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syngo.via View&GO is indicated for image rendering and post-processing of DICOM images to support the interpretation in the field of radiology, nuclear medicine and cardiology.

syngo.via View&GO is a software-only medical device, which is delivered by download to be installed on common IT hardware. This hardware has to fulfil the defined requirements. Any hardware platform that complies to the specified minimum hardware and software requirements and with successful installation verification and validation activities can be supported. The hardware itself is not seen as part of the medical device syngo.via View&GO and therefore not in the scope of this 510(k) submission.

syngo.via View&GO provides tools and features to cover the radiological tasks preparation for reading, reading images and support reporting. syngo.via View&GO supports DICOM formatted images and objects.

syngo.via View&GO is a standalone viewing and reading workplace. This is capable of rendering the data from the connected modalities for the post processing activities. syngo.via View&GO provides the user interface for interactive image viewing and processing with a limited short-term storage which can be interfaced with any Long-term storage (e.g. PACS) via DICOM syngo.via View&GO is based on Microsoft Windows operating systems.

syngo.via View&GO supports various monitor setups and can be adapted to a range of image types by connecting different monitor types.

The provided text is a 510(k) Summary for the Siemens Healthcare GmbH device "syngo.via View&GO" (Version VA30A). This document focuses on demonstrating substantial equivalence to a predicate device (syngo.via View&GO, Version VA20A) rather than presenting a detailed study of the device's performance against specific acceptance criteria for a novel algorithm.

The document states that the software is a Medical Image Management and Processing System, and its purpose is for "image rendering and post-processing of DICOM images to support the interpretation in the field of radiology, nuclear medicine and cardiology." It specifically states, "No automated diagnostic interpretation capabilities like CAD are included. All image data are to be interpreted by trained personnel."

Therefore, the provided text does not contain the information requested regarding acceptance criteria and a study proving an algorithm meets those criteria for diagnostic performance. It does not describe an AI/ML algorithm or its associated performance metrics.

However, based on the provided text, I can infer some aspects and highlight what information is missing if this were an AI-driven diagnostic device.

Here's an analysis based on the assumption that if this were an AI-based device, these fields would typically be addressed:

Summary of Device Performance (Based on provided text's limited scope for a general medical image processing system):

Since "syngo.via View&GO" is a medical image management and processing system without automated diagnostic interpretation capabilities, the acceptance criteria and performance data would revolve around its functionality, usability, and safety in handling and presenting medical images. The provided text primarily establishes substantial equivalence based on the lack of significant changes in core functionality and the adherence to relevant standards for medical software and imaging.

1. Table of acceptance criteria and the reported device performance:

The document doesn't provide a table of performance metrics for an AI algorithm. Instead, it describes "Non-clinical Performance Testing" focused on:

• Conformance to standards (DICOM, JPEG, ISO 14971, IEC 62304, IEC 82304-1, IEC 62366-1, IEEE Std 3333.2.1-2015).
• Software verification and validation (demonstrating continued conformance with special controls for medical devices containing software).
• Risk analysis and mitigation.
• Cybersecurity requirements.
• Functionality of the device (as outlined in the comparison table between subject and predicate device).

Reported Performance/Findings (General):

• "The testing results support that all the software specifications have met the acceptance criteria."
• "Testing for verification and validation for the device was found acceptable to support the claims of substantial equivalence."
• "Results of all conducted testing were found acceptable in supporting the claim of substantial equivalence."
• The device "does not introduce any new significant potential safety risks and is substantially equivalent to and performs as well as the predicate device."

Example of what a table might look like if this were an AI algorithm, along with why it's not present:

2. Sample size used for the test set and the data provenance:

• Not explicitly stated for diagnostic performance, as the device does not have automated diagnostic capabilities.
• The software verification and validation activities would involve testing with various DICOM images to ensure proper rendering and processing. The exact number of images or datasets used for these software tests is not detailed.
• Data Provenance: Not specified, as it's not a clinical performance study.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable / Not stated. Ground truth for diagnostic accuracy is not established for this device, as it does not perform automated diagnosis. The ground truth for software functionality would be the expected behavior of the software according to its specifications.

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

• Not applicable. No clinical adjudication method is described, as this is neither a clinical study nor an AI diagnostic device.

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 study was NOT done/described. The device explicitly states it has "No automated diagnostic interpretation capabilities like CAD are included. All image data are to be interpreted by trained personnel." Therefore, it does not offer AI assistance for diagnosis.

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

• Not applicable. The device is a "Medical Image Management and Processing System" that provides tools for human interpretation; it is not a standalone diagnostic algorithm.

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

• Not applicable for diagnostic purposes. For software functionality, the ground truth is the defined behavior as per the software specifications and design.

8. The sample size for the training set:

• Not applicable/Not stated. The document explicitly states for the "Imaging algorithms" section that "No re-training or change in algorithm models was performed." This implies that the algorithms are traditional image processing algorithms, not machine learning models that require training data in the context of diagnostic AI. If there were any minor algorithmic adjustments, the training data for such classical algorithms is typically the mathematical formulation itself rather than a dataset of clinical cases for machine learning.

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

• Not applicable. As indicated above, there is no mention of "training" in the context of machine learning. The algorithms are described as undergoing "bug-fixing and minor improvements" but no "re-training or change in algorithm models."

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syngo.via RT Image Suite is a 3D and 4D image visualization, multi-modality manipulation and contouring tool that helps the preparation of treatments such as, but not limited to those performed with radiation (for example, Brachytherapy, Particle Therapy, External Beam Radiation Therapy).

