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This radiographic system is intended for use by a qualified/trained doctor or technician on both adult and pediatric subjects for taking diagnostic radiographic exposures of the skull, spinal column, chest, abdomen, extremities, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position.

This diagnostic x-ray system consists of a tubehead/collimator assembly mounted on a ceiling suspension along with a generator, generator control, and an elevating x-ray table. Power ratings for the available generators are in the rage of 32 kw to 80 kW. Exposure voltage range varies from 40 - 125 KV or 40 - 150 kV with current of 300 -100 mA. Exposure time is 1 ms - 10 s.

This 510(k) submission describes a diagnostic X-ray system and does not involve Artificial Intelligence (AI) or machine learning. Therefore, many of the requested criteria, such as those related to AI performance, sample sizes for training/test sets in an AI context, expert ground truth establishment for AI, MRMC studies for AI, or standalone AI performance, are not applicable to this document.

The acceptance criteria and "device performance" described in this document relate to the substantial equivalence of the new device (RADVISION ET) to a predicate device (RADVISION E and RADVISION EU) based on safety and effectiveness.

Here's the information that can be extracted based on the provided text:

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

The acceptance criteria here are implicitly met if the new device is deemed "substantially equivalent" to the predicate device. The performance is compared based on functional and safety characteristics.

The "study that proves the device meets the acceptance criteria" is described as:

• "The results of bench and test laboratory indicates that the new device is as safe and effective as the predicate devices."
• "After analyzing bench and external laboratory testing to applicable standards, it is the conclusion of Almana Medical Imaging that the RADVISION ET Diagnostic X-Ray Systems are as safe and effective as the predicate device, have few technological differences, and has no new indications for use, thus rendering them substantially equivalent to the predicate devices."

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 submission references "bench and test laboratory" studies without specifying sample sizes for physical testing.

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 is not applicable as this is a traditional medical device submission, not an AI/ML submission requiring expert ground truth for image interpretation.

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

This is not applicable as this is a traditional medical device submission.

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

This is not applicable as this is a traditional medical device submission, not involving AI assistance.

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

This is not applicable as this is a traditional medical device submission, not involving an algorithm.

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

The "ground truth" in this context refers to the safety and effectiveness of the predicate device, established through its existing legal marketing and compliance with standards. The new device is compared to this established benchmark through technical specifications and bench/laboratory testing. There's no specific "ground truth" for diagnostic accuracy in the way it's used for AI algorithms.

8. The sample size for the training set

This is not applicable as this is a traditional medical device submission, not an AI/ML submission.

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

This is not applicable as this is a traditional medical device submission, not an AI/ML submission.

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