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This computed tomography system is intended to generate and process cross-sectional images of patients by computer reconstruction of x-ray transmission data. The images delivered by the system can be used by a trained physician as an aid in diagnosis.
The images delivered by the system can be used by trained staff as an aid in diagnosis, treatment preparation and radiation therapy planning.
This CT system can be used for low dose lung cancer screening in high risk populations. High risk populations are 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 in-room scan application is a planning and information system designed to perform the necessary functions required for planning and controlling scans of supported SIEMENS CT scanners. It allows users to work in close proximity to the scanner.
The in-room scan application runs on standard information technology hardware and software, utilizing the standard information technology operating systems and user interface. Communication and data exchange are done using special protocols

The SOMATOM go.Platform is comprised of the following 4 CT scanners and optional mobile workflow:

• SOMATOM go.Up
• SOMATOM go.Now
• SOMATOM go.Top
• SOMATOM go.All
• Scan&GO Mobile Medical Application (optional mobile workflow component)

The CT scanners feature one continuously rotating tube-detector system and function according to the fan beam principle. The system software is a command-based program used for patient management, data management, X-ray scan control, image reconstruction, and image archive/evaluation. The above referenced CT scanners produce CT images in DICOM format, which can be used by trained staff for post-processing applications commercially distributed by Siemens and other vendors as an aid in diagnosis and treatment preparation. The computer system delivered with the CT scanner is able to run optional post processing applications.

The Scan&GO mobile workflow is an optional planning and information software designed to perform the necessary functions required for planning and controlling of the workflow of the SOMATOM go.Platform CT scanners. Scan&GO can be operated on a Siemens provided tablet or a commercially available that meets certain minimum technical requirements. It allows users to work in close proximity to the scanner and the patient.

The provided text describes the acceptance criteria and supporting studies for the Siemens SOMATOM go.Platform CT scanners (SOMATOM go.All, SOMATOM go.Top, SOMATOM go.Now, SOMATOM go.Up) and the Scan&GO mobile workflow application.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document describes general "acceptance criteria" for software specifications and "pre-determined acceptance criteria" for customer use testing, but it does not provide specific quantitative acceptance criteria or reported performance values for clinical metrics. Instead, it emphasizes that testing demonstrated substantial equivalence to predicate devices and conformance to various performance, safety, and regulatory standards.

Therefore, a table with specific acceptance criteria and reported numeric performance cannot be generated from the given text. The text indicates that:

• "All test performed meet the pre-determined acceptance criteria."
• "The test results show that all of the software specifications have met the acceptance criteria."
• "The data included in this submission demonstrates that the SOMATOM go.Platform performs comparably to the predicate devices currently marketed for the same intended use."

This implies that the acceptance criteria were qualitative (e.g., "operates as intended," "comparable to predicate") or met internal Siemens specifications not detailed in this public summary.

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

The document primarily focuses on non-clinical testing.

• Test Set Description: Phantom tests, verification and validation testing, software testing, electrical safety and EMC testing, wireless coexistence testing, and customer use testing.
• Sample Size: Not explicitly stated for any of the non-clinical tests.
• Data Provenance:
  • Phantom Tests: Conducted by Siemens during product development.
  • Customer Use Tests:
    • Internal Clinical Use Test: Simulated in Siemens Test Cabins with "customers with clinical expertise."
    • External Clinical Use Test: Performed with "selected customer" in a "clinic/hospital environment."
  • Additional Supportive Data (Lung Cancer Screening): Refers to the National Lung Screening Trial (NLST) (N Engl J Med 2011; 365:395-409) which was a randomized trial.
    • NLST Sample Size: Not explicitly stated in this document, but the external reference (N Engl J Med 2011; 365:395-409) would contain this information.
    • NLST Data Provenance: Prospective, multi-center trial (implied by "National" and publication in a journal like NEJM). Information regarding country of origin is not explicitly stated in this document but is generally associated with the US for NLST.

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

• Non-clinical Tests: Ground truth for non-clinical tests (e.g., phantom images, software specifications) would typically be established by engineering and quality assurance teams based on design specifications and measurements, rather than clinical experts. No specific number or qualifications of "experts" are provided for these internal assessments.
• Customer Use Testing: "Customers with clinical expertise" were invited for internal clinical use tests. "Selected customer" (plural, implying multiple individuals or sites) performed external clinical use tests. No specific number or qualifications (e.g., "radiologist with 10 years experience") are provided.
• NLST (for lung cancer screening indication): The NLST identified lung nodules of 4mm diameter or greater. The ground truth for this large-scale clinical trial would involve extensive processes for diagnosis, follow-up, and potentially pathology, but the specific number and qualifications of experts involved in establishing ground truth for the NLST data itself are not detailed in this submission. The submission references the published literature for NLST for further information.

4. Adjudication Method for the Test Set

• Non-clinical Tests: Not explicitly stated. For engineering and software testing, adjudication would likely involve issue tracking systems and resolution processes, rather than clinical consensus.
• Customer Use Testing: Not explicitly stated. It states that "All test performed meet the pre-determined acceptance criteria," implying that the results were simply assessed against those criteria.
• NLST (for lung cancer screening indication): The adjudication methods for the NLST (referencing N Engl J Med 2011; 365:395-409) would be detailed in that study's methodology, but are not described in this 510(k) summary.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done, If So, What was the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance

• No, an MRMC comparative effectiveness study involving AI assistance for human readers was not done.
• This submission describes a CT scanner system and its optional mobile workflow application, not an AI-based diagnostic aid that assists human readers. The technologies described are fundamental CT imaging and workflow enhancements.
• The NLST is referenced to support the ability of CT systems (not necessarily this specific model or an AI component) to perform low-dose lung cancer screening, but it is not a study comparing human readers with and without AI assistance.