It provides tools to view existing contours, create, edit, modify, copy contours of regions of the body, such as but not limited to, skin outline, targets and organs-at-risk. It also provides functionalities to create simple geometric treatment plans. Contours, images and treatment plans can subsequently be exported to a Treatment Planning System.

The software combines the following digital image processing and visualization tools:

• . Multi-modality viewing and contouring of anatomical, and multi-parametric images such as but not limited to CT, PET, PET/CT, MRI, Linac CBCT images
• Multiplanar reconstruction (MPR) thin/thick, minimum intensity projection (MIP), volume rendering technique (VRT)
• . Freehand and semi-automatic contouring of regions-of-interest on any orientation including oblique
• Automated Contouring on CT images
• . Creation of contours on images supported by the application without prior assignment of a planning CT
• Manual and semi-automatic registration using rigid and deformable registration ●
• . Supports the user in comparing, contouring, and adapting contours based on datasets acquired with different imaging modalities and at different time points
• . Supports multi-modality image fusion
• . Visualization and contouring of moving tumors and organs
• Management of points of interest including but not limited to the isocenter ●
• . Creation of simple geometric treatment plans
• Generation of a synthetic CT based on multiple pre-define MR acquisitions ●

The subject device with the current software version SOMARIS/8 VB70 is an image analysis software for viewing, manipulation, 3D and 4D visualization, comparison of medical images from multiple imaging modalities and for the segmentation of tumors and organs-at-risk, prior to dosimetric planning in radiation therapy. syngo.via RT Image Suite combines routine and advanced digital image processing and visualization tools for manual and software assisted contouring of volumes of interest, identification of points of interest, sending isocenter points to an external laser system, registering images and exporting final results. syngo.via RT Image Suite supports the medical professional with tools to use during different steps in radiation therapy case preparation.

For the current software version SOMARIS/8 VB70 the following already cleared features have been modified:

• Patient Marking
• Contouring
• 4D Features ●
• Basic Features of the subject device ●

The provided text describes the acceptance criteria and a study demonstrating that the lobe-based lung ventilation algorithm within the syngo.via RT Image Suite meets these criteria.

Here's the breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

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syngo.CT Applications is a set of software applications for advanced visualization, measurement, and evaluation for specific body regions.

This software package is designed to support the radiologists and physicians from emergency medicine, specialty care, urgent care, and general practice e.g. in the:

• · Evaluation of perfusion of organs and tumors and myocardial tissue perfusion
• · Evaluation of bone structures and detection of bone lesions
• · Evaluation of CT images of the heart
• · Evaluation of the coronary lesions
• · Evaluation of the mandible and maxilla
• · Evaluation of dynamic vessels and extended phase handling
• · Evaluation of the liver and its intrahepatic vessel structures to identify the vascular territories of sub-vessel systems in the liver
• · Evaluation of neurovascular structures
• Evaluation of the lung parenchyma
• · Evaluation of non-enhanced Head CT images
• · Evaluation of vascular lesions

The syngo.CT Applications are syngo based post-processing software applications to be used for viewing and evaluating CT images provided by a CT diagnostic device and enabling structured evaluation of CT images.

The syngo.CT Applications is a combination of thirteen (13) former separately cleared medical devices which are now handled as features / functionalities within syngo.CT Applications. These functionalities are combined unchanged compared to their former cleared descriptions; however, some minor enhancements and improvements are made for the application syngo.CT Pulmo 3D only.

The provided document is a 510(k) summary for syngo.CT Applications, which is a consolidation of thirteen previously cleared medical devices. The document explicitly states that "The testing supports that all software specifications have met the acceptance criteria" and "The result of all testing conducted was found acceptable to support the claim of substantial equivalence." However, it does not explicitly define specific acceptance criteria (e.g., target accuracy, sensitivity, specificity values) for the device's performance or detail the specific studies that prove these criteria are met. Instead, it relies on the premise that the functionalities remain unchanged from the previously cleared predicate devices, with only minor enhancements to one application (syngo.CT Pulmo 3D).

Therefore, based on the provided text, I cannot fill in precise quantitative values for acceptance criteria or specific study results for accuracy, sensitivity, or specificity. The information provided heavily emphasizes software verification and validation, risk analysis, and adherence to consensus standards, rather than detailing a comparative effectiveness study or standalone performance metrics against a defined ground truth.

Here's a breakdown of the available information and what is missing:

1. Table of acceptance criteria and the reported device performance:

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

• Sample Size for Test Set: Not specified in the document.
• Data Provenance: Not specified in the document.

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):

• Number of Experts: Not specified in the document.
• Qualifications of Experts: Not specified in the document.

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

• Adjudication Method: Not specified in the document.

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:

• MRMC Study Done: No. The document does not mention any MRMC comparative effectiveness study where human readers' performance with and without AI assistance was evaluated. The submission focuses on the consolidation of existing, cleared applications.
• Effect Size of Improvement: Not applicable, as no MRMC study is reported.

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

• Standalone Study Done: Yes, implicitly. The document states, "The testing supports that all software specifications have met the acceptance criteria," suggesting that the software's performance was verified and validated independent of human interpretation to ensure its functionalities (visualization, measurement, evaluation) behave as intended. However, specific metrics (e.g., accuracy of a measurement tool compared to a gold standard) are not provided. The phrase "algorithm only" might not be fully accurate here given the device is a visualization and evaluation tool for human use, not an autonomous diagnostic AI.

