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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.

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AI-Rad Companion (Pulmonary) is image processing software that provides quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images to support radiologists and physicians from emergency medicine, specialty care, urgent care, and general practice in the evaluation and assessment of disease of the lungs. It provides the following functionality:

• Segmentation and measurements of complete lung and lung lobes
• Identification of areas with lower Hounsfield values in comparison to a predefined threshold for complete lung and lung lobes
• Providing an interface to external Medical Device syngo.CT Lung CAD
• Segmentation and measurements of found lung lesions and dedication to corresponding lung lobe.
  The software has been validated for data from Siemens (filtered backprojection and iterative reconstruction), GE Healthcare (filtered backprojection reconstruction), and Philips (filtered backprojection reconstruction).
  Only DICOM images of adult patients are considered to be valid input.

AI-Rad Companion (Pulmonary) is a software only image processing application that supports quantitative and qualitative analysis of previously acquired CT DICOM Images to support radiologists and physicians from emergency medicine, specialty care, and general practice in the evaluation of and assessment of disease of the thorax.

Here is a summary of the acceptance criteria and the study that proves the device meets them, based on the provided FDA document for Siemens AI-Rad Companion (Pulmonary):

1. Table of Acceptance Criteria & Reported Device Performance

The document doesn't explicitly state "acceptance criteria" as clear pass/fail thresholds for each metric. Instead, it describes validated performance results and claims they are "superior" to the predicate device, thereby supporting substantial equivalence. The key performance metrics are for lung lobe segmentation.

• Ranged between 0.95 and 0.98.
• Standard Deviation (SD) 4,500 CT data sets"

• Data Provenance: Retrospective performance study from "multiple clinical sites from within and outside United States."

3. Number of Experts and Qualifications for Ground Truth

• The document does not explicitly state the "number of experts" or their specific "qualifications" beyond mentioning "manually established ground truth." It implies that the ground truth was established by qualified professionals, likely radiologists or trained medical personnel, given the nature of the task (segmentation).

4. Adjudication Method for the Test Set

• The document does not specify an adjudication method (e.g., 2+1, 3+1). It states "manually established ground truth," which typically implies consensus among multiple readers or a single highly experienced reader whose work is considered the gold standard within the study's context.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No evidence of a MRMC study. The document describes a standalone (algorithm only) performance study directly comparing the AI algorithm's output to "manually established ground truth" and claiming superiority over a predicate device's algorithm, not an AI-assisted human reader study. The purpose of this submission is to demonstrate substantial equivalence of the new AI-Rad Companion to existing predicate devices, not improvement in human reader performance.

6. Standalone (Algorithm Only) Performance Study

• Yes, a standalone study was done. The performance metrics (DICE coefficients, surface distance, Hausdorff distance, volume error) were computed by comparing the output of the algorithm to the manually established ground truth. This confirms it was an algorithm-only performance evaluation without human-in-the-loop.

7. Type of Ground Truth Used

• Expert Consensus/Manual Establishment: The ground truth for the test set was "manually established ground truth." This typically refers to annotations or segmentations performed by human experts (e.g., radiologists) and potentially reviewed for consensus.

8. Sample Size for the Training Set

• The document mentions a "Training cohort: size and properties of data used for training" as a structural element of their analysis but does not provide the specific sample size for the training set.

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

• The document mentions "Description of ground truth / annotations generation" as a structural element for their algorithm analysis but does not detail how the ground truth for the training set was established. It can be inferred that it was likely generated through expert annotations, potentially similar to the test set, but specific methods (e.g., single expert, multiple experts, consensus, specific tools) are not described.

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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.CT Extended Functionality is intended to provide advanced visualization tools to prepare and process medical images for diagnostic purpose. The software package is designed to support technicians and physicians in qualitative and quantitative measurements and in the analysis of clinical data that was acquired and reconstructed by Computed Tomography (CT) scanners.

An interface shall enable the connection between the syngo.CT Extended Functionality software package and the interconnected CT Scanner system.

Result images created with the syngo.CT Extended Functionality software package can be used to assist trained technicians or physicians in diagnosis.

syngo.CT Extended Functionality is a software bundle consisting of previously cleared unmodified and modified post-processing applications that offer tools to support special clinical evaluations. syngo.CT Extended Functionality can be used to create advanced visualizations and measurements on clinical data that was acquired and reconstructed by Computed Tomography (CT) scanners.

