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Vantage ExSPECT II is intended to provide quality assurance enhancements to nuclear medicine images acquired using the ADAC Gamma Camera Systems.

Vantage ExSPECT II is a software program, which will be marketed as an optional addition to ADAC Laboratories Gamma Camera products. This is a modification of the Vantage 2.0 ExSPECT software package, cleared in 510k K971878.

Vantage ExSPECT II is a computer program that provides a patient's functional information, which is further improved by using the anatomical information, obtained using the external radioactive scanning line sources with special collimation to minimize patient exposure, and the acquisition electronics and software, cleared in 510k K943596 for Vantage 1.0.

Vantage ExSPECT II is a modification of Vantage 2.0 ExSPECT and is designed to provide the user with additional quality assurance (QA) to improve the consistency and usability of the Vantage 2.0 ExSPECT product. The Post Acquisition QA tool provides the user with feedback regarding the quality of the acquired images in that it alerts the user as to the level of any banding or truncation in the data as well as the level of counts acquired in each data set. The other improvement is two user-selectable iterative reconstruction methods for reducing the noise level in the transmission image.

The Vantage ExSPECT II is a software program designed to provide quality assurance (QA) enhancements to nuclear medicine images. It is a modification of the Vantage 2.0 ExSPECT software package.

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

1. Table of Acceptance Criteria and Reported Device Performance:

Explanation of "Reported Device Performance": The provided text is a 510(k) summary, which outlines the device's features and intended function rather than providing quantitative performance metrics from a specific study. The "reported device performance" directly reflects the claims made for the device's capabilities in the summary. No numerical or statistical performance data (e.g., sensitivity, specificity, accuracy, or reduction in banding magnitude) are provided to objectively "prove" these claims in a quantitative sense. The acceptance appears to be qualitative based on the functionality descriptions.

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

• Sample Size: Not explicitly stated. The text only mentions "Images were processed using both the Post Acquisition QA tool and the transmission iterative reconstruction methods." It does not specify the number of images or cases used for testing.
• Data Provenance: Not explicitly stated. It is not mentioned if the data was retrospective or prospective, or the country of origin.

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

• Not specified. The document does not mention the use of experts or how ground truth was established for the testing.

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

• Not specified. There is no mention of 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:

• No, a multi-reader multi-case (MRMC) comparative effectiveness study is not mentioned. The device focuses on quality assurance enhancements for image acquisition and reconstruction, not on assisting human readers in interpretation.

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

• Yes, the testing described appears to be a standalone evaluation of the algorithm's functionality. The statement "Images were processed using both the Post Acquisition QA tool and the transmission iterative reconstruction methods" implies an assessment of the algorithm's output without direct human interaction as part of the primary test of its claimed QA and noise reduction features.

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

• Not explicitly stated. Given the nature of the device (quality assurance and noise reduction), the "ground truth" would likely be related to objective measures of image quality, such as quantifiable levels of banding, truncation, or noise reduction as assessed against a reference standard or known simulated data. However, the document does not elaborate on how these were defined or measured.

8. The sample size for the training set:

• Not applicable/Not specified. This is a 510(k) summary for a software modification, not a machine learning or AI-based device that would typically involve a distinct "training set" in the context of supervised learning. The modifications are described as "improvements" to existing algorithms (Post Acquisition QA tool and iterative reconstruction methods), suggesting development or refinement of rule-based or model-based algorithms rather than data-driven AI training.

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

• Not applicable/Not specified, as it is not an AI/ML device with a training set.

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