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

• Type of Ground Truth: Not explicitly specified. Given the nature of visualization and evaluation tools, it would likely involve comparisons to known values, measurements, or expert-reviewed datasets, but the document does not detail this.

8. The sample size for the training set:

• Training Set Sample Size: Not applicable/Not specified. The document describes the device as a consolidation of existing, cleared software applications with "minor enhancements and improvements" only to syngo.CT Pulmo 3D. It does not indicate that new machine learning models requiring large training sets were developed for this specific submission; rather, it refers to the performance of existing, cleared applications.

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

• How Ground Truth for Training Set was Established: Not applicable/Not specified, for the same reasons as point 8. The document does not describe a new AI model training process for this submission.

Summary of Device Rationale:

The core of this 510(k) submission is the consolidation of thirteen previously cleared syngo.CT applications into a single "syngo.CT Applications" product. The applicant, Siemens Medical Solutions USA, Inc., states that the functionalities within this combined product are "unchanged compared to their former cleared descriptions" with only "minor enhancements and improvements" in syngo.CT Pulmo 3D (specifically regarding color assignments for lobe borders).

The document asserts that "The performance data demonstrates continued conformance with special controls for medical devices containing software." It also states, "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 acceptance criteria. Testing for verification and validation of the device was found acceptable to support the claims of substantial equivalence."

This implies that the "acceptance criteria" largely revolve around the continued functional performance and adherence to specifications of the already cleared individual applications, plus verification of the minor changes to syngo.CT Pulmo 3D, and the successful integration into a single software package. However, quantitative performance metrics for the device against specific clinical tasks are not provided in this 510(k) summary document, as the submission focuses on the substantial equivalence of the consolidated product to its predicate devices, rather than presenting new clinical efficacy data.

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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 *

• 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.

Scan&GO:
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.

Siemens intends to update software version, SOMARIS/10 syngo CT VA30 (Update) for Siemens SOMATOM Computed Tomography (CT) Scanner Systems with mobile workflow and 3D Camera options.

This update includes support of additional hardware for the go. platform and includes reuse of optional postprocessing applications for Recon&GO for all scanners subject of this submission.

The SOMATOM CT Scanner Systems that support the same software platform update include:

• . SOMATOM go.Up
• . SOMATOM go.Now
• SOMATOM go.Top .
• SOMATOM go.All .
• . SOMATOM ao.Sim
• . SOMATOM go.Open Pro
• SOMATOM X.cite
• Scan&GO Mobile Medical Application (optional mobile workflow component) .

The subject device SOMATOM go. platform and SOMATOM X.cite with SOMARIS/10 syngo CT VA30 (update) are Computed Tomography X-ray Systems which feature one continuously rotating tubedetector system and function according to the fan beam principle. The SOMATOM go. platform and SOMATOM X.cite with software SOMARIS/10 syngo CT VA30 (update) produces CT images in DICOM format. These images can be used by trained staff for post-processing applications commercially distributed by Siemens Healthcare and other vendors. These images aid in diagnosis, treatment preparation and therapy planning support (including, but not limited to, Brachytherapy, Particle including Proton Therapy, External Beam Radiation Therapy, Surgery), 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 SOMATOM X.cite and SOMATOM go. platform CT scanners. Scan&GO can be operated on a Siemens provided tablet or personal computer that meets certain minimum technical requirements. It allows users to work in close proximity to the scanner and the patient.

The software version for the SQMATOM go, platform and SOMATOM X.cite, syngo CT VA30 (update) (SOMARIS/10 syngo CT VA30 (update)), is a command-based program used for patient management, data management, X-ray scan control, image reconstruction, and image archive/evaluation. The software platform SOMARIS/10 syngo CT VA30 (update) is designed to support a software plugin interface to reuse a subset of stand-alone, cleared processing software applications.

The Siemens Medical Solutions USA, Inc. K200524 submission describes an update to the SOMATOM X.cite and SOMATOM Go Platform CT Scanners (software version SOMARIS/10 syngo CT VA30). The submission focuses on demonstrating substantial equivalence to previously cleared devices through non-clinical testing.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and the Reported Device Performance:

The document primarily discusses verification and validation testing, with acceptance criteria tied to the fulfillment of requirements and comparable performance to predicate devices. Specific quantitative acceptance criteria are not explicitly detailed in a separate table format within the provided text, but rather described in the "Testing Performed" column of Table S5-06.

2. Sample Sizes Used for the Test Set and the Data Provenance:

• FAST Integrated Workflow (3D Camera): "Clinical data based software validation" was conducted. The specific number of cases or patients is not quantified in the provided text. Data provenance is not explicitly stated beyond "clinical data based software validation," implying it's likely from a clinical setting, but country of origin or retrospective/prospective nature is not specified.
• Contrast Media Protocol: The evaluation was based on factory protocols and comparison to approved drug labeling. This does not involve a "test set" in the traditional sense of patient data.
• Scan&GO Supported Hardware: "Bench test." The sample size for this is not specified. Data provenance is a bench test, presumably conducted by the manufacturer.
• Wireless Coexistence Testing: No specific sample size (number of wireless devices or test scenarios) is mentioned.
• Customer Use Testing:
  • Internal Clinical Use Test: "The CT scanner customer environment is simulated in Siemens Test Cabins. For such a test, customers with clinical expertise are typically invited to perform tests." The number of "customers with clinical expertise" or individual test cases is not quantified.
  • External Clinical Use Test: "The CT scanner is tested in the environment of the clinic/hospital. Typically we perform these tests with selected customer before rollout of the CT scanner." The number of "selected customer" sites or test cases is not quantified. Data provenance is clinical environments.

