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As a part of the CXDI series radiography system, the CXDI Control Software when used with a compatible CXDI detector is intended to provide digital image capture, and display for conventional film/screen radiographic examinations. This device is intended to replace radiographic film/screen systems in all general purpose diagnostic procedures including specialist areas like intensive care, trauma, and pediatric work. This device is not intended for fluoroscopic, angiographic, or mammography applications.

The subject of this submission is a change to the CXDI Control Software to incorporate a scatter correction algorithm. By incorporating the scatter correction algorithm into the CXDI Control Software, the image contrast is enhanced and the images produced have similar detail contrast as images acquired with an anti-scatter grid.

The provided document is a 510(k) summary for Canon's "Scatter Correction for CXDI Series" device. It describes the device, its intended use, and a summary of testing conducted to demonstrate its substantial equivalence to predicate devices. However, the document does NOT contain details about specific acceptance criteria or a detailed study proving the device meets those criteria with quantitative values. It only provides a general statement that "Tests were performed on the proposed CXDI Control Software which demonstrates that the device is safe and effective, performs comparably to the predicate device(s), and is substantially equivalent to the predicate device(s)."

Therefore, I cannot provide the detailed information requested in your prompt based on this document.

Here's a breakdown of what can be extracted and what cannot:

Information that CANNOT be provided from this document:

1. A table of acceptance criteria and the reported device performance: The document does not list specific acceptance criteria (e.g., minimum DQE, contrast-to-noise ratio improvement) or corresponding measured performance values.
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 mentions "Additional evaluations were conducted with clinical images to demonstrate and evaluate the performance of the software feature" but does not specify the number of images, their origin, or whether they were retrospective or prospective.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: This information is not present.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: This information is not present.
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 describes "image comparisons involving flat panel display images taken without a grid" and "evaluations with clinical images" but does not detail a formal MRMC study or any results regarding human reader performance with/without the AI. The device is a "scatter correction algorithm" to enhance image quality, not a diagnostic AI that assists readers in interpretation.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: While the "image comparisons" and "evaluations with clinical images" suggest some form of standalone performance assessment of the scatter correction, no specific metrics or study design for standalone performance are provided.
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc): This information is not present.
8. The sample size for the training set: The document does not mention training data, as this is a software update incorporating an algorithm, not explicitly a machine learning model requiring a training set in the typical sense.
9. How the ground truth for the training set was established: Not applicable, as training data is not mentioned as part of this submission.

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