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Intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography.

AcuityDR G4 combines components into a complete digital x-ray system upgrade kit, including software and digital radiography panels. The customer selects one of the following digital x-ray receptor panels: AcuityDR 1013 G4, AcuityDR 1717 G4. The solid state imaging receptors can be used in wireless or tethered configurations for use remain unchanged: Intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography. So the only difference between this submission and the predicate submission is the available selection of (previously cleared) digital panels. Each system consists of the following items: Customer supplies: Diagnostic x-ray generator (HF) Class I Code IZO. + Tubehead: Class I Code ITV + Tube Mount: Class I Code IYB + Attached Collimator, Manual (IZX) Class II 510(k) Exempt We supply: Digital X-Ray Receptor Panel 892.1680 Class II Code MQB. Digital X-ray Software 892.2050 Class II Code LLZ. The software offered for sale with this system has received previous 510(k) clearance in K210919. We are supplying rebranded versions of the reference devices coupled with the Acculvue software cleared in our predicate K210919.

I am sorry, but the provided text does not contain the detailed information required to describe the acceptance criteria and the study that proves the device meets those criteria. Specifically, the document is a 510(k) summary for a medical device (AcuityDR G4) and focuses on demonstrating substantial equivalence to a predicate device.

The document discusses performance characteristics of the digital panels (DQE, MTF, resolution) and indicates that clinical image evaluation was performed on the proposed panels by a Board Certified Radiologist who found the images to be of excellent quality. However, it does not provide explicit acceptance criteria, a structured study design with specific metrics, or details about patient data, expert qualifications, or ground truth establishment in the way requested.

Here's a breakdown of why I cannot fulfill your request based on the provided text:

1. Table of acceptance criteria and reported device performance: While performance characteristics like DQE and MTF values are reported, these are not presented as acceptance criteria in the context of a specific study to prove the device meets them. There's no clear threshold or target defined that the device needs to achieve to be considered "acceptable" in a formal study sense.

2. Sample size used for the test set and data provenance: The document mentions "clinical image evaluation" was performed but does not specify the sample size of images or patients used. It also doesn't provide details on the data's origin (country, retrospective/prospective).

3. Number of experts used to establish the ground truth and qualifications: It states "a Board Certified Radiologist" performed clinical image evaluation, implying one expert. However, their specific qualifications (e.g., years of experience, specific sub-specialty) are not provided beyond being "Board Certified." The method for establishing "ground truth" (if any beyond the radiologist's assessment) is not described.

4. Adjudication method: Not mentioned.

5. Multi-reader multi-case (MRMC) comparative effectiveness study: Not mentioned and typically not required for substantial equivalence claims for this type of device (an X-ray system). The evaluation described is a qualitative assessment of image quality by a radiologist. There is no mention of comparing human readers with and without AI assistance.

6. Standalone (algorithm only) performance: Not applicable here as the device is an X-ray system, not an AI algorithm performing diagnostic tasks independently. The software provided is for controlling the X-ray generator and image acquisition, not for diagnostic interpretation.

7. Type of ground truth used: The text indicates a "Board Certified Radiologist" evaluated image quality. This suggests expert consensus (though with only one expert mentioned) on image quality, but not pathology or outcomes data.

8. Sample size for the training set: Not applicable and not mentioned, as this device itself is not a machine learning algorithm requiring a training set for diagnostic classification.

9. How the ground truth for the training set was established: Not applicable.

The document mainly focuses on comparing the AcuityDR G4 system's technological characteristics (like detector DQE, MTF, and resolution) and indications for use to its predicate device (AcuityDRe, K210919) and other reference devices, concluding that the new device is substantially equivalent. The image quality evaluation by the radiologist appears to be a supportive qualitative assessment rather than a structured quantitative study with predefined acceptance criteria.

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