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

• For the FAST Integrated Workflow, the ground truth for "isocenter deviation" and "landmark boundaries" was established presumably by comparison to a reference or manual measurements, but the document does not specify the number or qualifications of experts involved in establishing this ground truth.
• For the Contrast Media Protocol, the ground truth is established by the "approved labeling of Ultravist® or Visipaque®." No external experts were involved in establishing new ground truth for this test.
• For Customer Use Testing, "customers with clinical expertise" were invited for internal tests, and "selected customer" (presumably clinical staff) performed external tests. The exact number and specific qualifications (e.g., "radiologist with 10 years of experience") are not provided.

4. Adjudication Method for the Test Set:

The document does not describe any explicit adjudication method (like 2+1 or 3+1) for any of the described tests. Performance for FAST Integrated Workflow appears to be based on direct measurement comparison. For customer use tests, it's implied that feedback from "customers with clinical expertise" determined meeting acceptance criteria, but no formal adjudication process is detailed.

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

No, the document does not describe a Multi-Reader Multi-Case (MRMC) comparative effectiveness study to measure the effect size of how much human readers improve with AI vs. without AI assistance. The study focuses on the technical performance of the device's features.

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

Yes, the testing described appears to be primarily focused on standalone algorithm/device performance for the modifications. For example:

• FAST Isocentering and FAST Range: The measurement of deviation is a direct assessment of the algorithm's accuracy in proposing an isocenter or landmark, independent of a human reader's interpretation improvement.
• Recon&GO features are noted as "post-processing algorithms" or "inline results methods" that appear to be integrated for improved workflow, implying their standalone function in generating these results.
• Software Specifications and Wireless Coexistence testing inherently evaluate the algorithm and system performance without explicit human-in-the-loop assessment as the primary outcome.

7. The Type of Ground Truth Used:

• FAST Integrated Workflow: The ground truth for isocenter deviation and landmark boundaries seems to be based on a reference standard or manual measurements to which the device's output is compared. This is an implicit form of expert consensus or highly accurate measurement.
• Contrast Media Protocol: The ground truth is the approved labeling of Ultravist® or Visipaque®, which serves as a regulatory and clinical standard.
• Scan&GO Supported Hardware: The ground truth is the information shown on tablets, serving as a reference for comparison of the in-room monitor's display.
• Software Specifications: Ground truth is defined by the software requirements/specifications themselves.
• Wireless Coexistence Testing: Ground truth is adherence to technical standards and successful communication parameters.
• Customer Use Testing: Ground truth seems to be based on expert opinion/feedback from "customers with clinical expertise" or "selected customer" in clinical environments, confirming the safety and effectiveness of the intended use.
• National Lung Screening Trial (NLST): This is referenced as supportive data for lung cancer screening indications. The ground truth for this external study (NLST) would have been clinical outcomes data (e.g., biopsy-confirmed cancer, mortality). However, it's important to note this is not the ground truth created for the current device's primary testing but rather cited clinical evidence supporting an indication for use.

8. The Sample Size for the Training Set:

The document does not specify any sample sizes for training sets. The submission describes updates to existing CT scanner systems and software, and the testing focuses on the verification and validation of these updates against predicate devices and defined requirements. This implies the core algorithms were likely developed and trained prior to this specific update.

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

Since no training set is mentioned, the method for establishing its ground truth is not described in this document.

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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 .*

• 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.

Scan&GO:

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 subject device SOMATOM go.Platform with SOMARIS/10 syngo CT VA30 are Computed Tomography Xray Systems which feature one continuously rotating tube-detector system and function according to the fan beam principle. The SOMATOM go.Platform with Software SOMARIS/10 syngo CT VA30 produces CT images in DICOM format, which can be used by trained staff for post-processing applications commercially distributed by Siemens Healthcare and other vendors as an aid in diagnosis, treatment preparation and therapy planning support (including, but not limited to, Brachytherapy, Particle including Proton Therapy, External Beam Radiation Therapy, Surgery). 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 SOMATOM go.Platform CT scanners. Scan&GO can be operated on a Siemens provided tablet or a commercially available tablet that meets certain minimum technical requirements. It allows users to work in the scanner and the patient.

The provided text describes acceptance criteria and testing for the Siemens SOMATOM go.Platform CT Scanners with software version SOMARIS/10 syngo CT VA30, and the Scan&GO mobile medical application.

Here's the breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document details various non-clinical tests conducted, with statements of the test results meeting the acceptance criteria. However, it does not present a single consolidated table of specific, quantifiable acceptance criteria alongside reported performance values for those criteria. Instead, it offers narrative summaries of the testing and its outcomes, indicating successful verification and validation.

Below is a table constructed from the provided text, outlining the features tested and the reported performance (which is generally stated as "met acceptance criteria" or "similar/improved performance").

2. Sample size used for the test set and the data provenance

• Check&GO Testing:

  • Sample size: 500 CT-series from 100 patients.
  • Data provenance: Not explicitly stated, but clinical datasets were used ("clinical datasets from 100 patients"). It's specified as a "bench test," which implies it was likely retrospective from an existing data archive. Country of origin is not mentioned.

• Other Non-Clinical Testing (Phantom, Integration, Functional): The document frequently refers to "phantom images," "test levels," "development activities," and "bench tests." No specific sample sizes for these tests (e.g., number of phantom scans) or data provenance are provided beyond the general descriptions of the tests themselves, which are stated as having been conducted "during product development."

