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SpeedUp is an imaging technique to increase scan speed reducing the acquisition time. The sparsity which is implicit in MR images is exploited to significantly undersample the k-space, resulting in incoherent artifacts (noise like). Time is decreased reducing the number of acquired k-space lines (i.e. undersampling) and the final image can be reconstructed accurately with an appropriate non-linear reconstruction method.

SpeedUp is a software option intended for use on G-scan and O-scan Esaote MRI svstems. SpeedUp is an imaging technique to increase scan speed reducing the acquisition time. The sparsity which is implicit in MR images is exploited to significantly undersample the kspace, resulting in incoherent artifacts (noise like). Time is decreased reducing the number of acquired k-space lines (i.e. undersampling) and the final image can be reconstructed accurately with an appropriate non-linear reconstruction method.

The provided text is a 510(k) summary for a medical device called "SpeedUp," which is a software option for MRI systems designed to increase scan speed. It states that the device is "substantially equivalent" to predicate devices, but it does not contain specific acceptance criteria or details of a study with performance metrics in a format that would allow for the direct creation of the requested table and detailed study information.

Here's a breakdown of what is and is not available in the provided text, and how it relates to your request:

1. Table of acceptance criteria and reported device performance:

• Not Available. The document states that "non-clinical verification and validation testing" and "clinical images... demonstrated that the systems met performance requirements and are as safe and effective as the predicate devices." However, it does not provide any specific quantitative (e.g., sensitivity, specificity, accuracy, dice score) or qualitative performance metrics, nor does it define what those "performance requirements" actually were.

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

• Not Available. The document mentions "clinical images" were used, but does not specify the number of cases or patients included in any test set, nor does it provide information about the country of origin or 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:

• Not Available. The document does not describe how ground truth was established, nor does it mention the number or qualifications of any experts involved in such a process.

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

• Not Available. There is no mention of any 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:

• Not Available. The document does not mention an MRMC study or any AI component. SpeedUp is described as an "imaging technique" and "software option" for MRI to reduce acquisition time through k-space undersampling and non-linear reconstruction, which is a signal processing technique, not necessarily an AI algorithm in the contemporary sense. It focuses on the image acquisition and reconstruction quality compared to standard methods, not on human interpretation improvement with or without AI assistance.

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

• Partially Available (Implied Standalone). SpeedUp is described as a "software option" that "reconstructs" images. The statement "Clinical images... demonstrated that the systems met performance requirements" implies a standalone assessment of the generated images, likely for image quality and diagnostic utility, but specific standalone performance metrics (e.g., SNR, contrast, resolution) are not provided. The function itself is image reconstruction, which is inherently "algorithm only" in its output.

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

• Not Available. The document refers to "clinical images" and meeting "performance requirements" but does not specify the ground truth used to evaluate these images or the output of the SpeedUp software.

8. The sample size for the training set:

• Not Applicable/Not Available. Given that SpeedUp is described as exploiting "sparsity" and using a "non-linear reconstruction method" rather than a deep learning AI model requiring a training set in the modern sense, a "training set" as typically understood for AI algorithms may not be relevant. Even if there were parameters optimized, the document does not mention any specific training data or its size.

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

• Not Applicable/Not Available. (See point 8).

In summary, the provided 510(k) summary for SpeedUp attests to substantial equivalence based on meeting performance requirements through non-clinical and clinical testing, but it does not disclose the specific criteria, study designs, sample sizes, expert involvement, or quantitative results that would fulfill your request for detailed acceptance criteria and study proving device performance. The document is high-level and focused on regulatory compliance through substantial equivalence, not detailed technical performance disclosure.

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