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• This application software is image processing application software available for installation onto customer-owned hardware. This application software can be networked to provide for sharing of resources.
• This application software receives medical image data (CT, MRI, Ultrasound, Digital X-ray, X-ray Angiography, PET, SPECT, Nuclear Medicine, Secondary Capture, Mammography, X-ray Radiofluoroscopic image, and RT Image) from modalities or image archives such as PACS through network or media, and provides for the viewing, quantification, manipulation, communication, printing and management of medical images.
• This application software is intended only for use by trained medical professionals to supplement generally accepted methods of interpreting radiological images.

Ziostation2 is a basic DICOM image management system to further aid clinicians in their analysis of anatomy, physiology and pathology. Universal functions such as data retrieval, storage, management, querying and listing, and output are handled by the basic Ziostation2 software. Various imaging tools and techniques can be invoked to process images from the following image types: CT, MRI, Ultrasound, Digital X-ray, X-ray Angiography, PET, SPECT, Nuclear Medicine, Secondary Capture, Mammography, X-ray Radiofluoroscopic image, RT Image.
CT Dual kV is a new image processing tool of Ziostation2
The added capabilities provided by this additional image processing tool for use with CT DICOM compliant images are:
Generating and manipulating further images using volume data of two different tube voltage CT scans:

• O Blend image
• Subtraction image
• 0 Virtual non-contrast image
• lodine distribution map
• O 2-Material decomposition image

The provided text is a 510(k) Summary for the Ziostation2 device. It describes the device, its indications for use, and its substantial equivalence to predicate devices. However, the document does not contain specific details about acceptance criteria, device performance metrics, the studies conducted (including sample sizes, ground truth establishment, expert qualifications, or adjudication methods), or any mention of MRMC comparative effectiveness studies or standalone performance evaluations.

Therefore, I cannot provide the requested information based solely on the given text. The text only mentions "integration testing/verification testing" and "regression testing" without elaborating on their specifics or results against defined acceptance criteria.

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• This application is image processing application software available for installation onto customer-owned hardware. This application software can be networked to provide for sharing of resources.
• This application software receives medical image data (CT, MRI, Ultrasound, Digital X-ray, X-ray Angiography, PET, SPECT, Nuclear Medicine, Secondary Capture, Mammography, X-ray Radiofluoroscopic image, and RT Image) from modalities or image archives such as PACS through network or media, and provides for the viewing, quantification, manipulation, communication, printing and management of medical images.
•This application software is intended only for use by trained medical professionals to supplement generally accepted methods of interpreting radiological images.
• Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations. Mammographic images may only be interpreted using a monitor whose characteristics are approved by the regulatory agency governing the market within which this application is being offered.

Ziostation2 is a basic DICOM image management system to further aid clinicians in their analysis of anatomy, physiology and pathology. Universal functions such as data retrieval, storage, management, querying and listing, and output are handled by the basic Ziostation2 software. Various imaging tools and techniques can be invoked to process images from the following image types: CT, MRI, Ultrasound, Digital X-ray, X-ray Angiography, PET, SPECT, Nuclear Medicine, Secondary Capture, Mammography, X-ray Radiofluoroscopic image, RT Image.

The Ziostation2 is an image processing application software for viewing, quantifying, manipulating, communicating, printing, and managing medical images from various modalities. The 510(k) summary (K181892) indicates that several new functionalities and enhancements were added to the Ziostation2, building upon the features of the previously cleared Ziostation2 (K151212) and other predicate devices.

Here's an analysis of the acceptance criteria and study information provided:

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

The provided text does not explicitly state specific acceptance criteria or quantitative performance metrics for the Ziostation2 software itself. Instead, the document focuses on demonstrating substantial equivalence to predicate devices for its new functionalities and enhancements. The acceptance criteria appear to be based on the functional similarity and safety of the new features compared to already legally marketed devices.

However, based on the text, we can infer that the general "acceptance criterion" is that the new functionalities (LAA analysis, Lung Lobes, Virtual Bronchoscopy, CT Lung Resection Planning, MR Myocardial T1 Mapping) perform similarly to their counterparts in the predicate devices and do not introduce new safety or effectiveness concerns.

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

The document does not specify a sample size for any test set or the data provenance (e.g., country of origin, retrospective/prospective). The submission relies on establishing substantial equivalence to predicate devices, implying that testing focused on confirming functional similarity and safety, rather than a separate clinical performance study with a distinct test set.

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

The document does not mention the number or qualifications of experts used to establish ground truth for a test set. This type of information is typically part of a clinical performance study, which is not described here.

4. Adjudication method for the test set

The document does not describe an adjudication method for a test set. As no specific clinical test set is elaborated upon, neither is an adjudication method.

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 a multi-reader multi-case (MRMC) comparative effectiveness study. There is no indication of an assessment of human readers' improvement with or without AI assistance. The submission is for an image processing application, which may include AI components, but a specific MRMC study is not detailed.

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

The document does not provide details about a standalone (algorithm-only) performance study. The device is an "image processing application software" intended for "trained medical professionals to supplement generally accepted methods of interpreting radiological images," implying a human-in-the-loop context.

7. The type of ground truth used

The document does not explicitly state the type of ground truth used. Given the nature of a 510(k) summary focused on substantial equivalence to predicate devices across various image processing functions, the "ground truth" would likely be implied by the established and accepted performance of the predicate devices themselves, or by standard clinical and radiological interpretation methods. It does not mention pathology or outcomes data as explicitly used for a test set's ground truth.