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Check&GO Testing:

  • Ground Truth Establishment: The datasets were "manually annotated with a detailed GT contrast-state (None, Low, InhomogeneousLow, Standard, InhomogeneousHigh, High)."
  • Number & Qualifications of Experts: Not specified.

• Other Tests: For other tests, such as those involving image quality or physical measurements (e.g., Flex 4D Spiral, DirectDensity), the ground truth is typically derived from physical measurements, reference standards (e.g., known phantom properties), or established technical specifications, rather than expert consensus on clinical interpretation. The document does not mention the use of experts to establish ground truth for these tests. The indication for the new "Kidney Stones" feature notes: "Only a well-trained radiologist can make the final diagnosis under consideration of all available information," suggesting the involvement of radiologists in the clinical context, but not for ground truth establishment specifically for the device's technical validation.

4. Adjudication method for the test set

• The document does not explicitly describe an adjudication method (like 2+1, 3+1, etc.) for any of the test sets. For the Check&GO test, ground truth was "manually annotated," implying a single process for ground truth establishment rather than a consensus/adjudication method among multiple experts.

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 comparative effectiveness study involving human readers and AI assistance is reported for this device in the provided text. The device itself is a CT scanner system and its associated software, not explicitly an AI-assisted diagnostic tool for interpretation in collaboration with human readers. The Check&GO feature is described as "aiding the user," but no study on human performance improvement is included.

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

• The non-clinical performance testing, particularly phantom studies and specific feature evaluations like "kV and Filter independent CaScore," "Recon&GO - Spectral Recon," "TwinSpiral Dual Energy," "Flex 4D Spiral," "DirectDensity," "HD FoV," and "InjectorCoupling," can be considered standalone algorithm/device performance evaluations. These tests assess the technical output and accuracy of the device and its software features independent of human interpretation or interaction during the measurement process. The Check&GO feature's "Bench Test" also evaluates the algorithm's performance against annotated ground truth.

7. The type of ground truth used

• Check&GO: Expert annotation of "detailed GT contrast-state" (None, Low, InhomogeneousLow, Standard, InhomogeneousHigh, High) for 500 CT series.
• Other Feature Tests (e.g., CaScore, Spectral Recon, Flex 4D Spiral, DirectDensity, HD FoV, TwinSpiral DE): Primarily derived from physical phantom measurements, comparison to established technical specifications, or reference images/algorithms (e.g., comparing to initial release versions or conventional 120kV images).
• National Lung Screening Trial (NLST): Outcomes data from a large clinical trial (N Engl J Med 2011; 365:395-409) is cited to support the "low dose lung cancer screening" indication for use, not for direct ground truth establishment during this device's specific validation, but rather as supportive clinical literature for the screening concept itself.

8. The sample size for the training set

• The document does not specify any training set sizes. The studies described are primarily for verification and validation, not for training machine learning models. The Check&GO feature describes "500 CT-series from 100 patients were used for the testing of the algorithm," but this is explicitly called "testing," not training.

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

• Since no training set details (size or establishment method) are provided, this information is not available in the document.

Ask a specific question about this device

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. *

*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.

Scan&GO:

This 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

Siemens intends to market a new software version, SOMARIS/10 syngo CT VA30 for Siemens SOMATOM X.cite (CT) Scanner System with mobile workflow options.

Single Source CT Sacnner System:

• . SOMATOM X.cite
• Scan&GO Mobile Medical Application (optional mobile workflow component) ●

The subject device SOMATOM X.cite with SOMARIS/10 syngo CT VA30 is a Computed Tomography X-ray System which feature one (single source) continuously rotating tube-detector system and function according to the fan beam principle. The SOMATOM X.cite with Software SOMARIS/10 syngo CT VA30 produces CT images in DICOM format, which can be used by trained staff for post-processing applications commercially distributed by Siemens Healthcare and other vendors as an aid in diagnosis, treatment preparation and therapy planning support (including, but not limited to, Brachytherapy, Particle including Proton Therapy, External Beam Radiation Therapy, Surgery). 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 SOMATOM X.cite CT scanner. Scan&GO can be operated on a Siemens provided tablet or a commercially available tablet that meets certain minimum technical requirements. It allows users to work in close proximity to the scanner and the patient. Specifically Scan&GO allows control/display of the following software interactions via a wireless tablet that meets certain minimum requirements:

• Selection of patients o
• O Selection of pre-defined protocols
• Scan parameter display O
• Patient table position display and gantry tilt parameter display o
• Tools and instruction message area, O
• o Patient table position planning area
• O Physiological data display
• Patient data display (e.g. date of birth, name) O
• Display of acquired topogram and tomogram images O
• Finalization of exam (close patient) O
• Mobile Organizer, O
• O Patient Instruction Language ("API languages")
• O Interface to support control function for RTP Laser (e.g. LAP Laser)
• Control of moodlight functions o
• predefined workflow associated question/answer dialog o

NOTE: Scan&GO does not support storage of images. Additionally, Scan&GO cannot trigger a scan or radiation release.

The software version for the SOMATOM X.cite, syngo CT VA30 (SOMARIS/10 syngo CT VA30), is a command-based program used for patient management, X-ray scan control, image reconstruction, and image archive/evaluation. The software platform SOMARIS/10 syngo CT VA30 is designed to provide a plugin interface to support the optional Scan&GO mobile workflow as well as integrate potential advanced post processing tasks, tools, or extendable functionalities. Software version syngo CT VA30 (SOMARIS/10 syngo CT VA30) is a new software version based on syngo CT VA20A (SOMARIS/10 syngo CT VA20) which was cleared for the primary predicate devices in K173632), and supports the same plugin interfaces for the optional Scan&GO mobile workflow and integration of post-processing tasks as the predicate devices.