Depending on the clinical question, the user can select functionality which supports the explicit clinical fields as listed below. The syngo.CT Extended Functionality software package is designed to operate on the most recent version syngo-compatible postprocessing platform, which currently supports the following four tools:

• 1. Preparation of Vascular Case for Reading Physician
• 2. Preparation of Oncology Case for Reading Physician
• 3. Preparation of Osteo Case for Reading Physician
• 4. Preparation of Neuro DSA Bone Subtraction for Reading Physician

The supported functionality can be used on any CT data if basic requirements are met (e.g. spiral or sequence scan, reconstruction kernel). The supported functionality will check to ensure the basic requirements are met and will not allow its execution or will provide a warning or info message to the user if appropriate. This check also allows combination of functionality of different clinical fields, (e.q. a vascular case can be prepared also on Neuro DSA bone subtracted data or on the same case as Lung CAD computation, etc.). Afterwards, any tool can be accessed as long as the data and viewing type allows it. For example, an evaluation of a ROI defined by a contour and two HU thresholds can be used to measure a certain area. No specific sequential workflow is required.

The original clinical data that was acquired and reconstructed by Computed Tomography (CT) scanners will not be modified in any form. The results of the syngo.CT Extended Functionality can be stored as additional DICOM images if needed as kev images or range or images. The subject device syngo.CT Extended Functionality is designed to operate on a syngo compatible host system (e. g. syngo.via VB20 software platform or higher).

The provided text describes the 510(k) premarket notification for "syngo.CT Extended Functionality." However, it does not contain the specific details required to fully address all parts of your request regarding acceptance criteria and a detailed study proving device performance. The information provided is high-level and focuses on regulatory compliance and substantial equivalence to predicate devices, rather than a specific clinical performance study.

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

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

The provided document does not include a specific table of acceptance criteria with corresponding performance metrics for the syngo.CT Extended Functionality as a whole, or for its individual modified components. It generally states:

• "All verification and validation testing has been completed and meets Siemens acceptance criteria." (Page 7)
• "The testing results support that all the software specifications have met the acceptance criteria." (Page 7)
• "The results of these tests demonstrate that the subject device performs as intended." (Page 7)
• "The results of all conducted testing was found acceptable to support the claim of substantial equivalence." (Page 7)

This is a general statement of compliance, not a detailed report of specific performance metrics against defined acceptance criteria.

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 does not specify the sample size used for any test sets, nor does it provide information about the provenance of the data (country of origin, 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)

The document does not mention the use of experts to establish ground truth for any test set or their qualifications.

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

The document does not describe any adjudication methods used for a test set.

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

The document does not mention an MRMC comparative effectiveness study, nor does it discuss improvements in human reader performance with or without AI assistance. The device is described as providing "advanced visualization tools to prepare and process medical images for diagnostic purpose" and assisting "trained technicians or physicians in diagnosis," but not as an AI-powered diagnostic tool in the sense of comparing human performance with and without its specific assistance.

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

The document does not describe any standalone algorithm performance studies. The device is explicitly intended to "assist trained technicians or physicians in diagnosis," implying human-in-the-loop usage.

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

The document does not specify the type of ground truth used for any testing.

8. The sample size for the training set

The document does not mention a training set or its sample size. This is consistent with the device being a "software bundle consisting of previously cleared unmodified and modified post-processing applications," rather than a novel AI/ML algorithm that requires a dedicated training phase reported in this context.

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

As no training set is mentioned, information on how its ground truth was established is also absent.

Summary of what is present in the document regarding testing:

The document focuses on "Non-Clinical Testing Summary" (Page 7) to demonstrate substantial equivalence, rather than detailed clinical performance studies.

• Type of Study: Non-clinical tests (integration and functional) were conducted. Verification/validation testing was performed for modifications to previously cleared components.
• Acceptance Criteria (General): "All verification and validation testing has been completed and meets Siemens acceptance criteria." "The testing results support that all the software specifications have met the acceptance criteria."
• Risk Analysis: A risk analysis was completed, and risk control was implemented in accordance with ISO 14971.
• Software Verification and Validation: Documentation for "Moderate Level of Concern" software was included, conforming to FDA's "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" (May 11, 2005).
• Intended Use: The tests demonstrated the device "performs as intended."
• Comparison to Predicate: "Siemens used the same testing with the same workflows as used to clear the predicate device."

Conclusion based on the provided text:

The submission confirms that the device underwent verification and validation testing as part of the regulatory approval process for software, especially for modifications made to existing functionalities (like the Osteo feature). However, it does not provide the detailed clinical performance study data (including specific acceptance criteria, sample sizes, expert involvement, and ground truth methodologies) often associated with new diagnostic algorithms or AI-driven systems. The clearance is based on demonstrating substantial equivalence to predicate devices, supported by non-clinical performance data and software validation, suggesting the device's functionality is well-understood and its safety and effectiveness are established through these engineering and software-level tests.

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