8. The sample size for the training set

The document does not provide any information regarding the sample size for a training set. This information would typically be relevant for machine learning or AI components, but it is not detailed in this submission.

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

The document does not describe how ground truth for any training set was established. Similar to point 8, this information is absent.

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Ziostation2 is an image processing application software available for installation onto customer owned hardware. This application software can be networked to provide for sharing of resources.

This application software receives medical images from modalities (mage scanning devices such as CT) or image archives such as PACS through network or media and provides for the viewing, quantification, manipulation, communication, printing, and management of medical images.

This application software is intended for use by trained medical professionals to supplement generally accepted methods of interpreting radiological images.

Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations. Mammographic images may only be interpreted using a monitor whose characteristics are approved by the regulatory agency governing the market within which Ziostation2 is being offered.

Note: The clinician retains the ultimate responsibility for making the proper diagnosis based on standard radiological practices and visual comparison of the separate, unprocessed images. Ziostation2 is a tool to be used in support of those standard practices and visual comparisons.

ZIOSTATION2 is a basic DICOM image management system to further aid clinicians in their analysis of anatomy, physiology and pathology. Universal functions such as data retrieval, storage, management, querying and listing, and output are handled by the basic Ziostation2 software. Various imaging tools and techniques can be invoked to process images from the following image types: CT, MRI, Ultrasound, Digital X-ray X-ray Angiography, PET, SPECT, NM, SC, Mammography, X-ray Radiofluoroscopic image, RT Image.

The provided text is a 510(k) premarket notification for the Ziostation2, an image processing application software. It focuses on establishing substantial equivalence to existing predicate devices rather than directly presenting explicit acceptance criteria and a detailed study proving device performance against those criteria in a typical clinical performance study format.

However, based on the information provided, we can infer some aspects relevant to your request, especially concerning the "Testing Summary" section.

Here's an analysis based on your questions, extracting what's available and noting what is not explicitly stated in the document:

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

The document does not provide a table of acceptance criteria with corresponding performance metrics (e.g., sensitivity, specificity, accuracy) for the Ziostation2 for specific clinical tasks. The submission focuses on demonstrating substantial equivalence to predicate devices for various image processing functionalities.

The "Testing Summary" states: "The ZIOSTATION2 software package successfully completed integration testing/verification testing prior to Beta validation. Regression testing was also performed on all functionality present on Ziostation. Software Beta testing/validation was successfully completed prior to final testing and release. In addition, potential hazards have been addressed by the Qi Imaging Risk Management process."

This statement confirms that internal testing was performed, but it lacks specific quantitative acceptance criteria and their corresponding results. The acceptance criteria for these internal tests would likely be related to software functionality, accuracy of calculations (e.g., volume, perfusion parameters), visualization correctness, data integrity, and system stability, demonstrating that the new features perform as intended and comparably to the predicate devices. However, these specific criteria and results are not detailed in this public document.

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

This information is not explicitly stated in the provided text. The document refers to "integration testing/verification testing" and "Software Beta testing/validation," which would have used some form of test data, but the sample size, provenance, or type of data (e.g., real patient data, synthetic data, specific types of scans) are not disclosed. Given the nature of a 510(k) for an image processing system, it's probable that DICOM datasets were used, but details are absent.

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 explicitly stated in the provided text. For a device like Ziostation2, which is an image processing application, ground truth for verification testing would likely involve validation against known phantom measurements or expert measurements performed on clinical images, but the details of such expert involvement are not provided.

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

This information is not explicitly stated in the provided text.

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 is not mentioned in the provided text. The document focuses on showing substantial equivalence of the software's processing and visualization capabilities to those of predicate devices, not on the impact of the device on human reader performance in a controlled study. The device is intended "to supplement generally accepted methods of interpreting radiological images," implying it's a tool, not an AI for diagnosis.

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

While the "Testing Summary" mentions "integration testing/verification testing," "regression testing," and "Software Beta testing/validation," these are described as internal software tests. It's highly probable these included "standalone" evaluations of the algorithms for their intended functions (e.g., accuracy of measurements, correct rendering of images, proper application of filters). However, specific metrics and results of such standalone performance (e.g., a standalone AUC for a diagnostic task) are not provided, as the device is not presented as an AI diagnostic algorithm, but rather an image processing and visualization tool.

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

The type of ground truth used for the internal testing (integration, verification, beta testing) is not explicitly stated. Given the functionalities of Ziostation2 (e.g., CT Coronary Analysis, CT Colon Analysis, CT Perfusion Analysis, MR Tractography), ground truth could involve:

• Known phantom data: for quantitative measurements (e.g., volumes, distances).
• Expert measurements/annotations: on clinical images for comparison with the software's automated or semi-automated tools.
• Previous gold standard software outputs: especially for regression testing against the predecessor Ziostation.
• Pathology or follow-up outcomes data: less likely for general image processing tools, but could be relevant for specific modules if they had a diagnostic claim, which is not the primary focus here.

8. The sample size for the training set

This information is not applicable or not explicitly stated. Ziostation2 is described as an "image processing application software," and its features are discussed in terms of "workflow enhancements" and equivalency to existing functionalities (e.g., data reconstruction, vessel labeling, measurement, display tools). There is no indication that this product is a machine learning or AI model trained on a specific dataset that would require a "training set" in the conventional sense of AI/ML development. Its functionality seems to be based on established algorithms in image processing.

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

This information is not applicable or not explicitly stated, for the same reasons as in point 8.

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