The SOMATOM X.cite will support the following modifications/further developments in comparison to the predicate devices:

1. New/Modified Hardware

• . Table S01: Overview of Hardware modifications supported by software SOMARIS/10 syngo CT VA30

2. Software version SOMARIS/10 syngo CT VA30

• Table S02: Overview Software modifications of SOMATOM X.cite with syngo CT VA30 ●
  The submission show configuration table and comparison table and use the following Terms to describe various technological characteristics in comparison to the predicate device information:

The provided text describes the acceptance criteria and the study that proves the device meets them for the SOMATOM X.cite CT system and its associated Scan&GO mobile application.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of "acceptance criteria" alongside specific "reported device performance" values in a quantitative format for the entire device. Instead, it describes various tests performed and states that the tests "meet the pre-determined acceptance criteria" and "all of the software specifications have met the acceptance criteria."

However, specific performance aspects are mentioned:

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

The document details various types of testing, but it does not provide specific sample sizes (e.g., number of patients/cases) for the test sets.

• Test Sets:

  • Non-Clinical Testing: Includes "phantom tests" and "bench tests" (Table S07). No specific sample sizes (number of phantoms or tests) are mentioned for these.
  • Customer Use Testing:
    • Internal Clinical Use Test: "The CT scanner customer environment is simulated in Siemens Test Cabins." Customers with clinical expertise are invited to perform tests. No sample size is provided.
    • External Clinical Use Test: "The CT scanner is tested in the environment of the clinic/hospital." Performed with "selected customer before rollout." No sample size is provided.
  • NLST (National Lung Screening Trial) Reference: This is cited as supportive data for the lung cancer screening indication, but the device was not tested in this trial. The NLST itself involved over 53,000 subjects.

• Data Provenance:

  • The testing described is primarily prospective system and software verification and validation conducted by Siemens.
  • Country of Origin of Data: Not explicitly stated for Siemens' internal testing, but the manufacturing sites are listed as Forchheim, Germany, and Shanghai, China, suggesting the testing would likely occur in association with these locations or corporate R&D facilities.
  • The National Lung Screening Trial (NLST) (referenced for lung cancer screening indication) was conducted in the United States and spanned from 2002 to 2010.

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

• For Siemens Internal/External Clinical Use Tests: The document mentions "customers with clinical expertise" for internal testing and "selected customer" [sic] for external testing. However, it does not specify the number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience") used to establish ground truth for these tests. The nature of these tests appears to be more about workflow and functionality validation rather than diagnostic performance against a definitive ground truth.
• For the NLST (referenced for lung cancer screening): The NLST used a rigorous process for establishing ground truth, typically involving expert radiologists and follow-up pathology for confirmed malignancies. However, this study was performed independently and not specifically to test the SOMATOM X.cite device. The FDA's reference to NLST is for the clinical literature supporting the indication for use, not for the device's performance validation itself.

4. Adjudication Method for the Test Set

The document does not describe an adjudication method (e.g., 2+1, 3+1 consensus) for establishing ground truth within Siemens' own device performance testing. The clinical use tests described seem to focus on user workflow and system integration rather than diagnostic accuracy requiring multi-reader adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Its Effect Size of Human Readers Improve with AI vs. Without AI Assistance

• No, an MRMC comparative effectiveness study was not done or described in this document. The device is a CT scanner, not an AI-assisted diagnostic tool for interpretation.
• The document describes the CT system's ability to generate images for aid in diagnosis, but it does not include a study on how human readers' performance (e.g., diagnostic accuracy, reading time) improves with the SOMATOM X.cite specifically.
• The only reference to an "AI" type feature is "Artificial120" (a kernel for CaScore) and "ADMIRE" (an iterative reconstruction method), but these are features of image processing, not direct AI assistance for human interpretation.

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

• The testing described is for a physical medical device (CT scanner) and its associated software, not a standalone algorithm.
• The "non-clinical testing" and "bench tests" evaluate the functional and image quality performance of the CT system's hardware and software components in isolation or simulated environments, which could be considered analogous to "standalone" performance for individual features (e.g., the accuracy of DirectDensity™ or Recon&GO), but not for a diagnostic algorithm in a clinical context.

7. The Type of Ground Truth Used

Based on the descriptions:

• Bench/Phantom Tests: Ground truth is established by physical phantoms with known properties (e.g., known densities, dimensions, material compositions) and comparisons to established measurements or control values.
• Software Verification & Validation: Ground truth is established by software specifications and requirements, and testing verifies that the software functions as designed and meets these predefined criteria.
• Clinical Use Tests: Ground truth appears to be based on successful workflow completion and user feedback rather than a definitive diagnostic truth for patient cases.
• NLST (reference only): For the lung cancer screening claim, the underlying clinical evidence (NLST) established ground truth through longitudinal follow-up and pathology for confirmed lung cancers. This is external literature, not part of the device's direct performance study.

8. The Sample Size for the Training Set

• This document is for a CT scanner system (hardware and software), not specifically for a machine learning or AI model that requires a dedicated "training set."
• Therefore, no information on the sample size for a training set is provided or applicable in the context of this 510(k) submission. Iterative reconstruction methods (like ADMIRE) are often developed using traditional signal processing and physics-based models, and while some may incorporate statistical or adaptive elements, they aren't typically described with "training sets" in the same way as deep learning AI.

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

• As a "training set" for an AI or machine learning model is not discussed, this question is not applicable based on the provided document.