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

• No, a standalone algorithm performance study was not specifically done in the context of AI without human-in-the-loop.
• The device being cleared is a Computed Tomography (CT) system and an accompanying mobile workflow application. These are imaging devices and tools for workflow, not standalone diagnostic algorithms.
• The "software" mentioned (SOMARIS/10 syngo CT VA20) refers to the operating system and processing capabilities of the CT scanner itself, including basic post-processing and interfaces for advanced post-processing, not a standalone diagnostic algorithm for interpretation.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• Non-clinical (Technical/Performance) Testing: Ground truth for these tests would likely be based on:
  • Design specifications: For software functionality and hardware performance (e.g., compliance with IEC standards).
  • Measurements against known standards/phantoms: For image quality, radiation dose, electrical safety.
• Customer Use Testing: Ground truth would be the intended performance and user experience as validated by clinical users, ensuring the system operates as expected in a clinical context.
• Lung Cancer Screening Indication: The justification for the low-dose lung cancer screening indication comes from clinical literature, specifically referencing the National Lung Screening Trial (NLST). For NLST, the ground truth for lung cancer detection would ultimately be pathology and long-term outcomes data (mortality reduction). The submission states NLST's interpretation task was to detect lung nodules ≥ 4mm.

8. The Sample Size for the Training Set

• Not applicable. This submission is for a CT scanner platform and a workflow application, not a machine learning or AI algorithm in the context of diagnostic image interpretation that would require a dedicated "training set" for model development. The software mentioned handles system operation, image reconstruction, and basic post-processing, and is not described as involving a machine learning training phase.

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

• Not applicable. As no training set for a machine learning algorithm is discussed, the establishment of its ground truth is not relevant here.

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This computed tomography system is intended to generate and process cross-sectional images of patients by computer reconstruction of x-ray transmission data. The images delivered by the system can be used by a trained physician as an aid in diagnosis. This CT system can be used for low dose lung cancer screening in high risk populations. High risk populations are 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 Siemens SOMATOM go. Platform is comprised of 2 Computed Tomography (CT) Scanner Systems, SOMATOM go.Now and SOMATOM go.Up. These CT scanners feature one continuously rotating tube-detector system and function according to the fan beam principle. The system software is a command-based program used for patient management, data management, X-ray scan control, image reconstruction, and image archive/evaluation. The SOMATOM go.Now and SOMATOM go.Up scanners produce CT images in DICOM format, which can be used by trained staff for post-processing applications commercially distributed by Siemens and other vendors as an aid in diagnosis and treatment preparation. The computer system delivered with the CT scanner is able to run optional post processing applications.

The provided document describes the Siemens Somatom Go.up and Somatom Go.now Computed Tomography (CT) systems. The focus of the performance data section is on non-clinical testing, verification and validation of hardware and software modifications, and compliance with various standards. There is no specific study described that establishes detailed acceptance criteria for diagnostic performance outcomes (e.g., sensitivity, specificity for a particular disease) or includes human readers evaluating images from the new CT systems in comparison to ground truth.

Here's the information extracted from the document:

1. A table of acceptance criteria and the reported device performance:

The document does not provide a table with specific acceptance criteria for diagnostic performance (e.g., sensitivity, specificity, accuracy) alongside reported performance values for those metrics. Instead, the acceptance criteria relate to compliance with regulatory standards, successful completion of verification and validation testing, and comparability to predicate devices.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

• Test Set Sample Size: Not explicitly stated for specific diagnostic performance. The document refers to "phantom tests" and "verification and validation testing" without quantifying these test sets in terms of patient data or number of phantoms used in a way that suggests a diagnostic efficacy study.
• Data Provenance: The document emphasizes non-clinical testing (integration, functional, and phantom tests) and verification and validation of the device's hardware and software. It does not describe a clinical study with a patient test set. The National Lung Screening Trial (NLST) is cited as "Additional Supportive Data" to justify the indication for use for low-dose lung cancer screening, not as a direct performance study of the Somatom Go.up/Go.now CT systems themselves. The NLST was conducted in the USA (National Cancer Institute).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

Not applicable. The document describes non-clinical testing and general software/hardware validation. It does not detail a study where expert radiologists established a ground truth for a test set of images acquired by these specific new CT devices for diagnostic performance evaluation.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

Not applicable. No diagnostic performance study with expert adjudication is described for these specific CT systems.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

No MRMC study is mentioned. This device is a CT scanner, not an AI-powered CADe/CADx device that assists human readers.

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

Not applicable. This is a CT scanner, not a standalone AI algorithm. While the device produces images, its performance validation focuses on technical specifications, image quality via phantoms, and substantial equivalence to predicate CT systems, not standalone diagnostic performance of an interpretive algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):

For the core technical and safety validation described:

• Non-clinical testing: Ground truth is against design specifications, physical measurements, and compliance with industry standards.
• Phantom tests: Ground truth is against known phantom characteristics (e.g., density, spatial resolution targets).
• Software V&V: Ground truth is against software specifications and risk analysis.

The NLST, cited for the lung cancer screening indication, relied on biopsy/pathology and clinical follow-up for its ground truth regarding cancer presence. However, this was for the general effectiveness of low-dose CT screening, not a performance evaluation of the specific Siemens devices.

8. The sample size for the training set:

Not applicable. The document describes the marketing clearance for a CT imaging system. CT imaging systems do not typically have "training sets" in the artificial intelligence sense.

9. How the ground truth for the training set was established:

Not applicable.

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