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To detect or image the distribution of radionuclides in the body or organ, using the following techniques:

• Multiplanar Reconstruction (MPR)
• Maximum/Minimum Intensity Projection (MIP) .
• . Image Contrast Manipulation
• Image Zoom Manipulation .
• Automatic registration with Mutual Information Technique .

Clarity Fusion is an option within the Clarity PET system so the end-user may differentiate between the two different applications within the same software program. This viewing of fused images is the ability to see the same datasets, as if they were being viewed independently, overlapped with each others. These fused images will allow a physician to look at two different datasets from different modalities. This is common medical practice that has now been computerized.

The provided text is a 510(k) summary for the Clarity Fusion device. It describes the device, its intended use, and its substantial equivalence to a predicate device. However, it does not contain the specific information required to answer your request about acceptance criteria, device performance, study details (sample sizes, ground truth establishment, expert qualifications, adjudication methods), or multi-reader multi-case studies.

This submission is a "Special 510(k)", which typically streamlines the review process for modifications to a legally marketed device if the changes do not affect the device's fundamental scientific technology or intended use. In such cases, extensive new clinical performance studies are often not required, and instead, the focus is on verification and validation that the modifications meet the original safety and effectiveness requirements and maintain substantial equivalence.

Therefore, many of the details you're asking for (like specific acceptance criteria performance data, sample sizes of test/training sets, expert qualifications, or MRMC studies) are not present in this type of 510(k) summary. These types of details would typically be found in a Traditional 510(k) submission where a new device is being introduced.

Here's what can be extracted and what cannot:

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

• Cannot provide. This information is not present in the provided text. The submission relies on demonstrating substantial equivalence to a predicate device (Clarity PET) and states that "software validation/verification and a Declaration of Conformity were submitted to demonstrate substantial equivalence." It does not provide specific acceptance criteria or performance metrics for the Clarity Fusion itself beyond its functionalities listed.

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

• Cannot provide. This information is not present.

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)

• Cannot provide. This information is not present. Ground truth methodology and expert involvement are not detailed.

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

• Cannot provide. Adjudication methods are not mentioned.

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

• No MRMC study was mentioned. The device described is an image processing system that fuses images from different modalities and provides tools like MPR, MIP, contrast manipulation, and automatic registration. It's not presented as an AI-enabled diagnostic aid that would typically be evaluated with MRMC studies comparing human readers with and without AI assistance. It "serv[es] primarily as an aid in image display" and is "not intended to replace the final interpretation of physicians."

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

• Not applicable/Cannot provide. The device is an image display and processing tool, not an AI algorithm performing a diagnostic task independently. Its function is to enhance diagnostic image quality and aid in image display for a physician, not to operate as a standalone diagnostic algorithm.

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

• Cannot provide. The method for establishing ground truth is not described in this document.

8. The sample size for the training set

• Cannot provide. This information is not present.

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

• Cannot provide. This information is not present.

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