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syngo.via is a software solution intended to be used for viewing, manipulation, and storage of medical images.

It can be used as a stand-alone device or together with a variety of cleared and unmodified syngo based software options. syngo .via supports interpretation and evaluation of examinations within healthcare institutions, for example, in Radiology, Nuclear Medicine and Cardiology environments.

The system is not intended for the displaying of digital mammography images for diagnosis in the U.S.

Siemens Healthcare GmbH intends to market the Picture Archiving and Communications System, syngo.via. software version VB40A. This 510(k) submission describes several modifications to the previously cleared predicate device, syngo.via, software version VB10A.

syngo.via is a software only medical device, which is delivered by download to be installed on common IT hardware. This hardware has to fulfil the defined requirements. Any hardware platform that complies to the specified minimum hardware and software requirements and with successful installation verification and validation activities can be supported. The hardware itself is not seen as part of the medical device syngo.via and therefore not in the scope of this 510(k) submission.

syngo.via provides tools and features to cover the radiological tasks reading images and reporting. syngo.via supports DICOM formatted images and objects. syngo.via also supports storage of Structured DICOM Reports. In a comprehensive imaging suite, syngo.via interoperates with a Radiology Information System (RIS) to enable customer specific workflows.

syngo.via is based on a client-server architecture. The server processes and renders the data from the connected modalities. The server provides central services including image processing and temporary storage, and incorporates the local database. The client provides the user interface for interactive image viewing and can be installed and started on each workplace that has a network connection to the server.

The server's backend communication and storage solution is based on Microsoft Windows server operating systems. The client machines are based on Microsoft Windows operating systems.

syngo.via supports various monitor setups and can be adapted to a range of image types by connecting different monitor types.

The subject device and the predicate device share fundamental scientific technology. This device description holds true for the subject device, syngo.via, software version VB40A, as well as the predicate device, syngo.via, software version VB10A.

This document describes the 510(k) summary for syngo.via (Version VB40A), a Picture Archiving and Communications System developed by Siemens Healthcare GmbH. The submission asserts substantial equivalence to the predicate device, syngo.via (Version VB10A).

The document states that a study was conducted to demonstrate the substantial equivalence of the new device by focusing on verification and validation testing of the modifications. However, it does not provide detailed acceptance criteria and reported device performance in a table format. Nor does it detail a specific study with sample sizes, data provenance, expert qualifications, or adjudication methods for proving the device meets acceptance criteria.

Here's an attempt to extract and synthesize the information based on the provided text, recognizing the limitations of the submission's detail:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not provide a table of acceptance criteria or specific reported device performance in a quantitative manner. Instead, it makes a qualitative claim about the modifications:

"The software specifications have met the acceptance criteria. Testing for verification and validation for the device was found acceptable to support the claims of substantial equivalence."

And for specific features compared against the predicate:
"The changes between the predicate device and the subject device doesn't impact the safety and effectiveness of the subject device as the necessary measures taken for the safety and effectiveness of the subject device."

The document primarily focuses on verifying that new features and system updates (like operating system support, cyber security enhancements, and improvements to existing algorithms) do not negatively impact safety and effectiveness and perform comparably to the predicate device. The acceptance criteria would broadly relate to demonstrating functionality, safety, and effectiveness equivalent to the cleared predicate device.

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

The document states: "Non-clinical tests were conducted for the device syngo.via during product development. The modifications described in this Premarket Notification were supported with verification and validation testing." And "Performance tests were conducted to test the functionality of the device syngo.via."

However, no specific information is provided regarding:

• The sample size used for the test set (number of images, cases, or tests)
• The data provenance (e.g., country of origin, whether the data was retrospective or prospective).

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)

This information is not provided in the document. The submission describes software verification and validation, but not clinical performance studies involving expert readers to establish ground truth.

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

This information is not provided in the document.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not mentioned or indicated in this submission. The device (syngo.via) is described primarily as a viewing, manipulation, communication, and storage system for medical images, with enhancements to existing imaging algorithms (e.g., Cinematic VRT, Organ Segmentation) and user interface improvements. It explicitly states: "No automated diagnostic interpretation capabilities like CAD are included. All image data are to be interpreted by trained personnel." Therefore, it is unlikely an MRMC study comparing human readers with and without AI assistance was conducted for this particular clearance, as the device itself does not offer AI-driven diagnostic assistance.

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

The document describes "Imaging algorithms" such as:

• Multiplanar reconstruction (MPR)
• Maximum and Minimum Intensity Projection (MIP/MinIP)
• Volume Rendering Technique (VRT) with additional technique Cinematic VRT
• Organ segmentation based on existing ALPHA technology
• Change Visualization
• Automatic Spine Labeling, also for ribs in CT thorax scans (“Rib labeling”)

It states that "There are enhancements to the existing algorithms in the subject device compared to the predicate device." While these are algorithms, the submission treats them as enhancements to an imaging workstation rather than standalone diagnostic algorithms requiring separate "standalone performance" metrics in the way a CAD system might. The performance of these algorithms would have been assessed during the "Non-clinical Performance Testing" and "Software Verification and Validation" as mentioned, to ensure they function as intended and do not impact safety or effectiveness. However, no specific metrics for standalone performance (e.g., sensitivity, specificity for organ segmentation outside the context of human interpretation) are provided.

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

The document does not specify the type of ground truth used, as it does not detail specific performance studies involving diagnostic accuracy or clinical outcomes. The "ground truth" for software verification and validation would typically be established based on functional requirements, design specifications, and successful execution of tests.

