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The Solas OR is a cabinet X-ray system used to provide digital X-ray images of surgical and core biopsy specimens from various anatomical regions in order to allow rapid verification that the correct tissue has been excised during the biopsy procedure.

Doing the verification in the same room as the procedure or nearby improves workflow, the time the patient needs to be under examination.

The Solas OR cabinet x-ray system is a self-contained, direct-detection digital imaging system for imaging small to medium surgical and biopsy specimens. The system is comprised of the x-ray cabinet, optional cart and the PC with DICOM compliant software which provides the user interface, the means to enter patient details (either directly or from a DICOM Modality Worklist, if available) and the means to acquire, review and save or transmit DICOM images to the Picture Archiving and Communication System (PACS). The cabinet incorporates shielding and interlock circuits to meet regulatory requirements. The cabinet cart is mounted on casters to allow for easy transportation.

Specimen radiography units are utilized to confirm removal of the intended tissue, lesion, or site marker in surgical and core biopsy specimens from various anatomical regions. By generating a high-resolution x-ray image of the specimen, the presence of a lesion, marker or calcification in the extracted sample can be confirmed by the user reviewing the digital image.

The provided text does not contain detailed acceptance criteria or a study that proves the device meets specific acceptance criteria in the format requested. The document is a 510(k) Summary for the Solas OR, focusing on its substantial equivalence to a predicate device.

However, I can extract the information that is present and indicate what is missing:

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

The document mentions compliance with various standards and image quality tests. Here's a table based on the information provided, noting that specific numerical acceptance criteria and direct performance metrics against those criteria are not detailed:

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 provided in the document. The document mentions "Non-Clinical Testing included image quality tests with accredited phantom test objects and High Contrast resolution targets" and that "the device performance has been benchmarked against the predicate in a clinical setting," but no details on sample size or data provenance for these tests are given.

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 provided in the document. The text indicates "benchmarked against the predicate in a clinical setting," but does not mention expert involvement or their qualifications.

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

This information is not provided in the document.

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 the device as a "cabinet X-ray system used to provide digital X-ray images." It is a hardware device for image acquisition, not an AI-assisted diagnostic tool. Therefore, an MRMC comparative effectiveness study regarding "human readers improve with AI vs without AI assistance" is not applicable and not mentioned. The benchmarking mentioned is against a predicate device, not in the context of human reader performance with or without AI.

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

The device is an imaging system, not an algorithm, so this question is not directly applicable in the context of "algorithm only." The document states, "Non-Clinical Testing included image quality tests with accredited phantom test objects and High Contrast resolution targets," which implies standalone performance evaluation of the imaging system's technical specifications.

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

This information is not explicitly stated for the "benchmarking against the predicate in a clinical setting." For the non-clinical tests, "accredited phantom test objects and High Contrast resolution targets" were used, which serve as a form of ground truth for image quality measurements.

8. The sample size for the training set

This device is an X-ray imaging system, not an AI/machine learning model that typically requires a "training set." Therefore, the concept of a training set sample size is not applicable in the context of the information provided for this device.

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

As the device is an X-ray imaging system and not an AI/machine learning model, the concept of a "training set" and its associated ground truth establishment is not applicable here.

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