biograph Vision and Biograph mCT PET/CT Systems, comparing it to the predicate device (VG80). The "Acceptance Criteria" for the subject device are explicitly stated as "Same" as the predicate device's performance values. This implies that the updated system must perform at least as well as the predicate device across all tested metrics.

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

The document does not specify a "sample size" in terms of number of patient cases for the test set. Instead, it states:

• Test Set: The testing was "PET Testing in accordance with NEMA NU2-2018." NEMA NU2-2018 is a standard for performance measurements of PET scanners, which involves phantom studies, not human patient data.
• Data Provenance: The data provenance is from phantom studies conducted to NEMA NU2-2018 standards on the Siemens Biograph Vision and Biograph mCT systems. This is non-clinical testing. The document does not indicate country of origin or whether it was retrospective/prospective, as it's a controlled laboratory environment with phantoms.

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

This information is not applicable and therefore not provided in the document. Since the testing was conducted on phantoms according to NEMA NU2-2018 standards, the "ground truth" is defined by the known physical properties and configurations of the phantoms, as well as the standardized measurement protocols. There are no human experts involved in establishing a diagnostic "ground truth" for these types of physical performance measurements.

4. Adjudication Method for the Test Set

This information is not applicable and therefore not provided in the document. Adjudication methods like "2+1" or "3+1" are used in studies where human readers are interpreting images and there's a need to resolve discrepancies in their evaluations to establish a consensus ground truth. Since this study involved non-clinical phantom testing, such adjudication was not necessary.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done, What was the effect size of how much human readers improve with AI vs without AI assistance?

No, an MRMC comparative effectiveness study was not done. The document explicitly states: "Clinical testing was not conducted for this submission." This submission focuses on the hardware and software's physical performance characteristics and its substantial equivalence to a predicate device, not on clinical utility or an AI's impact on human reader performance.

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

The document describes the performance of the PET/CT system's hardware and software components. It's a "standalone" performance study in the sense that it measures the inherent physical and technical capabilities of the device (e.g., resolution, sensitivity, image quality) without human diagnostic interpretation being part of the measurement. However, it's critical to clarify that this is not a standalone AI algorithm performance study as typically understood in the context of diagnostic AI. The "Somaris Software" and "PETsyngo software" are operational control and image reconstruction software for the PET/CT system itself, not necessarily an "AI" in the diagnostic sense that would report findings for human review.

7. The Type of Ground Truth Used

The ground truth used for this testing was physical phantom data with known properties. The NEMA NU2-2018 standard specifies phantoms with defined sizes, activity concentrations, and geometric configurations, allowing for objective measurement of the scanner's performance parameters (e.g., resolution, sensitivity, image quality spheres).

8. The Sample Size for the Training Set

This information is not provided and is likely not applicable in the context of this 510(k) submission. The document describes updates to existing established software and hardware for a medical imaging device. While software development for such systems may involve extensive internal testing and validation, a "training set" with established ground truth as used for machine learning models is not typically a concept applied to the fundamental operational and reconstruction software of a PET/CT scanner being cleared under these circumstances. The emphasis is on meeting performance standards (NEMA NU2-2018) and demonstrating substantial equivalence.

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

As stated above, a "training set" in the machine learning sense is likely not applicable. For the development and verification of the system's operational and reconstruction software, the "ground truth" would have been established through methodologies typical for medical device software engineering, including:

• Reference data/simulations: Utilizing known physical models, simulations, and calibrated reference data to test reconstruction algorithms and system functionality.
• Engineering specifications: Ensuring the software's output aligns with predefined technical specifications and physics principles governing PET/CT image formation.
• Phantom studies (internal development): Similar to the NEMA NU2-2018 testing, phantom studies would be used extensively during development to refine algorithms and establish performance characteristics.

The specific details of the software's internal development and validation, including how any "training" (if applicable for specific algorithmic components, though not explicitly an AI for diagnosis) was performed, are not part of this public 510(k) summary.

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