8. The sample size for the training set

The document does not provide information about a training set size. This submission is for an updated version of a Picture Archiving and Communications System (PACS) and does not describe the development or training of new AI/ML models in a way that would typically involve a separate "training set." The Mention of "Organ segmentation based on existing ALPHA technology" suggests it might leverage pre-existing technology, but no details on its training are given for this submission.

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

Since no training set information is provided (as per point 8), details on how its ground truth was established are also not available in the provided text.

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syngo.via View&GO is a software solution intended to be used for Viewing, communication, and storage of medical images. It can be used as a stand-alone device or together with a variety of cleared and unmodified syngo based software options.

syngo.via View&GO supports interpretation of examinations within healthcare institutions, for example, in Radiology, Nuclear Medicine and Cardiology environments. The system is not intended for the displaying of digital mammography images for diagnosis in the US.

Siemens Healthcare GmbH intends to market the Picture Archiving and Communications System, syngo.via View&GO, software version VA10A. This 510(k) submission describes several modifications to the previously cleared predicate device, syngo.via, software version VB10A.

syngo.via View&GO is a software only medical device, which is delivered by download to be installed on common IT hardware. This hardware has to fulfil the defined requirements. Any hardware platform that complies to the specified minimum hardware and software requirements and with successful installation verification and validation activities can be supported. The hardware itself is not seen as part of the medical device syngo.via View&GO and therefore not in the scope of this 510(k) submission.

syngo.via View&GO provides tools and features to cover the radiological tasks preparation for reading, reading images and support for reporting 4. syngo.via View&GO supports DICOM formatted images and objects.

syngo.via View&GO is a standalone viewing and reading workplace. This is capable of rendering the data from the connected modalities for the post processing activities. syngo.via View&GO provides the user interface for interactive image viewing and processing with a limited short term storage which can be interfaced with any Long term storage (e.g. PACS) via DICOM.

syngo.via View&GO is based on Microsoft Windows operating systems.

syngo.via View&GO supports various monitor setups and can be adapted to a range of image types by connecting different monitor types.

The subject device and the predicate device share fundamental scientific technology.

The provided text describes a 510(k) premarket notification for syngo.via View&GO (Version VA10A), a Picture Archiving and Communications System (PACS). However, it does not contain the detailed information required to answer all parts of your request, specifically regarding a clinical study with detailed acceptance criteria, sample sizes, expert involvement, and ground truth establishment.

The document primarily focuses on demonstrating substantial equivalence to a predicate device (syngo.via VB10A) through comparisons of intended use, technological characteristics, and non-clinical performance testing. It highlights that syngo.via View&GO is a simplified version of the predicate device, with some functionalities removed.

Here's a breakdown of what can be extracted and what is missing:

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

The document states: "The testing results support that all the software specifications have met the acceptance criteria." However, it does not provide a specific table of acceptance criteria or quantitative performance metrics for the device’s functionality. It mentions "non-clinical tests were conducted for the device syngo.via View&GO during product development" to assess functionality, but the results themselves are not detailed in terms of specific performance against defined criteria.

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

This information is not provided in the document. The testing described is "non-clinical" and focuses on software verification and validation, not clinical performance using a specific test set of patient data.

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)

This information is not provided as the document does not describe a clinical study with expert-established ground truth.

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

This information is not provided as the document does not describe a clinical study.

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

This information is not provided. The device is a PACS system for viewing, manipulation, communication, and storage of medical images, and "no automated diagnostic interpretation capabilities like CAD are included." Therefore, an MRMC study assessing AI assistance is not applicable to this device based on the provided information.

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

The document primarily describes a "software only medical device" that is a "standalone viewing and reading workplace" intended for use by medical professionals. The software itself is a standalone system in terms of its architecture (compared to the client-server predecessor), but its intended use involves human interpretation. It does not describe a standalone algorithmic performance study in the context of diagnostic interpretation, as it explicitly states it has no CAD functionalities.

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

This information is not provided as there is no description of a clinical study that required ground truth establishment. The testing mentioned is "non-clinical" for software functionality.

8. The sample size for the training set

This information is not provided. The document describes a PACS system which displays existing medical images. It does not mention any machine learning or AI components that would require a "training set" of data in the typical sense for diagnostic algorithms.

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

This information is not provided, as there is no mention of a training set or ground truth establishment for such a set.

Summary of what is available from the document:

The provided document describes a non-clinical performance testing approach for a PACS system, syngo.via View&GO. The "study" (non-clinical testing) aims to demonstrate that modifications to a previously cleared predicate device (syngo.via VB10A) do not introduce new safety risks and that the new device remains substantially equivalent for its intended use.

• Acceptance Criteria & Performance: The document states that "all the software specifications have met the acceptance criteria" based on non-clinical verification and validation testing. However, specific quantitative acceptance criteria and detailed performance metrics are not provided.
• Study Type: Non-clinical software verification and validation testing.
• Sample Size/Data Provenance: Not applicable for a typical clinical test set as described. The testing focuses on software functionality, not clinical performance on a dataset of patient cases.
• Expert/Ground Truth/Adjudication: Not applicable, as it's a non-clinical software validation without diagnostic AI features.
• MRMC Study: Not applicable, as the device does not have AI diagnostic interpretation capabilities.
• Standalone Performance: The device is described as a "standalone viewing and reading workplace" in terms of its architecture, but not in the context of an algorithm performing diagnoses without human involvement.
• Training Set/Ground Truth for Training: Not applicable/not mentioned, as there are no AI components requiring training.

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